# Factor House

> Factor House builds Kpow for Apache Kafka, Flex for Apache Flink, and Iglu for Apache Iceberg, unified under Factor Platform for real-time data infrastructure. Factor House also hosts quarterly user group sessions across the Americas, EMEA, and APAC featuring data streaming experts, with lightning talk recordings and transcripts published at /talks/.

## Guides

- [Product demo videos | Factor House](https://factorhouse.io/resources/demos/): Short, focused video demos of Kpow, Flex, Iglu, and Factor Platform: real features, walked through end to end, on real Kafka, Flink, and Iceberg workloads.
- [Apache Kafka access policies & SSO: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-access-policies-and-sso/): Chad Harris covers access policies in Kpow: which permissions the role you are logged in as holds, and how SSO with OAuth, SAML or Entra ID drives that from your existing identity groups.
- [Apache Kafka audit logging: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-audit-logging/): Chad Harris walks through audit logging in Kpow: every action recorded on a Kafka topic you can inspect, traced back to the query and the data it exposed, and forwarded to Slack, Teams or a SIEM.
- [Kafka broker monitoring & config: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-broker-monitoring-and-configuration/): Chad Harris walks through brokers in Kpow: disk, throughput and replication health at a glance, filtering and exporting broker and topic tables, editing config under RBAC, and KRaft controllers.
- [Kpow CLI, terminal UI, and agentic skills | Factor House](https://factorhouse.io/resources/demos/kpow-cli-terminal-ui-and-agentic-skills/): Chad Harris previews Kpow's new CLI and terminal UI for Apache Kafka, plus the agentic skills that let an AI assistant query, diagnose, and operate Kafka through Kpow under your own SSO and RBAC.
- [Apache Kafka cluster health monitoring: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-cluster-health-monitoring/): Chad Harris digs into Signals in Kpow: a cluster health score tracked over time, a real example of partition data skew from a poor partitioning key, and the cost-saving cleanup signals beside it.
- [Kafka Connect monitoring and tasks: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-connect-monitoring-and-task-management/): Chad Harris walks through Kafka Connect in Kpow: connector and task state at a glance, historical health charts, deploying connectors from the UI, and bulk-restarting a subset of tasks.
- [Kafka consumer group monitoring & lag: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-consumer-group-monitoring-and-lag/): Chad Harris walks through consumer groups in Kpow: stability over time, lag broken down to the partition, resetting or skipping offsets on a running group, and topology that traces lag to a host.
- [Apache Kafka data inspection & search: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-data-inspection-and-search/): Chad Harris walks through data inspection in Kpow: filtering topic data with kJQ, streaming searches with no scan limit, narrowing a scan by partition or key, and cloning a result set elsewhere.
- [Kafka data masking & PII protection: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-data-masking-and-pii-protection/): Chad Harris walks through data masking in Kpow: last-four, full and email-domain rules applied during data inspection, and the policy playground for testing show-first and hashing before rollout.
- [Apache Kafka RBAC & multi-tenancy: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-rbac-and-tenancy/): Chad Harris walks through tenancy and role-based access control in Kpow: scoping a virtual cluster view down to a single team's topics and metrics, governed by SSO and fine-grained RBAC.
- [Apache Kafka schema registry management: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-schema-registry-management/): Chad Harris walks through schema registry management in Kpow, against Confluent, Karapace, MSK and others: viewing and editing schemas, new revisions, compatibility settings, subjects and deletes.
- [Kpow Signals: automated operational insights | Factor House](https://factorhouse.io/resources/demos/kpow-signals-automated-operational-insights/): Chad Harris introduces Signals, a new Kpow feature that continuously monitors your Kafka clusters for misconfigurations and early warning signs, so you catch problems before they break something.
- [Kafka just-in-time permissions: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-temporary-access-and-just-in-time-permissions/): Chad Harris walks through time-boxed, API-driven permissions in Kpow: scoped access granted to a role for a duration that expires by itself, wired to a service desk like Jira for approval.
- [Apache Kafka tenant configuration: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-tenant-configuration/): Chad Harris covers tenant configuration in Kpow, building on the RBAC and multi-tenancy overview earlier in the series: admin against team-member access, a team scoped to one tenant, all from YAML.
- [Apache Kafka topic management: Kpow demo | Factor House](https://factorhouse.io/resources/demos/kpow-topic-management/): Chad Harris walks through topic management in Kpow: truncating data by offset, electing leaders, increasing partitions, break-glass config changes like retention, and topic-level ACLs, under RBAC.
- [Apache Flink: the complete guide | Factor House](https://factorhouse.io/resources/flink/): Apache Flink is a distributed stream processing framework for stateful computation over unbounded and bounded data. This hub indexes what we have written about running it in production.
- [How Airbus uses Apache Flink in production | Factor House](https://factorhouse.io/resources/flink/use-cases/airbus/): How Airbus's AirSense unit processes more than 2 billion aircraft-position events a day with Apache Flink, fusing multi-provider ADS-B feeds into real-time global flight tracking.
- [How Alibaba uses Apache Flink in production | Factor House](https://factorhouse.io/resources/flink/use-cases/alibaba/): How Alibaba built Blink, its internal Apache Flink fork, to handle 4 billion records a second during Double 11, then merged its isolation and checkpointing innovations into Apache Flink 1.9 and 1.10.
- [How Booking.com uses Apache Flink in production | Factor House](https://factorhouse.io/resources/flink/use-cases/booking-com/): How Booking.com's Security Platform Services team runs Apache Flink as the engine behind an internal security-as-a-service platform, scaling to more than 250 jobs under Ververica Platform.
- [Flink use cases | Apache Flink use cases by Factor House](https://factorhouse.io/resources/flink/use-cases/): Companies running Apache Flink in production, the architectures behind their deployments, and what to read next. Indexed as we publish new use-case research.
- [How Lyft uses Apache Flink in production | Factor House](https://factorhouse.io/resources/flink/use-cases/lyft/): How Lyft's Streaming Compute and Marketplace teams run Apache Flink for pricing features, fraud detection, and event persistence, including the 2026 migration off a homegrown Kubernetes operator.
- [How Netflix uses Apache Flink in production | Factor House](https://factorhouse.io/resources/flink/use-cases/netflix/): How Netflix runs more than 30,000 Apache Flink jobs across Keystone, its Data Mesh SQL Processor, and a real-time distributed graph, sourced from its own engineering blog and conference talks.
- [How Pinterest uses Apache Flink in production | Factor House](https://factorhouse.io/resources/flink/use-cases/pinterest/): How Pinterest runs 130+ Apache Flink jobs across eight multitenant YARN clusters for image-similarity detection, experiment analytics, ad-budget enforcement, and CDC ingestion into Iceberg.
- [How Uber uses Apache Flink in production | Factor House](https://factorhouse.io/resources/flink/use-cases/uber/): How Uber runs more than 2,000 Apache Flink SQL jobs processing over 4 trillion messages a day, from its early AthenaX SQL platform through to IngestionNext, its Flink-to-Hudi streaming data lake.
- [Apache Iceberg: the complete guide | Factor House](https://factorhouse.io/resources/iceberg/): Apache Iceberg is an open table format bringing ACID transactions, schema evolution, and time travel to huge analytic tables in S3. This hub indexes what we've written about running it in production.
- [How Airbnb uses Apache Iceberg in production | Factor House](https://factorhouse.io/resources/iceberg/use-cases/airbnb/): How Airbnb moved its Kafka-fed warehouse ingestion pipeline off Tez and Hive onto Spark 3 and Apache Iceberg, consolidating tables via partition-spec evolution and cutting compute by over 50%.
- [How Apple uses Apache Iceberg in production | Factor House](https://factorhouse.io/resources/iceberg/use-cases/apple/): How Apple built GDPR- and DMA-compliant row-level deletes on Iceberg tables holding tens of petabytes of data, sourced from a peer-reviewed paper by eight Apple engineers and named conference talks.
- [Iceberg use cases | Apache Iceberg use cases by Factor House](https://factorhouse.io/resources/iceberg/use-cases/): Companies running Apache Iceberg in production, the architectures behind their deployments, and what to read next. Indexed as we publish new use-case research.
- [How LinkedIn uses Apache Iceberg in production | Factor House](https://factorhouse.io/resources/iceberg/use-cases/linkedin/): How LinkedIn's OpenHouse control plane governs 300,000+ Apache Iceberg tables holding over an exabyte of data, with automated compaction and cross-region replication.
- [How Netflix uses Apache Iceberg in production | Factor House](https://factorhouse.io/resources/iceberg/use-cases/netflix/): How Netflix moved its S3 data warehouse off Hive onto an Iceberg-only architecture, and how Maestro, Psyberg, and its Cassandra engine lean on Iceberg's own snapshot and partition metadata.
- [How Pinterest uses Apache Iceberg in production | Factor House](https://factorhouse.io/resources/iceberg/use-cases/pinterest/): How Pinterest rebuilt CDC ingestion and ML feature backfills on Apache Iceberg, cutting pipeline latency from 24+ hours to minutes and backfill time from 140 days to 26.
- [What is a Kafka broker? Config and troubleshooting | Factor House](https://factorhouse.io/resources/kafka/architecture/kafka-broker/): A Kafka broker stores partition logs and serves client requests. Broker configuration, bootstrap timeouts, crash loops and the JMX metrics to watch.
- [Kafka KRaft | Kafka architecture by Factor House](https://factorhouse.io/resources/kafka/architecture/kraft/): KRaft replaces ZooKeeper with a Raft quorum built into Kafka. The migration path and deadlines, controller sizing, the real scale limits, and day-2 operations for KRaft clusters.
- [Difference between Kafka and RabbitMQ | Factor House](https://factorhouse.io/resources/kafka/brokers/difference-between-kafka-and-rabbitmq/): The difference between Kafka and RabbitMQ is the data model: a replayable log against a delete-on-acknowledge queue. Storage, routing, scaling and when to pick which.
- [How does RabbitMQ work | RabbitMQ queue types by Factor House](https://factorhouse.io/resources/kafka/brokers/how-does-rabbitmq-work/): How RabbitMQ works, mapped to Kafka concepts: exchanges and bindings, per-message acknowledgement, competing consumers, quorum queues and dead lettering.
- [RabbitMQ | Kafka vs RabbitMQ by Factor House](https://factorhouse.io/resources/kafka/brokers/): How Kafka compares with RabbitMQ and other message brokers: retention models, routing, use cases and operational trade-offs, from the operator's seat.
- [Managed vs unmanaged database | Kubernetes by Factor House](https://factorhouse.io/resources/kafka/brokers/managed-vs-unmanaged-database/): Managed vs unmanaged databases for teams running Kafka: operational overhead, SLAs, cost architecture, connector and CDC integration, control and security.
- [What is RabbitMQ | RabbitMQ architecture by Factor House](https://factorhouse.io/resources/kafka/brokers/what-is-rabbitmq/): RabbitMQ explained for Kafka operators: the AMQP queue model, broker-side routing, push delivery, streams, and where it fits beside a Kafka deployment.
- [Redpanda | AWS Kinesis by Factor House](https://factorhouse.io/resources/kafka/cloud-brokers/): Redpanda and the Kafka-compatible brokers, evaluated from the operator's seat: compatibility proof, operational claims, TCO against tiered-storage Kafka, and benchmarks.
- [What is Redpanda | AWS Kinesis by Factor House](https://factorhouse.io/resources/kafka/cloud-brokers/what-is-redpanda/): Redpanda reimplements the Kafka wire protocol in a single C++ binary. The architecture, the drop-in compatibility boundaries, and how to evaluate it against tiered-storage Kafka.
- [AKHQ vs CMAK | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/akhq-vs-cmak/): AKHQ ships releases. CMAK last shipped in April 2022 and still needs ZooKeeper. What each one costs to carry, where each runs out, and which to pick.
- [AKHQ vs Confluent Control Center | Kafka UIs by Factor House](https://factorhouse.io/resources/kafka/comparisons/akhq-vs-confluent-control-center/): AKHQ is free and runs against any Kafka. Confluent Control Center is not sold separately and needs Confluent Platform in the brokers. What each costs.
- [AKHQ vs Kadeck | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/akhq-vs-kadeck/): AKHQ is free under Apache 2.0 with no paid tier. Kadeck sells governance at ten seats. What each costs, where each runs out, and which fits which team.
- [AKHQ vs Kafbat UI | Free Kafka UI tools compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/akhq-vs-kafbat-ui/): AKHQ and Kafbat UI are both free and Apache 2.0, so the split is maintenance and governance. What each masks, what each audits, where each runs out.
- [AKHQ vs Kafdrop | Kafka UI tools compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/akhq-vs-kafdrop/): AKHQ and Kafdrop are both free Kafka UIs under Apache 2.0. What each keeps up with, what free actually costs a team, and which one fits which job.
- [AKHQ vs Lenses | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/akhq-vs-lenses/): AKHQ is free with no vendor behind it. Lenses prices by tier and needs a Postgres per component. What each costs to run, where each runs out, and which fits.
- [AKHQ vs Offset Explorer | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/akhq-vs-offset-explorer/): AKHQ is free and deployed once for a whole team. Offset Explorer is licensed per named user and installed per machine. What each costs and where each runs out.
- [AKHQ vs Redpanda Console | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/akhq-vs-redpanda-console/): AKHQ holds nothing back from its Apache 2.0 release. Redpanda Console gives away the viewer and charges for the login. What each costs, and which fits.
- [CMAK vs Conduktor | Kafka management tools by Factor House](https://factorhouse.io/resources/kafka/comparisons/cmak-vs-conduktor/): CMAK is free and stopped shipping in 2022. Conduktor bills per seat and needs PostgreSQL. What each costs, where each runs out, and which one fits your team.
- [CMAK vs Confluent Control Center | Kafka UIs by Factor House](https://factorhouse.io/resources/kafka/comparisons/cmak-vs-confluent-control-center/): CMAK needs ZooKeeper. Confluent Control Center needs Confluent Platform. What each one costs to run, where each runs out, and which fits which team.
- [CMAK vs Kadeck | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/cmak-vs-kadeck/): CMAK is free and needs a ZooKeeper. Kadeck is commercial and bills per user. What each one costs, which clusters each can reach, and which fits which team.
- [CMAK vs Kafbat UI | Free Kafka UI tools compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/cmak-vs-kafbat-ui/): CMAK needs ZooKeeper, so it cannot reach a Kafka 4.x cluster. Kafbat UI connects like any client. What each free tool really costs, and where each runs out.
- [CMAK vs Kafdrop | Kafka UI tools compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/cmak-vs-kafdrop/): CMAK is an admin console that needs ZooKeeper. Kafdrop is a message viewer that does not. What each is for, what free costs, and what a KRaft cutover breaks.
- [CMAK vs Lenses | Kafka management tools by Factor House](https://factorhouse.io/resources/kafka/comparisons/cmak-vs-lenses/): CMAK is free and stopped shipping in 2022. Lenses is a control plane with a database per cluster. What each costs, where each runs out, and which fits.
- [CMAK vs Offset Explorer | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/cmak-vs-offset-explorer/): CMAK is a server-side console with a hard ZooKeeper dependency. Offset Explorer is a desktop client licensed per user. What each costs and where each runs out.
- [CMAK vs Redpanda Console | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/cmak-vs-redpanda-console/): CMAK cannot reach a KRaft cluster. Redpanda Console licenses the partition reassignment CMAK did for nothing. What each covers free, and which fits your team.
- [Conduktor vs Confluent Control Center | Kafka UIs by Factor House](https://factorhouse.io/resources/kafka/comparisons/conduktor-vs-confluent-control-center/): Conduktor bills per seat and runs against any Kafka distribution. Confluent Control Center is bundled with Confluent Platform and reaches nothing else.
- [Conduktor vs Kadeck | Kafka UI pricing by Factor House](https://factorhouse.io/resources/kafka/comparisons/conduktor-vs-kadeck/): Conduktor sells policy enforcement, Kadeck sells data exploration. What each charges per person, where governance sits on each ladder, and which fits a team.
- [Conduktor vs Kafbat UI | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/conduktor-vs-kafbat-ui/): Conduktor Console Community caps at 50 users and 3 clusters. Kafbat UI caps at nothing. What each free tier reaches, what the next rung costs, which to pick.
- [Conduktor vs Kafdrop | Kafka UI tools compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/conduktor-vs-kafdrop/): Kafdrop is free with no authentication at all. Conduktor Team Edition is 1,200 US dollars per seat per year. What each free tier reaches, and which fits yours.
- [Conduktor vs Lenses | Kafka governance pricing by Factor House](https://factorhouse.io/resources/kafka/comparisons/conduktor-vs-lenses/): Conduktor bills per seat and puts a proxy on the wire. Lenses tiers by user count and connects as a client. What each costs, and which fits a team.
- [Conduktor vs Offset Explorer | Kafka UI pricing by Factor House](https://factorhouse.io/resources/kafka/comparisons/conduktor-vs-offset-explorer/): Conduktor is a per-seat platform with a database behind it. Offset Explorer is a per-user desktop application. What each costs, and which fits which team.
- [Conduktor vs Redpanda Console | Kafka UI by Factor House](https://factorhouse.io/resources/kafka/comparisons/conduktor-vs-redpanda-console/): Conduktor bills per seat. Redpanda Console is free, with RBAC behind a broker vendor licence. What each costs, where each runs out, and which fits your team.
- [Confluent Control Center vs Kadeck | Kafka UIs by Factor House](https://factorhouse.io/resources/kafka/comparisons/confluent-control-center-vs-kadeck/): Confluent Control Center ships with Confluent Platform and watches nothing else. Kadeck bills per seat. What each costs, where each runs out, and which fits.
- [Confluent Control Center vs Kafbat UI | Kafka UIs by Factor House](https://factorhouse.io/resources/kafka/comparisons/confluent-control-center-vs-kafbat-ui/): Confluent Control Center only ever watches Confluent Platform. Kafbat UI is free and reaches any cluster. What each costs, where each runs out, and which fits.
- [Confluent Control Center vs Kafdrop | Kafka UIs by Factor House](https://factorhouse.io/resources/kafka/comparisons/confluent-control-center-vs-kafdrop/): Confluent Control Center needs a broker-side reporter and a platform licence. Kafdrop runs on any Kafka and has no login. What each one obliges you to own.
- [Confluent Control Center vs Lenses | Kafka tools by Factor House](https://factorhouse.io/resources/kafka/comparisons/confluent-control-center-vs-lenses/): Confluent Control Center needs a broker-side reporter and reaches only Confluent Platform. Lenses connects to any Kafka and bills per user. What each costs.
- [Confluent Control Center vs Offset Explorer | Factor House](https://factorhouse.io/resources/kafka/comparisons/confluent-control-center-vs-offset-explorer/): Control Center ships with Confluent Platform and reaches nothing else. Offset Explorer is a per-user desktop app. What each costs, and which one fits you.
- [Control Center vs Redpanda Console | Kafka UI by Factor House](https://factorhouse.io/resources/kafka/comparisons/confluent-control-center-vs-redpanda-console/): Control Center only runs on Confluent Platform. Redpanda Console is free until governance. What each costs, where each runs out, and which to pick.
- [Kafka UI comparison | Kafka console comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/): Every major Kafka UI and console compared head to head: Kpow against each, and each against the others. Pricing, plan limits and where each one runs out.
- [Kadeck vs Kafbat UI | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/kadeck-vs-kafbat-ui/): Kadeck bills per user and puts governance behind Enterprise. Kafbat UI is Apache-2.0 with RBAC, masking, and audit built in. What each costs, and which fits.
- [Kadeck vs Kafdrop | Kafka UI tools compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/kadeck-vs-kafdrop/): Kafdrop is free with nobody to escalate to. Kadeck bills per user and its governed tier starts at ten seats. What each costs, and which fits which team.
- [Kadeck vs Lenses | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/kadeck-vs-lenses/): Lenses meters users and capability tiers. Kadeck meters seats. What governance costs on each side, what each one makes you deploy, and which fits your team.
- [Kadeck vs Offset Explorer | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/kadeck-vs-offset-explorer/): Kadeck bills per user and puts governance behind a ten-user floor. Offset Explorer is a per-user purchase nobody logs in to. What each costs, and which fits.
- [Kadeck vs Redpanda Console | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/kadeck-vs-redpanda-console/): Kadeck bills per user and holds governance at Enterprise. Redpanda Console is free and gates the login. What each costs, where each runs out, and which fits.
- [Kafbat UI vs Kafdrop | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/kafbat-ui-vs-kafdrop/): Kafdrop is a fast viewer with no authentication and no KRaft support. Kafbat UI adds RBAC, masking, and an audit log. What each costs, and which to pick.
- [Kafbat UI vs Lenses | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/kafbat-ui-vs-lenses/): Kafbat UI is free and stateless, with no database. Lenses meters by user count from 4,000 US dollars a year. What each costs, and where each runs out.
- [Kafbat UI vs Offset Explorer | Kafka UI tools by Factor House](https://factorhouse.io/resources/kafka/comparisons/kafbat-ui-vs-offset-explorer/): Kafbat UI is free and deployed once for a team. Offset Explorer is licensed per named user. What each costs, where each runs out, and which fits which team.
- [Kafbat UI vs Redpanda Console | Kafka UI compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/kafbat-ui-vs-redpanda-console/): Kafbat UI is Apache 2.0 with governance included. Redpanda Console gates access control behind a Redpanda licence. What each costs, where each runs out.
- [Kafdrop vs Lenses.io | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/kafdrop-vs-lenses/): Kafdrop is a free viewer with no login in front of it. Lenses bills by user count and needs PostgreSQL. What each costs, where each runs out, who picks which.
- [Kafdrop vs Offset Explorer | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/kafdrop-vs-offset-explorer/): Kafdrop is free with no login in front of it. Offset Explorer is a purchase per named user. What each costs, where each runs out, and which fits which team.
- [Kafdrop vs Redpanda Console | Kafka UIs compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/kafdrop-vs-redpanda-console/): Kafdrop is Apache 2.0 with no paid tier. Redpanda Console is source-available, with SSO and RBAC licensed. What each costs, and which fits which team.
- [Kpow vs AKHQ | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/kpow-vs-akhq/): AKHQ is free and costs operator time. Kpow is licensed per cluster with a published price. How the two differ on masking, audit, and support.
- [Kpow vs CMAK | Kafka cluster management by Factor House](https://factorhouse.io/resources/kafka/comparisons/kpow-vs-cmak/): CMAK connects through ZooKeeper and stops at Kafka 4.0. Kpow bills per cluster and talks to brokers. What each one costs, and which one fits your team.
- [Kpow vs Confluent Control Center | Kafka UI by Factor House](https://factorhouse.io/resources/kafka/comparisons/kpow-vs-confluent-control-center/): Control Center needs a Confluent JAR in the broker classpath. Kpow runs against any distribution, per cluster, at a published price. Which fits which team.
- [Kpow vs Kadeck | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/kpow-vs-kadeck/): Kadeck bills per user, Kpow bills per cluster. What each one costs, what each needs in order to start, and which one fits your team.
- [Kpow vs Kafbat UI | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/kpow-vs-kafbat-ui/): Kafbat UI is free and carries real RBAC, masking and an audit log. Kpow is licensed per cluster with support behind it. Which fits which team.
- [Kpow vs Kafdrop | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/kpow-vs-kafdrop/): Kafdrop is free with no tier above it. Kpow is licensed per cluster. What each one does well, where each runs out, and which fits your cluster.
- [Kpow vs Lenses.io | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/kpow-vs-lenses/): Lenses.io bills by capability with a user cap. Kpow bills per cluster. How the pricing, the control plane and the exit differ, and which fits which team.
- [Kpow vs Offset Explorer | Kafka UI by Factor House](https://factorhouse.io/resources/kafka/comparisons/kpow-vs-offset-explorer/): Offset Explorer is licensed per named user and installed on a laptop. Kpow is licensed per cluster and shared. What each costs, and which fits which team.
- [Kpow vs Redpanda Console | Kafka UI comparison by Factor House](https://factorhouse.io/resources/kafka/comparisons/kpow-vs-redpanda-console/): Redpanda Console is free, and its governance is licensed to the broker vendor. Kpow prices per cluster and publishes the price. Which one fits which team.
- [Lenses vs Offset Explorer | Kafka tools compared by Factor House](https://factorhouse.io/resources/kafka/comparisons/lenses-vs-offset-explorer/): Offset Explorer is a desktop purchase per named user. Lenses is a control plane billed by tier. What each costs, where each runs out, and who should pick which.
- [Debezium vs Kafka Connect | Kafka Connect by Factor House](https://factorhouse.io/resources/kafka/connect/debezium-vs-kafka-connect/): Debezium and Kafka Connect are not alternatives: Debezium is a CDC connector family that runs on Connect. The real decisions are log-based CDC vs JDBC polling, and Connect cluster vs Debezium Server.
- [Kafka Connect | Kafka Connect REST API by Factor House](https://factorhouse.io/resources/kafka/connect/): Kafka Connect moves data between Kafka and external systems through source and sink connectors. What a deployment is made of, the operational tasks, and where the sub-pages go deeper.
- [Kafka Connect MongoDB example | Kafka Connect by Factor House](https://factorhouse.io/resources/kafka/connect/kafka-connect-mongodb-example/): A production Kafka Connect MongoDB example in both directions: source connector via change streams, sink connector with idempotent writes, DLQ settings and secrets handled properly.
- [Kafka Connect pricing | Kafka Connect by Factor House](https://factorhouse.io/resources/kafka/connect/kafka-connect-pricing/): Kafka Connect is free open source software. The cost is in running it: managed per-task and per-GB billing, network surcharges, or the engineering hours of self-hosting. The TCO math, honestly.
- [Kafka Connect troubleshooting | Failed connectors by Factor House](https://factorhouse.io/resources/kafka/connect/kafka-connect-troubleshooting/): A connector reports RUNNING while its tasks have FAILED. The diagnostic sequence, and how to tell a connector fault from a limit in the external system it writes to.
- [What is Kafka Connect | Kafka Connect API by Factor House](https://factorhouse.io/resources/kafka/connect/what-is-kafka-connect/): Kafka Connect streams data between Kafka and other systems as managed, fault-tolerant tasks. The internal architecture, production scaling, error handling and custom development.
- [Kafka consumer group | Delete consumer group by Factor House](https://factorhouse.io/resources/kafka/development/kafka-clients/): A Kafka consumer group shares the work of consuming a topic, one partition per member. Troubleshooting lag and rebalances, the CLI cheat sheet, coordinator architecture and offset commits.
- [Kafka consumer | Kafka Spring Boot by Factor House](https://factorhouse.io/resources/kafka/development/kafka-consumer/): A Kafka consumer reads records from topic partitions, tracking its own offset. The configuration that decides message loss, rebalance troubleshooting, and poll-loop patterns that survive production.
- [Kafka producer | Kafka Java client by Factor House](https://factorhouse.io/resources/kafka/development/kafka-producer/): A Kafka producer appends records to topic partitions. Configuration and tuning with real numbers, idempotence and delivery guarantees, and the client-library decision that quietly matters most.
- [Kafka Spring Boot | Kafka consumer by Factor House](https://factorhouse.io/resources/kafka/development/spring-boot/): Spring Boot integrates with Kafka through spring-kafka: KafkaTemplate, @KafkaListener and auto-configuration. Production setup, dead letter topics, non-blocking retries, and listener tuning.
- [Kafka offset | Kafka offset management by Factor House](https://factorhouse.io/resources/kafka/essentials/kafka-offset/): A Kafka offset is a record's position in its partition, and a committed offset is a consumer group's bookmark. Reading lag from CURRENT-OFFSET and LOG-END-OFFSET, plus the reset strategies.
- [Kafka use cases | Apache Kafka use cases by Factor House](https://factorhouse.io/resources/kafka/essentials/kafka-use-cases/): The production Kafka use cases with the numbers behind them: real-time analytics, event-driven microservices, change data capture, event sourcing and log aggregation, plus the business case for each.
- [Kafka Docker | Kafka tutorial by Factor House](https://factorhouse.io/resources/kafka/getting-started/kafka-docker/): Running Kafka in Docker: the official images, reliable Docker Compose topologies, the advertised.listeners trap that breaks local connections, and why a single-node container is not a deployment.
- [Kafka tutorial | What is Apache Kafka by Factor House](https://factorhouse.io/resources/kafka/getting-started/kafka-tutorial/): A Kafka tutorial for people who run it in production: zero-downtime upgrades, broker tuning, layered security, troubleshooting signals, and the client settings that decide delivery guarantees.
- [AI agent access to Kafka | Kafka governance by Factor House](https://factorhouse.io/resources/kafka/governance/ai-agent-kafka-access/): Give an AI agent Kafka access without the keys: its own identity and ACLs, read-only first, human approval for destructive changes, audit of every call, and no sensitive payloads in the model.
- [Data governance policies examples | Factor House](https://factorhouse.io/resources/kafka/governance/data-governance-policies-examples/): Worked data governance policy examples for Kafka: schema compatibility rules, topic lifecycle, PII classification tiers, ACL and RBAC templates, and lineage requirements.
- [Stream governance | Data governance policies by Factor House](https://factorhouse.io/resources/kafka/governance/): Stream governance applies data governance to data in motion: schemas enforced at produce time, lineage across topics and jobs, catalogs, and access and quality rules on live streams.
- [Kafka ACL | Multi-tenant architecture by Factor House](https://factorhouse.io/resources/kafka/governance/kafka-acl/): A Kafka ACL allows or denies a principal an operation on a resource from a host. Syntax, copy-paste commands, production best practices and GitOps automation.
- [Kafka authentication | Kafka security by Factor House](https://factorhouse.io/resources/kafka/governance/kafka-authentication/): Kafka authentication verifies every client and broker connection with SASL or mutual TLS. Listener and JAAS blueprints, mechanism choice, rotation and troubleshooting.
- [Kafka data masking | Find and mask PII in topics by Factor House](https://factorhouse.io/resources/kafka/governance/kafka-data-masking/): An audit finds card numbers or emails readable in a Kafka topic. How to find every field that carries PII, choose where to mask it, and prove the masking holds, with the commands for each step.
- [Multi-tenant architecture | RBAC roles by Factor House](https://factorhouse.io/resources/kafka/governance/multi-tenant-architecture/): A multi-tenant Kafka architecture shares one cluster across teams with quotas, ACLs and naming conventions. Isolation, namespaces, chargeback and topology.
- [RBAC roles | Multi-tenant architecture by Factor House](https://factorhouse.io/resources/kafka/governance/rbac-roles/): RBAC roles bundle permissions into named sets like viewer, operator and admin. How role definitions, resource patterns and operation mappings work across the Kafka ecosystem.
- [What is a data governance policy | Factor House](https://factorhouse.io/resources/kafka/governance/what-is-a-data-governance-policy/): A data governance policy is an enforceable rule for how data is structured, accessed, retained and traced. On Kafka it is implemented as configuration and code, not documents.
- [What is envelope encryption | Field encryption by Factor House](https://factorhouse.io/resources/kafka/governance/what-is-envelope-encryption/): Envelope encryption encrypts data with a local data key, then wraps that key with a KMS-held key. On Kafka it is the pattern that makes per-field encryption work at full throughput.
- [Apache Kafka | The complete guide by Factor House](https://factorhouse.io/resources/kafka/): Apache Kafka is a distributed event streaming platform that stores ordered, replayable records in partitioned topics. This hub covers Kafka fundamentals, operations, governance and tooling.
- [Confluent Kafka Docker | Run Kafka in Docker by Factor House](https://factorhouse.io/resources/kafka/production/confluent-kafka-docker/): Running Confluent's Kafka images and the official apache/kafka image in Docker: a working compose file, the advertised-listeners fix, log levels and custom Connect images.
- [Deployment automation | Helm Kubernetes by Factor House](https://factorhouse.io/resources/kafka/production/deployment-automation/): Deployment automation for Kafka: zero-downtime rolling changes, declarative topics and ACLs, Kubernetes operators, Terraform and GitOps for cluster state.
- [Inspect Kafka from the terminal | Kafka tools by Factor House](https://factorhouse.io/resources/kafka/production/inspect-kafka-from-terminal/): Inspect Kafka over SSH with no web UI: brokers, under-replicated partitions, safe message peeks, Avro, consumer lag and Connect status, checked against Kafka 4.3 and kcat.
- [Kafka CLI commands | List topics, reset offsets by Factor House](https://factorhouse.io/resources/kafka/production/kafka-cli-commands/): The Kafka CLI commands operators run in production, checked against Kafka 4.3: topics, produce and consume, lag and offset resets, configs, cluster health and ACLs, with KRaft-era flag changes.
- [Kafka Streams | ksqlDB by Factor House](https://factorhouse.io/resources/kafka/streams/): Kafka Streams is a Java library for stream processing that runs inside your application, with state in local RocksDB stores and changelog topics. The operational realities, and where Flink wins.
- [Kafka Streams documentation | Kafka Streams by Factor House](https://factorhouse.io/resources/kafka/streams/kafka-streams-documentation/): The Kafka Streams documentation divides into four layers: API reference, configuration surface, state store internals, and the upgrade guide. Which layer answers which production question.
- [Kafka ksqlDB | Kafka Streams by Factor House](https://factorhouse.io/resources/kafka/streams/ksql/): ksqlDB is the streaming SQL layer for Kafka: streams and tables in SQL, persistent queries with state in internal topics. The push-vs-pull trap, and where it stands as Confluent shifts to Flink.
- [Apache Kafka vs Confluent Kafka: cost and licence | Factor House](https://factorhouse.io/resources/kafka/tools/apache-kafka-vs-confluent-kafka/): The broker is the same. What differs is licensing, bundled components, deployment models and support. Where the Apache line sits, what Confluent adds, and the dependency questions to ask.
- [Best tools to manage Kafka ACLs, scored | Factor House](https://factorhouse.io/resources/kafka/tools/kafka-acl-management-tools/): kafka-acls.sh, Terraform, kafka-gitops, Julie Ops, Strimzi, Klaw, Apache Ranger, OPA and the Kafka UIs, scored on auditability, pattern handling, automation, drift and visibility.
- [Kafka agent skills | Claude Kafka tools by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-agent-skills/): Kafka agent skills for Claude Code and other coding agents, scored on one rubric: what the agent sees and changes, guardrails, SSO and RBAC reuse, context cost, and availability and cost a year.
- [Best tools for Kafka audit logging | Factor House](https://factorhouse.io/resources/kafka/tools/kafka-audit-logging-tools/): Kafka audit logging happens at three layers: broker authorization logs, the audit trail of the tools people use, and data lineage. Every option for each layer, scored on the same rubric.
- [Best tools to manage Kafka broker configs | Factor House](https://factorhouse.io/resources/kafka/tools/kafka-broker-config-tools/): kafka-configs.sh, Strimzi, Terraform, JulieOps, AKHQ, Kafbat UI, Confluent Control Center and Kpow, scored on seeing the running config, catching drift, changing it safely and knowing who changed it.
- [Best Kafka broker health monitoring tools | Factor House](https://factorhouse.io/resources/kafka/tools/kafka-broker-health-tools/): Prometheus with the JMX Exporter, kafka_exporter, KMinion, Cruise Control, Datadog, New Relic, Confluent Control Center and Kpow, scored on the broker signals they actually see.
- [Kafka CLI tools | kafkactl, kcat and kcl by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-cli-tools/): Kafka CLIs scored on coverage, contexts, auth, agent safety, maintenance and annual cost: the bundled scripts, kcat, kafkactl, kaf, kcl, rpk, Confluent CLI, Zoe, kt and the Kpow CLI.
- [Kafka Connect monitoring tools | Kafka Connect by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-connect-monitoring-tools/): Kafka Connect monitoring tools, from the REST API and JMX to Strimzi, Kpow and Lenses, scored on task state, per-task metrics, restarts, alerting and access.
- [Kafka consumer lag monitoring tools | Kafka tools by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-consumer-lag-monitoring-tools/): Kafka consumer lag tools compared on one rubric: client-side against cluster-side measurement, offset lag against time lag, per-partition detail and alerting. The CLI, JMX, Burrow, Kpow and more.
- [Kafka data masking tools | Field level encryption by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-data-masking-tools/): Kafka data masking tools hide sensitive fields at ingestion, in the client, in a proxy or at view time. Eleven options scored on what each placement protects against.
- [Best tools to control destructive Kafka operations | Factor House](https://factorhouse.io/resources/kafka/tools/kafka-destructive-ops-tools/): Topic deletes, offset resets and config changes run the moment they are allowed. Every control that stops a bad one, from broker settings and authorizers to GitOps and Kafka UIs, scored on one rubric.
- [Kafka dead letter queue tools | Kafka tools by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-dlq-tools/): Kafka DLQ tools compared on one rubric: error context, triage, repair, targeted replay and governance. Covers Kafka Connect, Spring Kafka, Kafka Streams, kcat, Kpow, AKHQ and Kafbat UI.
- [Best Kafka governance tools for financial services | Factor House](https://factorhouse.io/resources/kafka/tools/kafka-governance-tools-financial-services/): Kafka governance tools for banks, payments and insurance, scored against what DORA, PCI DSS, GDPR and SOX ask for: limited access, strong authentication, controlled change, audit and masked data.
- [Best Kafka MCP servers | Kafka tools by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-mcp-servers/): Every Kafka MCP server and AI-agent access route, scored on governance: read vs write scope, permission model, audit, approval for destructive ops, message exposure to the model, and deployment.
- [Kafka message search tools | Kafka data inspect by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-message-search-tools/): Kafka is a log, not an index, so every search is a scan. Eleven ways to search messages across Kafka topics, scored on scan scope, filter language, multi-topic reach and masking.
- [Kafka multi-cluster management tools | Kpow by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-multi-cluster-tools/): Twelve tools to manage multiple Kafka clusters from one place, scored on mixed distributions, deployment shape, per-cluster RBAC, cross-cluster views and cost at N clusters.
- [Kafka consumer offset reset tools | Kafka tools by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-offset-management-tools/): How to reset Kafka consumer group offsets with the native CLI, dry run first, then tools scored on preview, scope, strategies, the inactive-group rule and audit: Kpow, AKHQ, Kafbat UI and Conduktor.
- [Best tools to reassign Kafka partitions | Kafka by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-partition-rebalancing-tools/): kafka-reassign-partitions.sh, Cruise Control, Strimzi, topicctl, topicmappr, Kpow and the managed-service options, scored on planning, throttling, batching, progress, cancellation and audit.
- [Kafka poison pill tools | Kafka tools by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-poison-pill-tools/): Kafka poison pill tools compared on one rubric: isolation in the consumer, finding the bad record, skipping it safely, keeping a copy, and who may do it. Spring Kafka, the CLI, Kpow, AKHQ and more.
- [Kafka RBAC tools | Kafka multi tenancy by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-rbac-tools/): Apache Kafka authorizes with ACLs, not roles. Eleven ways to add RBAC, scored on where each one enforces, who it binds, what it covers and how it audits change.
- [Kafka schema registry tools: 10 options scored | Factor House](https://factorhouse.io/resources/kafka/tools/kafka-schema-registry-tools/): Confluent Schema Registry, Apicurio, Karapace, AWS Glue, Redpanda, Kpow, Kafbat UI and AKHQ, scored on formats, compatibility checks, diffs, multiple registries and RBAC.
- [Best tools for Kafka SSO integration | Factor House](https://factorhouse.io/resources/kafka/tools/kafka-sso-tools/): Kafka SSO is two jobs: people signing in to Kafka UIs through Okta, Entra ID or Keycloak, and services authenticating to brokers with OAuth tokens. The tools for each half, scored on one rubric.
- [Kafka Tool download | Kafka UI by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-tool-download/): Kafka Tool is the former name of Offset Explorer. What each download actually installs, the production jobs a Kafka GUI has to do, and the enterprise constraints to check before connecting one.
- [Best tools to manage Kafka topics at scale, scored | Factor House](https://factorhouse.io/resources/kafka/tools/kafka-topic-management-tools/): Strimzi, Terraform, topicctl, JulieOps, Conduktor, AKHQ, Kafbat UI and Kpow, scored on topics as code, creation guardrails, scoped self-service, audit, multi-cluster reach and bulk operations.
- [Kafka TUI | Kafka terminal UI by Factor House](https://factorhouse.io/resources/kafka/tools/kafka-tui/): Kafka terminal UIs scored on what you can see and change, multi-cluster, auth, keyboard UX and annual cost: ktea, Kaskade, kafui, Yozefu, kaftui, karat, the Kpow TUI and plain scripts.
- [Kafka topic | What is a Kafka topic by Factor House](https://factorhouse.io/resources/kafka/topics/): A Kafka topic is a named, append-only log, and in production it is the unit you operate on. The CLI verbs, partition and replication mechanics, retention, compaction and the troubleshooting moves.
- [Kafka topic example | Topic naming convention by Factor House](https://factorhouse.io/resources/kafka/topics/kafka-topic-example/): A production Kafka topic example: the exact creation command with durability properties, the same topic as Terraform and Strimzi code, the naming convention that scales, and the schema contract.
- [Kafka topic vs partition | Kafka topics by Factor House](https://factorhouse.io/resources/kafka/topics/kafka-topic-vs-partition/): A topic is the logical name; a partition is the physical log. How replication, ordering, parallelism and key hashing follow the physical unit, and why keyed topics lock their count.
- [Kafka AI troubleshooting | AI tools for Kafka by Factor House](https://factorhouse.io/resources/kafka/troubleshooting/kafka-ai-troubleshooting/): Troubleshoot Kafka with an AI agent: the read-only access to give it, the questions to ask, how to tell a connector fault from a database or catalog fault, and how to check its answer before you act.
- [Kafka deserialization error | Schema errors by Factor House](https://factorhouse.io/resources/kafka/troubleshooting/kafka-deserialization-error/): A consumer throwing SerializationException and looping on one offset has one of five causes. How to tell which, and how to unblock the partition without losing data.
- [Kafka poison pill | Kafka troubleshooting by Factor House](https://factorhouse.io/resources/kafka/troubleshooting/kafka-poison-pill/): A consumer stuck on one offset, lag climbing on one partition, the same deserialization error looping. How to confirm it is a poison pill, inspect the record, and skip it safely with a dry run first.
- [Unbalanced Kafka cluster | Diagnose and fix by Factor House](https://factorhouse.io/resources/kafka/troubleshooting/kafka-unbalanced-cluster/): One broker hot, disk alerts on another, leader counts uneven. How to tell leader imbalance from data imbalance, the commands that fix each, how to throttle the move, and how to stop it coming back.
- [Kafka vs RabbitMQ performance | Kafka performance by Factor House](https://factorhouse.io/resources/kafka/troubleshooting/kafka-vs-rabbitmq-performance/): Kafka and RabbitMQ perform differently because their storage models differ. Throughput and latency behaviour, durability trade-offs, benchmark methodology and the workload facts that decide the fit.
- [What is Kafka rebalancing | Kafka rebalance by Factor House](https://factorhouse.io/resources/kafka/troubleshooting/what-is-kafka-rebalancing/): Kafka rebalancing redistributes a consumer group's partitions when membership changes. The triggers, the three timeouts, cooperative rebalancing, KIP-848 and the metrics that explain incidents.

## Pages

- [About Factor House and Kpow | Factor House](https://factorhouse.io/about/): Factor House builds enterprise-ready tools for engineers working with real-time, streaming data. Learn who we are and what we believe.
- [Accelerating incident response with AI queries | Factor House](https://factorhouse.io/articles/accelerating-incident-response-advanced-filters-ai-powered-queries/): Fix streaming data failures faster. Learn how Kpow uses advanced kJQ filtering, BYO AI, and Streaming Search to slash incident response times.
- [How Adidas uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/adidas-kafka-architecture/): A deep-dive into Adidas's Kafka architecture, covering observability at 100 billion messages per day, self-service topic provisioning, and custom GoLang tooling.
- [How Airbnb uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/airbnb-kafka-architecture/): A deep-dive into Airbnb's Kafka architecture, covering six production systems, 35+ billion daily events, SpinalTap CDC, Flink-based personalisation, and Kafka as a write-ahead log.
- [AKHQ: pricing and alternatives | Factor House](https://factorhouse.io/articles/akhq/): AKHQ review for 2026: features, known limitations, pricing, and the best alternatives for teams that need more than open-source tooling.
- [Apache Kafka 4.3.0: A guide for platform engineers | Factor House](https://factorhouse.io/articles/apache-kafka-4-3-0/): Kafka 4.3.0 covers broker cordoning, partition size metrics, share group tuning, and tiered storage fixes. Here's what platform engineers need to act on.
- [How Apple uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/apple-kafka-architecture/): A deep-dive into Apple's Kafka architecture, covering their managed internal platform, Strimzi on EKS, tiered storage, zero-data-movement balancing, and mTLS migration.
- [How Barclays uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/barclays-kafka-architecture/): A deep-dive into Barclays' Kafka architecture, covering dual-environment deployment on AWS and IBM Z-Linux, operating practices, and the broader streaming stack.
- [Best free Kafka UI tools in 2026 | Factor House](https://factorhouse.io/articles/best-free-kafka-ui-tools/): Compare the best free Kafka UI and management tools in 2026: Kpow Community Edition, Conduktor Console Community, Lenses Community Edition, AKHQ, and Kafbat UI.
- [Best Kafka management tools for 2026 | Factor House](https://factorhouse.io/articles/best-kafka-management-tools/): Compare the 10 best Kafka management tools for 2026, including Kpow, AKHQ, Conduktor, and Confluent Control Center. Covers pricing, RBAC, and deployment requirements.
- [Best Kafka monitoring tools for 2026 | Factor House](https://factorhouse.io/articles/best-kafka-monitoring-tools/): Compare 12 Kafka monitoring tools for 2026, from enterprise-grade Kpow to open-source AKHQ and Prometheus. Covers deployment, pricing, and key trade-offs.
- [Best practices for Kafka data observability | Factor House](https://factorhouse.io/articles/best-practices-kafka-data-observability/): 12 best practices for Kafka data observability covering consumer lag monitoring, schema enforcement, end-to-end auditing, DLQs, and lineage, with an implementation roadmap.
- [Beyond JMX: supercharging Grafana dashboards | Factor House](https://factorhouse.io/articles/beyond-jmx-supercharging-grafana-dashboards-with-high-fidelity-metrics/): Move beyond raw JMX noise. Learn how to feed high-fidelity, pre-calculated Kafka metrics from Kpow into Grafana for proactive capacity planning and incident response.
- [Beyond Kafka: signals from Current London 2025 | Factor House](https://factorhouse.io/articles/beyond-kafka-sharp-signals-from-current-london-2025/): At Current London 2025, the shift from Kafka Summit signaled a move toward streaming-first AI, system-level control, and production-ready Flink. Here's what Factor House saw and learned.
- [Beyond Reagent: migrating to React 19 | Factor House](https://factorhouse.io/articles/beyond-reagent-migrating-to-react-19-with-hsx-and-rfx/): Introducing HSX and RFX, two new open source Clojure UI libraries by Factor House. They are drop-in replacements for Reagent and Re-Frame, enabling migration to React 19.
- [Real-time leaderboards with Kafka and Flink | Factor House](https://factorhouse.io/articles/building-a-real-time-leaderboard-with-kafka-and-flink/): Learn how to build a real-time "Top-K" analytics pipeline using Apache Kafka, Apache Flink, and Streamlit to ingest, process, and visualize live events on an interactive dashboard.
- [How Bytedance uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/bytedance-kafka-architecture/): ByteDance ran Kafka at tens of TB/s before replacing it with ByteMQ, a Kafka-compatible platform separating storage from compute. How it works, and why migrating cut resource cost by roughly 70%.
- [Clone to topic for DLQs in Apache Kafka | Factor House](https://factorhouse.io/articles/clone-to-topic-for-kafka-dlq/): Learn about Clone to Topic, the latest feature available in Kpow 96.2, enabling you to replay Dead Letter Queue (DLQ) records inside a governed UI.
- [How Cloudflare uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/cloudflare-kafka-architecture/): A deep-dive into Cloudflare's Kafka architecture: use cases at trillion-message scale, 14 clusters, internal tooling decisions, and the engineering lessons behind a decade of Kafka operations.
- [CMAK: pricing and alternatives | Factor House](https://factorhouse.io/articles/cmak/): CMAK is a free, open-source Kafka admin tool from Yahoo. This review covers features, KRaft limitations, security gaps, and the best alternatives for 2026.
- [Conduktor: pricing and alternatives | Factor House](https://factorhouse.io/articles/conduktor/): Conduktor review for 2026: pricing, strengths, deployment trade-offs, and how it compares to alternatives for enterprise Kafka governance teams.
- [Confluent Control Center: pricing and alternatives | Factor House](https://factorhouse.io/articles/confluent-control-center/): An honest technical review of Confluent Control Center in 2026, covering features, deployment, pricing, and the best alternatives for Kafka teams.
- [Amazon Corretto 11 Memory Issues | Factor House](https://factorhouse.io/articles/corretto-memory-issues/): A move to v2 cgroups by several Linux distributions, including Amazon Linux 2022 and RHEL 9, highlights a JVM issue in Amazon Corretto 11 that can cause a container to exit with OOMKilled errors.
- [Data lineage support in Factor Platform | Factor House](https://factorhouse.io/articles/data-governance-for-kafka-introducing-lineage-support-in-factor-platform/): Learn how Factor Platform brings OpenLineage metadata into your Kafka environment, making data ownership, PII classification, and lineage visible by default.
- [Data Inspect Enhancements in Kpow 94.5 | Factor House](https://factorhouse.io/articles/data-inspect-enhancements-94-5/): Kpow 94.5 enhances data inspection with comma-separated kJQ Projection expressions, in-browser search, and flexible deserialization. It also adds high-performance streaming and new kJQ transforms.
- [Improvements to Data Inspect in Kpow 94.3 | Factor House](https://factorhouse.io/articles/data-inspect-improvements-94-3/): Kpow's 94.3 release lets you query topics using plain English with AI-powered filtering, automatically decode any message format, and use new enhancements to the kJQ language.
- [How Datadog uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/datadog-kafka-architecture/): A deep-dive into Datadog's Kafka architecture, covering use cases, scale, engineering decisions, and key contributors across hundreds of clusters.
- [Dead letter queues in Kafka: patterns and pitfalls | Factor House](https://factorhouse.io/articles/dead-letter-queues-kafka/): How to implement a dead letter queue in Apache Kafka, with Spring Kafka, Connect, and Streams examples, and the production failure modes to avoid.
- [Defense in depth: unifying RBAC and data policies | Factor House](https://factorhouse.io/articles/defense-in-depth-unifying-rbac-and-data-policies/): Balance Kafka velocity and compliance. Learn how Kpow uses RBAC and Data Policies for safe, self-service production debugging without manual tickets.
- [Delete Records in Kafka | Factor House](https://factorhouse.io/articles/delete-records-in-kafka/): This article provides a step-by-step guide on the various ways to delete records in Kafka.
- [Deploy Clojure projects to Maven Central | Factor House](https://factorhouse.io/articles/deploy-clojure-projects-to-maven-central/): This article provides a step-by-step guide to how we deploy the Kpow Kafka Streams Monitoring Agent to Maven Central with Leiningen.
- [Deploy Kpow on EKS via AWS Marketplace using Helm | Factor House](https://factorhouse.io/articles/deploy-kpow-on-eks-via-aws-marketplace/): Deploy Kpow on Amazon EKS via the AWS Marketplace using Helm and eksctl to automate IRSA, enabling secure license validation via AWS License Manager and usage reporting for hourly subscriptions.
- [How DoorDash uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/doordash-kafka-architecture/): A deep dive into DoorDash's Kafka architecture, covering the Iguazu event platform, Flink-based ML feature pipelines, self-serve topic governance, and hundreds of billions of daily events.
- [Enhanced URP detection in Kpow | Factor House](https://factorhouse.io/articles/enhanced-urp-detection/): Kpow now offers enhanced under-replicated partition detection for accurate Kafka health monitoring. The improved calculation identifies URPs even when brokers are offline, aiding fault tolerance.
- [How to prove Kafka compliance in an audit | Factor House](https://factorhouse.io/articles/ensuring-your-data-streaming-stack-is-ready-for-the-eu-data-act/): What Apache Kafka records on its own, the evidence an auditor asks for, and where the EU Data Act fits, with each regulatory point cited to the Regulation itself.
- [Factor House expands to Europe | Factor House](https://factorhouse.io/articles/factor-house-expands-to-europe/): Discover how Factor House's expansion into Germany brings enterprise-grade control and monitoring to European teams running Kafka and Flink.
- [Introducing Factor House 2.0 🚀 | Factor House](https://factorhouse.io/articles/factor-house-flow/): Today we introduce Flex for Apache Flink, and announce the Factor Platform, the future of distributed systems engineering.
- [Factor House Product VPAT | Factor House](https://factorhouse.io/articles/factor-house-product-vpat/): We first published a VPAT in the release notes of Kpow for Apache Kafka v92.4, with one available in every Kpow release since. We are extending that commitment to all future Factor House releases.
- [A final goodbye to OperatrIO | Factor House](https://factorhouse.io/articles/final-goodbye-operatr-io/): 2025 is a pivotal moment at Factor House, formerly Operatr.IO. We've announced our fundraise and have more to share about our roadmap this year, so it's time to retire the io.operatr artifacts.
- [Foundational Kafka data inspection in Kpow | Factor House](https://factorhouse.io/articles/foundational-kafka-data-inspection-in-kpow/): Stop fighting complex Kafka serialization. Learn how Kpow uses Auto SerDes, kJQ, and transparent queries to streamline data inspection.
- [From batch to real-time CDC with Debezium | Factor House](https://factorhouse.io/articles/from-batch-to-real-time-a-hands-on-cdc-project-with-debezium-kafka-and-thelook-ecommerce-data/): This project turns the static "theLook" eCommerce dataset into a live stream. A Python generator simulates activity in PostgreSQL while Debezium captures changes and streams them to Kafka.
- [From Bootstrap to Blackbird: The Future of Factor House](https://factorhouse.io/articles/from-bootstrap-to-blackbird/): Factor House has closed a $5M seed round led by Blackbird Ventures to accelerate the commercial release of the Factor Platform, ending a five-year bootstrapped journey.
- [How Goldman Sachs uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/goldman-sachs-kafka-architecture/): A deep-dive into Goldman Sachs's Kafka architecture, covering use cases across three divisions, migration to Amazon MSK, resilience design, and key engineering decisions.
- [How Grab uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/grab-kafka-architecture/): A deep-dive into Grab's Kafka architecture, how the Coban team built a terabyte-per-hour streaming platform serving 300 billion events a week across GrabFood, GrabPay, mobility, and more.
- [How to monitor Kafka consumer lag | Factor House](https://factorhouse.io/articles/how-to-monitor-kafka-consumer-lag/): Learn what Kafka consumer lag is, why it occurs, and how to monitor it using built-in tools, custom solutions, and Kafka monitoring platforms.
- [Integrate Confluent-compatible registries in Kpow | Factor House](https://factorhouse.io/articles/integrate-confluent-compatible-registries-kpow/): This guide shows how to integrate Confluent-compatible schema registries - Confluent Schema Registry, Apicurio Registry, and Karapace - and manage them all through Kpow.
- [Integrate Kpow with Bufstream | Factor House](https://factorhouse.io/articles/integrate-kpow-with-bufstream/): Integrate Kpow with Bufstream in minutes. Gain unified visibility and control over your Kafka-compatible broker and Buf Schema Registry in the Kpow UI.
- [Integrate Kpow with Google Managed Schema Registry | Factor House](https://factorhouse.io/articles/integrate-kpow-with-google-schema-registry/): Kpow 94.3 integrates with Google Cloud's managed Schema Registry using native OAuth. This guide covers configuring authentication and using Kpow to manage Avro schemas.
- [Integrate Kpow with OCI Streaming for Kafka | Factor House](https://factorhouse.io/articles/integrate-kpow-with-oci-streaming/): Integrate Kpow with Oracle Cloud Infrastructure Streaming with Apache Kafka in minutes and gain unified visibility and control over your OCI brokers and ecosystem components.
- [Integrate Kpow with Redpanda Streaming Platform | Factor House](https://factorhouse.io/articles/integrate-kpow-with-redpanda/): Integrate Kpow with Redpanda in minutes. Gain unified visibility and control over your Redpanda brokers and built-in Schema Registry in the Kpow UI.
- [Integrate Kpow with StreamNative Cloud | Factor House](https://factorhouse.io/articles/integrate-kpow-with-streamnative-cloud/): Integrate Kpow with StreamNative Cloud in minutes. Gain unified visibility and control over your managed Kafka brokers and Schema Registry in the Kpow UI.
- [Integrate Kpow with WarpStream | Factor House](https://factorhouse.io/articles/integrate-kpow-with-warpstream/): Integrate Kpow with WarpStream in minutes. Gain unified visibility and control over your BYOC Kafka data plane and Schema Registry in the Kpow UI.
- [Introducing Factor House Docs | Factor House](https://factorhouse.io/articles/intro-factor-house-docs/): Factor House Docs is now live: a unified hub for our product documentation, with a new task-based structure, interactive kJQ examples, and powerful search across Kpow, Flex, and Factor Platform.
- [Introduction to Factor House Local | Factor House](https://factorhouse.io/articles/intro-to-factor-house-local/): Factor House Local offers pre-configured Docker environments for Kafka, Flink, Spark, and Iceberg with Kpow and Flex, plus hands-on labs for building a Kafka client through to a full data lakehouse.
- [Introducing Kpow's new API | Factor House](https://factorhouse.io/articles/introducing-kpows-new-api/): Kpow's new API lets you manage topics, consumer groups, and monitor Kafka clusters directly from your own tools, mirroring the functionality of the Kpow user interface.
- [Introducing Webhook Support in Kpow | Factor House](https://factorhouse.io/articles/introducing-webhook-support-in-kpow/): This guide shows how to configure Kpow webhooks that send real-time audit log alerts from your Kafka environment into collaboration platforms like Slack and Microsoft Teams.
- [Our Java compatibility and evolution strategy | Factor House](https://factorhouse.io/articles/java-compatibility-and-evolution-strategy/): This post outlines our release process and future plans for Java support at Factor House, including how we approach deprecating older versions for diverse customer bases.
- [Factor House launches open community Slack | Factor House](https://factorhouse.io/articles/join-the-conversation-community-slack/): Factor House has opened a public Slack for anyone working with streaming data, offering faster peer-to-peer support, open discussion, and a friendly on-ramp for newcomers.
- [How JPMorgan uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/jpmorgan-kafka-architecture/): A deep dive into JPMorgan Chase's Kafka architecture, covering multi-tenant cluster design, managed Kafka Connect, the Photon Framework, and decisions behind a large-scale deployment.
- [Kadeck: pricing and alternatives | Factor House](https://factorhouse.io/articles/kadeck/): Kadeck review for 2026: features, deployment, pricing, and how it compares to AKHQ, Kafbat, Conduktor, and Kpow for Kafka management teams.
- [Kafbat UI: pricing and alternatives | Factor House](https://factorhouse.io/articles/kafbat-ui/): A practical review of Kafbat, the open-source kafka-ui fork: features, deployment, security, pricing, and the best alternatives in 2026.
- [Kafdrop: pricing and alternatives | Factor House](https://factorhouse.io/articles/kafdrop/): Kafdrop review for 2026: strengths, limitations, pricing, and the best alternatives for platform and data engineers running production Kafka clusters.
- [Kafka 4.1 Release: Queues, Stream Groups, and More | Factor House](https://factorhouse.io/articles/kafka-4-1-release-announcement/): Apache Kafka 4.1 has landed with queue support in preview, improved Kafka Streams coordination, and new security and metrics features for real-time data systems.
- [Kafka alerting with Kpow, Prometheus, Alertmanager | Factor House](https://factorhouse.io/articles/kafka-alerting-with-kpow-prometheus-and-alertmanager/): This article covers setting up Kafka alerting with Kpow using Prometheus and Alertmanager, so you can detect and predict problems across brokers, Streams, Connect, and schema registries.
- [Apache Kafka architecture: a complete guide | Factor House](https://factorhouse.io/articles/kafka-architecture/): A complete guide to Apache Kafka architecture: internals, components, KRaft, replication, consumers, Connect, Streams, and deployment options.
- [Kafka broker monitoring | Factor House](https://factorhouse.io/articles/kafka-broker-monitoring/): How to monitor Kafka brokers and the cluster they form: key JMX metrics, alert rules, capacity signals, health check scripts and step-by-step diagnosis.
- [The complete guide to Kafka change data capture | Factor House](https://factorhouse.io/articles/kafka-cdc-change-data-capture/): Learn how to implement change data capture with Kafka using Debezium. Includes working PostgreSQL CDC examples, architecture patterns, and monitoring.
- [Kafka cluster management: a practical guide | Factor House](https://factorhouse.io/articles/kafka-cluster-management/): A practical guide to Kafka cluster management: architecture sizing, day-to-day operations, performance tuning, KRaft migration, and monitoring for production clusters.
- [Kafka consumer performance tuning and monitoring | Factor House](https://factorhouse.io/articles/kafka-consumer-monitoring/): Learn which Kafka consumer metrics matter most, how to interpret them, and which configuration changes will improve performance and reduce lag.
- [Kafka dashboard: features that matter | Factor House](https://factorhouse.io/articles/kafka-dashboard/): A Kafka dashboard gives you real-time visibility into consumer lag, broker health, and partition state. Here's what to look for and how Kpow delivers it in production.
- [Kafka data management with Kpow | Factor House](https://factorhouse.io/articles/kafka-data-management-with-kpow/): Enterprise Kafka adoption brings scale, but working with streaming data creates operational friction. This article shows how Kpow addresses four friction points to unlock engineering productivity.
- [Kafka DLQ per consumer group, not per topic | Factor House](https://factorhouse.io/articles/kafka-dlq-consumer-side/): Why each Kafka consumer group should own its DLQ and retry topic, why replay never goes back to the main topic, and why retrying transient downstream failures is an anti-pattern.
- [Kafka management console: what to look for | Factor House](https://factorhouse.io/articles/kafka-management-console/): A Kafka management console gives your team full control of topics, consumers, schemas, and connectors from one UI. See what to look for and how Kpow delivers it.
- [Kafka message key best practices | Factor House](https://factorhouse.io/articles/kafka-message-key-best-practices/): A technical guide to Kafka message key best practices covering partitioning, ordering guarantees, hot keys, log compaction, and serialization for production systems.
- [Kafka message size best practice | Factor House](https://factorhouse.io/articles/kafka-message-size-best-practice/): How large should Kafka messages be in production? Covers sizing tiers, the four-config chain, compression codecs, and patterns for handling payloads above 1 MB.
- [Kafka performance monitoring: metrics that matter | Factor House](https://factorhouse.io/articles/kafka-monitoring/): The Kafka metrics that matter when something breaks: the ten critical broker, replication, JVM and storage metrics, alert thresholds, and KRaft changes.
- [Kafka observability with Kpow | Factor House](https://factorhouse.io/articles/kafka-observability-with-kpow-driving-operational-excellence/): Operating Apache Kafka at scale often leads to reactive maintenance. This article covers three gaps in context, data quality, and governance, and a strategy for operational excellence with Kpow.
- [Kafka partition key best practices | Factor House](https://factorhouse.io/articles/kafka-partition-key-best-practices/): How Kafka partition keys work, what makes a good key, and practical guidance on cardinality, hot partitions, compaction, cross-language hashing, and safe key migration.
- [Kafka 3.2.0: idempotent producer breaking change | Factor House](https://factorhouse.io/articles/kafka-producer-breaking-change/): Apache Kafka KIP-679 changes the default Producer configuration to enable idempotence by default. This can cause message production to fail after updating to the 3.2.0 kafka-client libraries.
- [A detailed guide to Kafka producer monitoring | Factor House](https://factorhouse.io/articles/kafka-producer-monitoring/): A practical guide to Kafka producer metrics, JMX collection, alerting thresholds, and diagnostic scripts for Java-based Kafka producers.
- [Kafka scaling best practices: An in-depth primer | Factor House](https://factorhouse.io/articles/kafka-scaling-best-practices/): A practical guide to scaling Apache Kafka in production, covering partitioning strategy, consumer group design, broker sizing, KRaft migration, and more.
- [Kafka security architecture for production | Factor House](https://factorhouse.io/articles/kafka-security-architecture/): Kafka ships insecure by default. Learn how to build a production-ready Kafka security architecture covering TLS encryption, SASL authentication, ACLs, audit logging, and network isolation.
- [Kafka topic partition best practices | Factor House](https://factorhouse.io/articles/kafka-topic-partition-best-practices/): Size Kafka topic partitions correctly from day one. Covers the throughput formula, the keyed topic asymmetry, KRaft-era limits, and operational best practices.
- [Kafka UI: The Ultimate Guide | Factor House](https://factorhouse.io/articles/kafka-ui/): A Kafka UI is a web interface for managing Apache Kafka, giving operators visual control over topics, consumers, brokers, and connectors without the CLI.
- [KIP-1150: diskless topics in Kafka explained | Factor House](https://factorhouse.io/articles/kip-1150-diskless-topics-explained/): Discover how Kafka's KIP-1150 Diskless Topics aim to bring cloud-native scalability and cost-efficiency by natively utilizing object storage, and what it means for your streaming architecture.
- [KIP-932 queues for Kafka explained | Factor House](https://factorhouse.io/articles/kip-932-queues-for-kafka-explained/): Discover how Kafka's KIP-932 Share Groups bring native queue semantics to your event streaming architecture, and the new complexities engineers must manage.
- [Announcing Kpow Community Edition 🚀 | Factor House](https://factorhouse.io/articles/kpow-community-edition/): The announcement post for Kpow Community Edition, our free toolkit for Apache Kafka clusters, schema registries, and connect installations. Current terms and downloads are on the product page.
- [Kpow Custom Serdes and Protobuf v4.31.1 | Factor House](https://factorhouse.io/articles/kpow-custom-serdes-protobuf-4-31-1/): This post explains an update in the version of protobuf libraries used by Kpow, and a possible compatibility impact this update may cause to user defined Custom Serdes.
- [Lenses.io review: pricing and alternatives | Factor House](https://factorhouse.io/articles/lenses/): Lenses.io review for 2026: honest assessment of SQL Studio, deployment complexity, pricing, and when to consider alternatives like Conduktor or Kpow.
- [How LinkedIn uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/linkedin-kafka-architecture/): A deep-dive into LinkedIn's Kafka architecture, covering use cases, scale, engineering decisions, and key contributors.
- [Reset Kafka consumer offsets safely with Kpow | Factor House](https://factorhouse.io/articles/manage-kafka-consumer-offsets-with-kpow/): Resetting a consumer group's offsets makes it reprocess or skip records. How to clear, reset and skip Kafka consumer offsets safely, step by step, in Kpow.
- [Manage Kafka Visibility with Multi-Tenancy | Factor House](https://factorhouse.io/articles/manage-kafka-visibility-with-multi-tenancy/): This article teaches you how to configure Kpow to restrict visibility of Kafka resources with Multi-Tenancy.
- [MirrorMaker 2 migration: moving to Kafka Connect | Factor House](https://factorhouse.io/articles/mirrormaker-2-connect-managed/): Moving MirrorMaker 2 off connect-mirror-maker.sh onto Kafka Connect: replication progress doesn't migrate, offset translation is off by default, and when not to bother.
- [Lambda retry visibility for Amazon MSK | Factor House](https://factorhouse.io/articles/msk-lambda-retry-visibility/): Lambda's default retry behavior for MSK-triggered functions gives you no attempt count and no way to tell a first try from a tenth. A lab comparing implicit and explicit retry paths closes the gap.
- [How Netflix uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/netflix-kafka-architecture/): A deep-dive into Netflix's Kafka architecture, covering the Keystone pipeline, Data Mesh platform, scale figures from 700 billion to 2 trillion events per day, and the engineering decisions behind it.
- [How New Relic uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/new-relic-kafka-architecture/): A deep-dive into New Relic's Kafka architecture, covering use cases, scale, engineering decisions and key contributors.
- [How Notion uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/notion-kafka-architecture/): A deep-dive into Notion's Kafka architecture, covering use cases, scale, engineering decisions, and key contributors across their data lake and AI pipelines.
- [Kafka audit trail: stream admin changes to Slack | Factor House](https://factorhouse.io/articles/operational-transparency-audit-trail-integrated-with-webhooks/): Apache Kafka keeps no built-in record of who made an admin change. How Kpow's audit log records user actions and streams them to Slack with a webhook.
- [Operatr.IO has a new name: Meet Factor House](https://factorhouse.io/articles/operatr-io-has-a-new-name-meet-factor-house/): Operatr.IO is now Factor House, an independent, engineering-led software house that builds Kpow, an enterprise tool for Apache Kafka.
- [Our Commitment to Engineers | Factor House](https://factorhouse.io/articles/our-commitment-to-engineers/): With our funding announcement and the upcoming Factor Platform launch, some customers are asking what it means for Kpow and Flex. Here's our answer: at Factor House, we're here for engineers.
- [How PagerDuty uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/pagerduty-kafka-architecture/): A deep-dive into PagerDuty's Kafka architecture, covering event ingestion, notification scheduling, task execution, and the engineering decisions behind each.
- [How PayPal uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/paypal-kafka-architecture/): A deep-dive into PayPal's Kafka architecture, covering use cases, scale, engineering decisions, and key contributors across a fleet handling 1.3 trillion messages per day.
- [How Pinterest uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/pinterest-kafka-architecture/): A deep-dive into Pinterest's Kafka architecture, covering use cases, scale, engineering decisions, and key contributors. From 15 million to 40 million messages per second across 3,000 brokers.
- [How to query a Kafka topic | Factor House](https://factorhouse.io/articles/query-a-kafka-topic/): Querying Kafka topics is a critical but often complex and time-consuming task for engineers building streaming applications. Kpow's data inspect feature simplifies and speeds up Kafka topic queries.
- [Rapid Kafka diagnostics: a unified workflow | Factor House](https://factorhouse.io/articles/rapid-kafka-diagnostics-a-unified-workflow-for-root-cause-analysis/): Fragmented tools force engineers to manually piece together logs and metrics when troubleshooting Kafka. This guide shows how Kpow's unified workflow finds a stall, inspects data, and resolves it.
- [RBAC for Kafka: implementation and considerations | Factor House](https://factorhouse.io/articles/rbac-for-kafka/): Learn how to implement Kafka RBAC with practical steps, real-world configuration insights from a hands-on lab, and a clear comparison of RBAC vs ACLs at scale
- [Melbourne Kafka x Flink meetup recap: July 2025 | Factor House](https://factorhouse.io/articles/real-time-data-to-insights-meetup-july25/): From structuring data streams to spinning up full pipelines locally, our Kafka x Flink meetup in Melbourne was packed with hands-on demos and real-time insights. Catch the highlights and what's next.
- [How Reddit uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/reddit-kafka-architecture/): A deep-dive into Reddit's Kafka architecture, covering use cases, scale, engineering decisions and key contributors.
- [Redpanda Console: pricing and alternatives | Factor House](https://factorhouse.io/articles/redpanda-console/): Redpanda Console reviewed for 2026: features, pricing, limitations, and the best alternatives for engineering teams running Apache Kafka or Redpanda.
- [How Robinhood uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/robinhood-kafka-architecture/): A deep-dive into Robinhood's Kafka architecture: use cases, scale, and engineering decisions. Robinhood processes 2.2 million messages per second across equities, crypto, and fraud detection.
- [Run Kpow in Kubernetes with Helm | Factor House](https://factorhouse.io/articles/run-kpow-in-kubernetes-with-helm/): This article covers running Kpow in Kubernetes using the Kpow Helm Chart. Kpow is the all-in-one toolkit to manage, monitor, and learn about your Kafka resources, packaged for Helm deployment.
- [How Salesforce uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/salesforce-kafka-architecture/): A deep-dive into Salesforce's Kafka architecture, covering use cases, scale, engineering decisions and key contributors across a fleet of 100+ clusters processing 3+ trillion events per day.
- [Self-service Kafka governance with ServiceNow | Factor House](https://factorhouse.io/articles/self-service-kafka-governance-with-kpow-and-servicenow/): Implement Just-in-Time Kafka access by integrating Kpow with ServiceNow. Automate approvals and temporary policy management to enhance security and developer self-service.
- [Set up Kpow with Amazon MSK | Factor House](https://factorhouse.io/articles/set-up-kpow-with-aws/): Integrate Kpow with Amazon Managed Streaming for Apache Kafka in minutes. Gain unified visibility and control over your AWS brokers, MSK Connect, and Glue Schema Registry.
- [Set Up Kpow with Confluent Cloud | Factor House](https://factorhouse.io/articles/set-up-kpow-with-confluent-cloud/): Integrate Kpow with Confluent Cloud in minutes. Gain unified visibility and control over your managed Kafka brokers, Schema Registry, Managed Connect, and ksqlDB.
- [Set up Kpow with Google Cloud Managed Kafka | Factor House](https://factorhouse.io/articles/set-up-kpow-with-gcp/): Integrate Kpow with Google Cloud Managed Service for Apache Kafka in minutes. Gain unified visibility and control over your managed Kafka brokers and Schema Registry.
- [Set Up Kpow with NetApp Instaclustr Platform | Factor House](https://factorhouse.io/articles/set-up-kpow-with-instaclustr/): Integrate Kpow with Instaclustr in minutes. Gain unified visibility and control over your managed Kafka brokers, Karapace Schema Registry, and Kafka Connect through our toolkit.
- [How Shopify uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/shopify-kafka-architecture/): A deep-dive into Shopify's Kafka architecture, covering CDC at 100,000 records/sec, Kubernetes deployment, the Sarama Go client library, and BFCM scale engineering.
- [How Spotify used Apache Kafka in production | Factor House](https://factorhouse.io/articles/spotify-kafka-architecture/): A deep-dive into Spotify's Kafka architecture, covering their event delivery system, 700K events/second scale, engineering decisions, and why they ultimately migrated to Google Cloud Pub/Sub.
- [How Tencent uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/tencent-kafka-architecture/): A deep-dive into Tencent's Kafka architecture, covering their federated cluster design, 20 trillion messages per day, KIP contributions, and tiered storage at Tencent Cloud.
- [How The New York Times uses Kafka | Factor House](https://factorhouse.io/articles/the-new-york-times-kafka-architecture/): A deep-dive into The New York Times' Kafka publishing pipeline, covering the Monolog architecture, single-partition design, Kafka Streams usage, and treating Kafka as a permanent content store.
- [Top Kafka UI tools in 2026: a practical comparison | Factor House](https://factorhouse.io/articles/top-kafka-ui-tools-in-2026-a-practical-comparison-for-engineering-teams/): Honest comparison of Kafka UI tools for enterprise teams. We evaluate AKHQ, Kafbat, Redpanda Console, Conduktor, Confluent Control Center, and Kpow.
- [Triage, repair, and replay: Kafka remediation | Factor House](https://factorhouse.io/articles/triage-repair-and-replay-integrated-kafka-remediation-workflows/): Fix broken Kafka data pipelines fast. Learn how Kpow replaces messy CLI scripts with an intuitive UI to isolate, repair, and re-inject data.
- [How Uber uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/uber-kafka-architecture/): A deep-dive into Uber's Kafka architecture - covering use cases, scale, engineering decisions, and key contributors. From one region to trillions of messages a day.
- [Unified community license for Kpow and Flex | Factor House](https://factorhouse.io/articles/unified-community-license/): The unified Factor House Community License works with Kpow and Flex Community Edition, unlocking both products with one license, making it simpler to explore, prototype, and evaluate our products.
- [Updates to container specifics: DockerHub and Helm | Factor House](https://factorhouse.io/articles/updates-to-container-specifics/): Discover how our 94.1 release has streamlined DockerHub, Helm Charts, and AWS Marketplace deployments!
- [How Walmart uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/walmart-kafka-architecture/): A deep-dive into Walmart's Kafka architecture, covering real-time inventory, fraud detection, the Customer Data Platform, and the Messaging Proxy Service handling trillions of messages per day.
- [Web Accessibility at Factor House](https://factorhouse.io/articles/web-accessibility-at-factor-house/): How do skateboards and green suns drive web accessibility at Factor House? Learn why accessible products matter to us, and how we've embedded accessibility into our development process.
- [What is Apache Kafka? | Factor House](https://factorhouse.io/articles/what-is-kafka/): Apache Kafka is an open-source distributed event streaming platform that stores records in ordered, partitioned, replayable logs. This is what it is, how the pieces fit, and when to choose it.
- [What the IBM-Confluent deal means for Kafka users | Factor House](https://factorhouse.io/articles/what-the-ibm-confluent-acquisition-means-for-kafka-users/): IBM's $11B Confluent acquisition raises questions for Kafka users. Assess your lock-in risk across Schema Registry, managed connectors, and operational tooling.
- [How Wix uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/wix-kafka-architecture/): A deep-dive into Wix's Kafka architecture: 66 billion daily messages, 2,200+ microservices, the Greyhound SDK, Confluent Cloud migration, and operating 500,000+ partitions across 4 regions.
- [Chad Harris, Solutions Architect | Factor House](https://factorhouse.io/authors/chad-harris/): Chad Harris is a Solutions Architect at Factor House who has worked with Apache Kafka since 2012 and previously led engineering on PCI-compliant payments systems at Square (Block).
- [Derek Troy-West, Co-founder & CEO | Factor House](https://factorhouse.io/authors/derek-troy-west/): Derek Troy-West is Co-founder and CEO of Factor House, the company behind Kpow, Flex, and the Factor Platform for Apache Kafka and Flink.
- [Factor House, Company updates | Factor House](https://factorhouse.io/authors/factor-house/): Factor House builds tooling for real-time data: Kpow for Apache Kafka, Flex for Apache Flink, and Iglu for Apache Iceberg.
- [Gaurav Bhatt, Solution Engineer at Sportsbet | Factor House](https://factorhouse.io/authors/gaurav-bhatt/): Gaurav Bhatt is a Solution Engineer at Sportsbet with close to two decades of experience in enterprise Java development and data engineering.
- [Jaehyeon Kim, Former Developer Experience Engineer | Factor House](https://factorhouse.io/authors/jaehyeon-kim/): Jaehyeon worked at the intersection of real-time data engineering and developer education, focused on making Apache Kafka and Apache Flink approachable for platform and data engineering teams.
- [Karel Sague, Staff Software Engineer | Factor House](https://factorhouse.io/authors/karel-sague/): Karel Sague is a Staff Software Engineer at Factor House, working on real-time data streaming across Apache Kafka, Apache Flink and Apache Iceberg, after eight years as an engineer at Square (Block).
- [Kylie Troy-West, Co-founder & COO | Factor House](https://factorhouse.io/authors/kylie-troy-west/): Kylie Troy-West is Co-founder and COO of Factor House, leading go-to-market strategy, operations, and business growth.
- [Lutz Hühnken, Contributor | Factor House](https://factorhouse.io/authors/lutz-huhnken/): Factor House builds tooling for real-time data: Kpow for Apache Kafka, Flex for Apache Flink, and Iglu for Apache Iceberg.
- [Moslem Chalfouh, Tech Lead and Software Architect | Factor House](https://factorhouse.io/authors/moslem-chalfouh/): Moslem Chalfouh is a Tech Lead and Software Architect with 15+ years building distributed systems in Java, Kafka, and AWS for finance and insurance.
- [Nicolas Venegas, Contributor | Factor House](https://factorhouse.io/authors/nvenegas/): Factor House builds tooling for real-time data: Kpow for Apache Kafka, Flex for Apache Flink, and Iglu for Apache Iceberg.
- [Sarah Brown, Contributor | Factor House](https://factorhouse.io/authors/sarah-brown/): Factor House builds tooling for real-time data: Kpow for Apache Kafka, Flex for Apache Flink, and Iglu for Apache Iceberg.
- [Thomas Crowley, Contributor | Factor House](https://factorhouse.io/authors/thomas-crowley/): Factor House builds tooling for real-time data: Kpow for Apache Kafka, Flex for Apache Flink, and Iglu for Apache Iceberg.
- [Tim Mapperson, Contributor | Factor House](https://factorhouse.io/authors/tim-mapperson/): Factor House builds tooling for real-time data: Kpow for Apache Kafka, Flex for Apache Flink, and Iglu for Apache Iceberg.
- [Kafka & Flink blog - Page 10 | Factor House](https://factorhouse.io/blog/10/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 11 | Factor House](https://factorhouse.io/blog/11/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 12 | Factor House](https://factorhouse.io/blog/12/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 13 | Factor House](https://factorhouse.io/blog/13/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 14 | Factor House](https://factorhouse.io/blog/14/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 15 | Factor House](https://factorhouse.io/blog/15/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 16 | Factor House](https://factorhouse.io/blog/16/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 17 | Factor House](https://factorhouse.io/blog/17/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 18 | Factor House](https://factorhouse.io/blog/18/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 19 | Factor House](https://factorhouse.io/blog/19/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 2 | Factor House](https://factorhouse.io/blog/2/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 20 | Factor House](https://factorhouse.io/blog/20/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 21 | Factor House](https://factorhouse.io/blog/21/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 22 | Factor House](https://factorhouse.io/blog/22/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 23 | Factor House](https://factorhouse.io/blog/23/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 24 | Factor House](https://factorhouse.io/blog/24/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 25 | Factor House](https://factorhouse.io/blog/25/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 26 | Factor House](https://factorhouse.io/blog/26/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 27 | Factor House](https://factorhouse.io/blog/27/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 28 | Factor House](https://factorhouse.io/blog/28/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 29 | Factor House](https://factorhouse.io/blog/29/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 3 | Factor House](https://factorhouse.io/blog/3/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 30 | Factor House](https://factorhouse.io/blog/30/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 4 | Factor House](https://factorhouse.io/blog/4/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 5 | Factor House](https://factorhouse.io/blog/5/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 6 | Factor House](https://factorhouse.io/blog/6/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 7 | Factor House](https://factorhouse.io/blog/7/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 8 | Factor House](https://factorhouse.io/blog/8/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog - Page 9 | Factor House](https://factorhouse.io/blog/9/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Kafka & Flink blog | Factor House](https://factorhouse.io/blog/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Careers and open roles | Factor House](https://factorhouse.io/careers/): We're scaling fast and looking for collaborative and curious minds who want to shape the future of realtime streaming.
- [Block's one-stop shop for Kafka expertise | Factor House](https://factorhouse.io/case-studies/block/): Block operates Kafka infrastructure supporting millions of payment transactions in a regulated FinTech environment. Chad explains why Factor House is their go-to partner.
- [How Claritev cut Kafka support costs with Kpow | Factor House](https://factorhouse.io/case-studies/claritev/): A US healthcare technology company brings Kafka support in-house, saving $150,000 a year, after Kpow gave its middleware team the visibility to manage the platform with confidence.
- [How a distributor scaled Kafka to 130 developers | Factor House](https://factorhouse.io/case-studies/foodservice/): A national foodservice distributor scales Kafka observability from a handful of admins to a dozen product teams behind its e-commerce platform.
- [How Gmarket replaced Control Center with Kpow | Factor House](https://factorhouse.io/case-studies/gmarket/): South Korean e-commerce marketplace Gmarket replaces Confluent's licensed Control Center with Kpow, giving 150 users across production and development self-service Kafka operations.
- [Pepperstone's first stop for Kafka visibility | Factor House](https://factorhouse.io/case-studies/how-kpow-is-delivering-for-pepperstone/): A leading Forex and CFD broker uses Kpow as its first stop for Kafka visibility, giving engineers direct sight of topics, consumer groups, and cluster health.
- [Customer case studies | Factor House](https://factorhouse.io/case-studies/): How platform and data engineering teams use Kpow to run Apache Kafka in production.
- [How German bank NORD/LB cut debugging time by 50% | Factor House](https://factorhouse.io/case-studies/nord-lb/): A German financial institution overcomes critical compliance gaps and operational inefficiencies to build a scalable, enterprise-grade streaming data platform.
- [Pickles replaces hours of Kafka checks with Kpow | Factor House](https://factorhouse.io/case-studies/pickles-kafka-and-kpow/): Australia's leading auction marketplace replaces custom validation microservices with Kpow's Data Inspect, giving its engineering team instant visibility into real-time auction data.
- [How US Foods scaled Kafka access to 130 developers | Factor House](https://factorhouse.io/case-studies/us-foods/): US Foods scales Kafka observability from a handful of admins to a dozen product teams behind its MOXē e-commerce platform.
- [How Kpow became critical to Verrency's payments | Factor House](https://factorhouse.io/case-studies/verrency-fintech-kafka-and-kpow/): A fintech running Apache Kafka as its payments backbone turns to Kpow for visibility, then shapes the product as it becomes mission-critical infrastructure.
- [Product changelog - Page 10 | Factor House](https://factorhouse.io/changelog/10/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Product changelog - Page 2 | Factor House](https://factorhouse.io/changelog/2/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Product changelog - Page 3 | Factor House](https://factorhouse.io/changelog/3/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Product changelog - Page 4 | Factor House](https://factorhouse.io/changelog/4/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Product changelog - Page 5 | Factor House](https://factorhouse.io/changelog/5/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Product changelog - Page 6 | Factor House](https://factorhouse.io/changelog/6/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Product changelog - Page 7 | Factor House](https://factorhouse.io/changelog/7/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Product changelog - Page 8 | Factor House](https://factorhouse.io/changelog/8/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Product changelog - Page 9 | Factor House](https://factorhouse.io/changelog/9/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Product changelog | Factor House](https://factorhouse.io/changelog/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Factor House community | Factor House](https://factorhouse.io/community/): Join the Factor House community on Slack, connect with engineers working on Apache Kafka and Apache Flink, and get product updates in your inbox.
- [Contact the Factor House team | Factor House](https://factorhouse.io/contact/): Fill out the form and a Factor House team member will be in touch. For technical support, visit our Help Center.
- [End User License Agreement (EULA) | Factor House](https://factorhouse.io/eula/): The End User License Agreement that governs your use of Factor House products.
- [Apache Kafka Meetup - Melbourne - August 2026 | Factor House](https://factorhouse.io/events/apache-kafka-meetup-melbourne-august-2026/): Join us for an Apache Kafka x Apache Flink meetup in Melbourne, with talks on streaming Kafka into Apache Iceberg and on Kafka, Flink, and GenAI, followed by networking.
- [Beyond IAM: multi-team Amazon MSK (Europe) | Factor House](https://factorhouse.io/events/beyond-iam-multi-team-msk-europe-october-2026/): In the Europe session, Chad Harris of Factor House looks at what IAM alone doesn't cover on a shared Amazon MSK cluster, and the observability and governance patterns that fill the gap.
- [Beyond IAM: governance for multi-team Amazon MSK | Factor House](https://factorhouse.io/events/beyond-iam-multi-team-msk-october-2026/): In the Americas session, Chad Harris of Factor House looks at what IAM alone doesn't cover on a shared Amazon MSK cluster, and the observability and governance patterns that fill the gap.
- [Events and webinars | Factor House](https://factorhouse.io/events/): Webinars, conferences, and meetups where you can learn how platform teams run real-time data streaming in production.
- [Reduce Kafka spend: cluster consolidation (APAC) | Factor House](https://factorhouse.io/events/kafka-cluster-consolidation-apac-august-2026/): Karel Sague, Staff Software Engineer at Factor House, walks through a practical methodology for cutting Kafka costs by consolidating, splitting, and migrating clusters without downtime or data loss.
- [Kafka into Apache Iceberg: an open lakehouse | Factor House](https://factorhouse.io/events/kafka-to-iceberg-lakehouse-amer-august-2026/): Karel Sague, Staff Software Engineer at Factor House, shares real-world lessons streaming Kafka into Apache Iceberg with Kafka Connect, and the schema and partitioning challenges involved.
- [Kafka User Group - Americas (July 2026) | Factor House](https://factorhouse.io/events/kafka-user-group-americas-july-2026/): A free online session for Kafka practitioners on July 29. Sessions open with a lightning talk from a community member, then group discussion: share what you're building and connect with peers.
- [Kafka User Group - Americas (October 2026) | Factor House](https://factorhouse.io/events/kafka-user-group-americas-october-2026/): A free online session for Kafka practitioners on October 28. Sessions open with a lightning talk from a community member, then group discussion: share what you're building and connect with peers.
- [Kafka User Group - Asia Pacific (July 2026) | Factor House](https://factorhouse.io/events/kafka-user-group-asia-pacific-july-2026/): A free online session for Kafka practitioners in Asia Pacific on July 29. Sessions open with a lightning talk, then group discussion to share what you're building and connect with peers.
- [Kafka User Group - Asia Pacific (October 2026) | Factor House](https://factorhouse.io/events/kafka-user-group-asia-pacific-october-2026/): A free online session for Kafka practitioners in Asia Pacific on October 28. Sessions open with a lightning talk, then group discussion to share what you're building and connect with peers.
- [Kafka User Group - Europe (December 2026) | Factor House](https://factorhouse.io/events/kafka-user-group-europe-december-2026/): A free online session for Kafka practitioners across Europe on December 9. Sessions open with a lightning talk from a community member, then group discussion: share what you are building.
- [Kafka User Group - Europe (September 2026) | Factor House](https://factorhouse.io/events/kafka-user-group-europe-sep-2026/): A free online session for Kafka practitioners across Europe. Each session opens with a lightning talk from a community member, followed by open group discussion.
- [Migrating to open source Kafka (APAC) | Factor House](https://factorhouse.io/events/migrating-to-open-source-kafka-apac-september-2026/): In the APAC session, Chad Harris (Factor House) and Justin George (NetApp Instaclustr) walk through moving from Confluent or Amazon MSK to open source Kafka, and how to avoid the operational burden.
- [Migrating to open source Kafka (Europe) | Factor House](https://factorhouse.io/events/migrating-to-open-source-kafka-europe-october-2026/): In the Europe session, Chad Harris (Factor House) and Justin George (NetApp Instaclustr) walk through moving from Confluent or Amazon MSK to open source Kafka, and how to avoid the operational burden.
- [New at Factor House: September 2026 (Americas) | Factor House](https://factorhouse.io/events/new-at-factor-house-september-2026-americas/): See what's new in Factor House's products this month in the Americas session: live demos of recent capabilities, the thinking behind them, and what's coming next, with time to ask your own questions.
- [New at Factor House: September 2026 (Europe) | Factor House](https://factorhouse.io/events/new-at-factor-house-september-2026-europe/): See what's new in Factor House's products this month in the Europe session: live demos of recent capabilities, the thinking behind them, and what's coming next, with time to ask your own questions.
- [Things that go bump in the night: Kafka ops | Factor House](https://factorhouse.io/events/things-that-go-bump-in-the-night-webinar/): Chad Harris, Solutions Architect at Factor House, walks through real-world Kafka operational failures and the debugging workflows that actually help when the system stops behaving the way you expect.
- [Help center and support | Factor House](https://factorhouse.io/help-center/): Get the answers, documentation, and support you need to make the most of Factor House tools.
- [Factor House | Kafka & Flink UI | Manage, Monitor & Govern](https://factorhouse.io/): Manage Kafka and Flink clusters, search across topics in seconds, monitor consumer lag by partition, and enforce governance across every environment.
- [Install Kpow Enterprise | Factor House](https://factorhouse.io/install/): Get a free trial license key for Kpow Enterprise, install via Docker, JAR, Helm, Kubernetes, AWS Marketplace, or CloudFormation, then set up the Factor House CLI and Agent Skills.
- [Factor House opens office in Munich, Germany | Factor House](https://factorhouse.io/press/factor-house-expands-to-europe/): Factor House enters the European market with its first local hire in Munich, as European revenue grows 178% year over year.
- [Press and media coverage | Factor House](https://factorhouse.io/press/): Press releases and news coverage of Factor House, maker of Kpow, Flex, and the Factor Platform for Apache Kafka.
- [Product and plan pricing | Factor House](https://factorhouse.io/pricing/): Compare pricing for Kpow, Flex, and Factor Platform. Every product starts free, with Enterprise plans for governance, scale, and support in production.
- [Privacy policy details | Factor House](https://factorhouse.io/privacy/): How Factor House collects, uses, shares and protects your information across our websites and products.
- [Factor Platform | Factor House](https://factorhouse.io/products/factor-platform/): Factor Platform is the unified control plane for Kafka, Flink, and Iceberg. One interface, one permission model, one audit log. No proxy, and no changes to how your applications connect.
- [Flex for Apache Flink | Factor House](https://factorhouse.io/products/flex/): Flex puts you in command of Apache Flink, delivering instant visibility, governed control, and enterprise-grade security so you can run streaming jobs with confidence, precision, and speed.
- [Iglu changelog and releases | Factor House](https://factorhouse.io/products/iglu/changelog/): Release notes for every Iglu version, covering new features, fixes, and improvements to the schema registry UI.
- [Iglu for Apache Iceberg | Factor House](https://factorhouse.io/products/iglu/): Iglu gives your team a single interface to inspect tables, track snapshots, and manage maintenance across every Iceberg catalog, without stitching together query engines and CLI tools.
- [Kpow changelog and releases - Page 2 | Factor House](https://factorhouse.io/products/kpow/changelog/2/): Release notes for every Kpow version, covering new features, fixes, and improvements to the Kafka toolkit.
- [Kpow changelog and releases - Page 3 | Factor House](https://factorhouse.io/products/kpow/changelog/3/): Release notes for every Kpow version, covering new features, fixes, and improvements to the Kafka toolkit.
- [Kpow changelog and releases - Page 4 | Factor House](https://factorhouse.io/products/kpow/changelog/4/): Release notes for every Kpow version, covering new features, fixes, and improvements to the Kafka toolkit.
- [Kpow changelog and releases - Page 5 | Factor House](https://factorhouse.io/products/kpow/changelog/5/): Release notes for every Kpow version, covering new features, fixes, and improvements to the Kafka toolkit.
- [Kpow changelog and releases - Page 6 | Factor House](https://factorhouse.io/products/kpow/changelog/6/): Release notes for every Kpow version, covering new features, fixes, and improvements to the Kafka toolkit.
- [Kpow changelog and releases - Page 7 | Factor House](https://factorhouse.io/products/kpow/changelog/7/): Release notes for every Kpow version, covering new features, fixes, and improvements to the Kafka toolkit.
- [Kpow changelog and releases - Page 8 | Factor House](https://factorhouse.io/products/kpow/changelog/8/): Release notes for every Kpow version, covering new features, fixes, and improvements to the Kafka toolkit.
- [Kpow changelog and releases | Factor House](https://factorhouse.io/products/kpow/changelog/): Release notes for every Kpow version, covering new features, fixes, and improvements to the Kafka toolkit.
- [Kpow Community Edition: free Kafka UI | Factor House](https://factorhouse.io/products/kpow/community-edition/): Kpow Community Edition is a free Kafka UI for topics, consumer groups, schema registry, and message browsing. No cost, no credit card, single Docker container.
- [Kpow features overview | Factor House](https://factorhouse.io/products/kpow/features/): Compare every Kpow Community Edition and Enterprise feature side by side, from Kafka compatibility and topic search to governance, SSO, and support.
- [Kpow | Apache Kafka UI and GUI Tool | Factor House](https://factorhouse.io/products/kpow/): Kpow is the enterprise UI for Apache Kafka. Search, inspect, manage, and govern your production clusters in one place, with no extra infrastructure to maintain.
- [Kafka Consumer Lag & Cluster Health | Kpow | Factor House](https://factorhouse.io/products/kpow/kafka-consumer-lag/): Track Kafka consumer lag and cluster health in real time, with automated risk checks across brokers and topics. Start free with Kpow.
- [Kafka Message Search & Data Inspect | Kpow | Factor House](https://factorhouse.io/products/kpow/kafka-data-inspect/): Search and filter Kafka messages with kJQ filters, AI-powered queries, and automatic Avro/Protobuf/JSON deserialization. No consumer required. Start free with Kpow.
- [Kafka Multi-Cluster Management | Kpow | Factor House](https://factorhouse.io/products/kpow/kafka-multi-cluster/): Manage Kafka clusters across MSK, Confluent, Redpanda, and self-managed distributions from one Kpow instance, with no vendor lock-in. Start free.
- [Kafka Multi-Tenancy, SSO & Audit Log | Kpow | Factor House](https://factorhouse.io/products/kpow/kafka-multi-tenancy/): Role-based access control, multi-tenancy, data masking, SSO, and a complete audit log for every Kafka cluster you run. Start free with Kpow.
- [Kpow pricing plans | Factor House](https://factorhouse.io/products/kpow/pricing/): Kpow is priced per cluster, not per seat. Start free on up to 3 clusters with Community Edition, or upgrade to Enterprise for governance and support.
- [Kpow, Flex, and Iglu releases - Page 2 | Factor House](https://factorhouse.io/releases/2/): Release notes and downloads for Kpow, Flex, Iglu, and Factor Platform, covering every version we ship.
- [Kpow, Flex, and Iglu releases - Page 3 | Factor House](https://factorhouse.io/releases/3/): Release notes and downloads for Kpow, Flex, Iglu, and Factor Platform, covering every version we ship.
- [Release 35: OKTA single sign-on authentication | Factor House](https://factorhouse.io/releases/35/): Kpow now supports OKTA authentication for single sign-on, simplifying login and access management for teams.
- [Release 36: Improved Error Reporting | Factor House](https://factorhouse.io/releases/36/): Kpow 36 improves UI performance and error reporting, making issues easier to diagnose and resolve quickly.
- [Release 37: Quick Search for Produced Messages | Factor House](https://factorhouse.io/releases/37/): Kpow now links produced message output to Search by Key, so you can quickly search for produced messages in a single click.
- [Release 38: Schema Registry Integration | Factor House](https://factorhouse.io/releases/38/): Kpow integrates with your Schema Registry, providing dashboards and allowing you to edit, update, delete, diff, and validate Schemas and Subjects
- [Release 39: OpenID SSO Including Okta and Github | Factor House](https://factorhouse.io/releases/39/): Kpow integrates with OpenID SSO providers including Github SSO and Okta
- [Kpow, Flex, and Iglu releases - Page 4 | Factor House](https://factorhouse.io/releases/4/): Release notes and downloads for Kpow, Flex, Iglu, and Factor Platform, covering every version we ship.
- [Release 40: Kafka Dashboard Refresh | Factor House](https://factorhouse.io/releases/40/): Kpow provides a suite of new and updated Kafka dashboards, giving you insights into brokers, topics, groups, members, assignments, offsets and much more
- [Release 41: Broker and Topic Configuration | Factor House](https://factorhouse.io/releases/41/): Kpow provides you the ability to view, edit, and understand Kafka topic and broker configuration
- [Release 42: Multi-Cluster Monitoring | Factor House](https://factorhouse.io/releases/42/): Kpow now allows you to manage multiple Kafka clusters from a single installation
- [Release 43: Kafka Tool Improvements | Factor House](https://factorhouse.io/releases/43/): This is a performance and feature release to improve the ability of Kpow to snapshot resources.
- [Release 44: Prometheus Integration | Factor House](https://factorhouse.io/releases/44/): Kpow provides group state heatmaps and insights, with all metrics made available to Prometheus for egre
- [Release 45: Kafka Connect Integration | Factor House](https://factorhouse.io/releases/45/): Kpow provides the ability to manage and monitor Kafka Connectors, including configuration, task control, and Prometheus metrics.
- [Release 46: Kafka Dashboard UI / UX | Factor House](https://factorhouse.io/releases/46/): This maintenance release improves the overall UI / UX of Kpow considerably.
- [Release 47: RBAC, SAML SSO, and HTTPS | Factor House](https://factorhouse.io/releases/47/): Kpow support user authz capabilities including Role Based Access Control (RBAC), SAML SSO including Azure AD and AWS SSO, and the ability to easily configure HTTPS out-of-the-box
- [Release 48: Realtime Kafka Monitoring | Factor House](https://factorhouse.io/releases/48/): Kpow supports realtime monitoring of Apache Kafka with Live Mode, improving consumer group lag visualisations.
- [Release 49: JS Optimization Bugfix | Factor House](https://factorhouse.io/releases/49/): A minor bugfix release resolving an issue with the datepicker in Topic Inspect
- [Kpow, Flex, and Iglu releases - Page 5 | Factor House](https://factorhouse.io/releases/5/): Release notes and downloads for Kpow, Flex, Iglu, and Factor Platform, covering every version we ship.
- [Release 50: Data masking and Slack integration | Factor House](https://factorhouse.io/releases/50/): Kpow provides masking and redaction of Topic Inspect results via configurable Data Policies, and integrates the Audit Log with Slack so you can monitor user actions on Kafka resources.
- [Release 51: Topic Inspect with KJQ | Factor House](https://factorhouse.io/releases/51/): Kpow provides support for executing JQ-like queries on Kafka topics with KJQ.
- [Release 52: Topic Inspect Serdes Warnings | Factor House](https://factorhouse.io/releases/52/): Kpow provides inline reporting of serialisation and deserialisation errors in the results of Topic Inspect queries.
- [Release 53: Websocket Concurrency | Factor House](https://factorhouse.io/releases/53/): Kpow improves support for multiple concurrent users executing complex queries
- [Release 54: Custom Serdes for KJQ | Factor House](https://factorhouse.io/releases/54/): Kpow supports custom serdes for KJQ, allowing you to search protobuf data on Kafka topics with JQ queries.
- [Release 55: RBAC Improvements | Factor House](https://factorhouse.io/releases/55/): Kpow supports RBAC configurable role field and authenticated roles restrictions
- [Release 57: Kafka Connect Resilience | Factor House](https://factorhouse.io/releases/57/): Release 57 improves Kpow's resilience when integrating with Kafka Connect, reducing errors during connector state changes.
- [Release 58: SAML with Reverse Proxy | Factor House](https://factorhouse.io/releases/58/): Kpow supports SAML SSO when installed behind an HTTPS terminating Reverse Proxy
- [Release 59: SAML Session Duration | Factor House](https://factorhouse.io/releases/59/): Kpow provides a new environment variable to control the SAML session duration
- [Kpow, Flex, and Iglu releases - Page 6 | Factor House](https://factorhouse.io/releases/6/): Release notes and downloads for Kpow, Flex, Iglu, and Factor Platform, covering every version we ship.
- [Release 60: Kpow, LDAP, JAAS, and Compute | Factor House](https://factorhouse.io/releases/60/): Kpow, Compute Console, and Jetty User Authentication (LDAP, File, and DB) all come in v60 of our engineering toolkit for Apache Kafka®
- [Release 61: Connect Permissive SSL | Factor House](https://factorhouse.io/releases/61/): Kpow provides a new environment variable to allow SSL connections to Kafka Connect where the SSL certificate is deliberately not validated.
- [Release 62: Compute Label Correction | Factor House](https://factorhouse.io/releases/62/): Kpow corrects a labelling off-by-one error with horizontally displayed topologies.
- [Release 63: Header kJQ and UI Snappiness | Factor House](https://factorhouse.io/releases/63/): Kpow introduces the ability to apply kJQ queries to message headers, allowing you to search tens of thousands of messages a second and match by header.
- [Release 64: Dependency Improvements | Factor House](https://factorhouse.io/releases/64/): This release brings all dependencies to current, ameliorating CVEs raised by our automated vulnerability scanning.
- [Release 65: Klang in the kREPL | Factor House](https://factorhouse.io/releases/65/): Discover the kREPL, an interactive development environment for Apache Kafka providing all the tooling to consume topics and explore structured data with Klang, a language blending JQ and Clojure.
- [Release 66: UI Stability | Factor House](https://factorhouse.io/releases/66/): Kpow 66 resolves an issue that occasionally impacted compute and consumer group views in the UI.
- [Release 67: Memory Leak Fix | Factor House](https://factorhouse.io/releases/67/): Resolves a memory issue by reverting to a slightly older version of Jetty.
- [Kpow, Flex, and Iglu releases - Page 7 | Factor House](https://factorhouse.io/releases/7/): Release notes and downloads for Kpow, Flex, Iglu, and Factor Platform, covering every version we ship.
- [Release 70: Azure Event Hubs and Confluent Cloud | Factor House](https://factorhouse.io/releases/70/): Version 70 of Kpow brings support for Azure Event Hubs, enables Multi-Tenant Confluent Cloud, introduces a new function to Klang, open sources our continuous delivery pipeline, and more..
- [Release 72: H/A Resource Management | Factor House](https://factorhouse.io/releases/72/): Version 72 of Kpow includes greater support for high availability Kafka resource management, new liveness probes, and greater support for reverse-proxying:
- [Release 73: SSL PEM Format Support | Factor House](https://factorhouse.io/releases/73/): Version 73 provides support for PEM format SSL certificates introduced in Kafka 2.7.0 and described in KIP-651.
- [Release 74: Kafka ACL Management and JSON Schema | Factor House](https://factorhouse.io/releases/74/): Kpow v74 comes packed with features including full support for Kafka ACL Management, JSON Schema support, and a raft of UI and performance improvements.
- [Release 75: Protobuf Schema and Serdes | Factor House](https://factorhouse.io/releases/75/): Version 75 provides support for Protobuf schema in the Schema Registry UI, and allows consumption and inspection of Protobuf in Data Inspect.
- [Release 76: New Kafka ACL Management Features | Factor House](https://factorhouse.io/releases/76/): Kpow v76 provides new Kafka ACL Management features and improves UI initialisation.
- [Release 77: Reverse proxy X-Forwarded-For support | Factor House](https://factorhouse.io/releases/77/): This minor release provides improved support for Jetty User Authentication (PropertyFile, JDBC, LDAP) when operating Kpow behind a reverse-proxy.
- [Release 78: Multi-Topic Search and UI Refresh | Factor House](https://factorhouse.io/releases/78/): Kpow v78 provides a major overhaul of our user interface and the brand new ability to search multiple topics at the same time in Data Inspect.
- [Release 79: Kpow Admin and staged mutations | Factor House](https://factorhouse.io/releases/79/): Kpow v79 introduces Kpow Admin roles with the ability to Stage Mutations and create Temporary RBAC Policies, all wrapped up in a new Settings UI.
- [Kpow, Flex, and Iglu releases - Page 8 | Factor House](https://factorhouse.io/releases/8/): Release notes and downloads for Kpow, Flex, Iglu, and Factor Platform, covering every version we ship.
- [Release 80: Kafka Streams UI, freshness metrics | Factor House](https://factorhouse.io/releases/80/): Kpow v80 provides a brand new Kafka Streams UI with the GA release of the open-source Kpow Streams Agent.
- [Release 81: Multi-tenancy and streaming search | Factor House](https://factorhouse.io/releases/81/): Kpow v81 is a major release containing Multi-Tenancy, Streaming Search, Confluent Metrics, and more.
- [Release 82: Multi-Tenancy Memory | Factor House](https://factorhouse.io/releases/82/): A quick UI update to skip user session immediately to previously selected tenant, where possible.
- [Release 83: Bulk Import, Multi-Produce, kJQ Search | Factor House](https://factorhouse.io/releases/83/): Kpow v83 features bulk message import, multi-message produce, improved kJQ filters, Github Teams RBAC integration, MSK IAM authentication, and more.
- [Release 84: AWS Glue Schema Registry | Factor House](https://factorhouse.io/releases/84/): Kpow v84 features support for AWS Glue Schema Registry, AWS IAM MSK authentication, and improved Data Produce UX.
- [Release 85: Data Masking Improvements | Factor House](https://factorhouse.io/releases/85/): Kpow v85 features improved Data Masking support and a new ConsumerOffsets serde in Data Inspect.
- [Release 86: Compute Performance and AWS Glue UX | Factor House](https://factorhouse.io/releases/86/): Kpow v86 features improved Kpow snapshot compute performance and an updated Schema UI that shows AWS Glue schema status.
- [Release 87: AVRO Decimal Logical Type Support | Factor House](https://factorhouse.io/releases/87/): Kpow v87 features support for AVRO Decimal Logical Type fields in both Data Inspect and Data Produce, improved Data Import UX for CSV message upload, and support for RHOSAK.
- [Release 88: Message headers and protobuf support | Factor House](https://factorhouse.io/releases/88/): Kpow v88 features support for producing with message headers, protobuf referenced schemas, a new topic partition increase function, and much more.
- [Release 88.1: A New Release Model | Factor House](https://factorhouse.io/releases/88-1/): Kpow v88.1 is the first release to follow our new Major.Minor release model.
- [Release 88.2: Flink consumers, encrypted config | Factor House](https://factorhouse.io/releases/88-2/): Kpow v88.2 improves support for monitoring Flink consumers, adds encrypted Kpow configuration to avoid plaintext passwords, and supports mutual TLS for Confluent Schema Registry.
- [Release 88.4: Security, performance, resizable UI | Factor House](https://factorhouse.io/releases/88-4/): Kpow v88.4 brings nice new UI features including resizable columns and easy JSON export of tabular data. Performance and security improvements are also included.
- [Release 88.5: Confluent Cloud Metrics API | Factor House](https://factorhouse.io/releases/88-5/): Kpow v88.5 brings improved integration with the Confluent Cloud Metrics API for large cloud clusters, and a number of minor improvements and bug fixes.
- [Release 88.6: AWS Glue Protobuf Support | Factor House](https://factorhouse.io/releases/88-6/): Kpow v88.6 brings Protobuf support for AWS Glue, configurable JSON logs, improved orphan schema detection, a JDK17 docker image, and better Confluent integration.
- [Release 88.7: Kafka Idempotent Producer Bug | Factor House](https://factorhouse.io/releases/88-7/): Kpow v88.7 resolves a minor consumer issue introduced by the Kafka 3.2.0 client library upgrade.
- [Release 89.1: Search Speed and Managed Connect | Factor House](https://factorhouse.io/releases/89-1/): Kpow v89.1 is a major feature release with up to 20x faster Data Inspect, support for Confluent Managed Connect, MSK Managed Connect, and MSK Serverless.
- [Release 89.2: Data Inspect and SAML Auth fixes | Factor House](https://factorhouse.io/releases/89-2/): Kpow v89.2 resolves minor issues with XML messages in Data Inspect and relay state when re-authenticating SAML sessions.
- [Release 89.3: Optional Log Cleaner Check Disable | Factor House](https://factorhouse.io/releases/89-3/): Kpow v89.3 introduces the ability to disable the log.cleaner.enable=true broker configuration check that Kpow requires for default installations
- [Release 89.4: Connect Inspect RBAC Permission | Factor House](https://factorhouse.io/releases/89-4/): Kpow v98.4 introduces a new CONNECT_INSPECT RBAC permission and fixes a number of minor UI bugs.
- [Release 90.1: New UI, features and Kpow Community | Factor House](https://factorhouse.io/releases/90-1/): Kpow v90.1 is packed with new Connect and Data Inspect features, provides a refreshed UI, and introduces Kpow Community Edition (CE).
- [Release 90.2: Improved OpenID Connect SSO support | Factor House](https://factorhouse.io/releases/90-2/): Kpow v90.2 is a minor release featuring improved OpenID Connect SSO support.
- [Release 90.3: OpenID and tenancy performance | Factor House](https://factorhouse.io/releases/90-3/): Kpow v90.3 features improved OpenID configuration and tenancy materialization performance improvements
- [Release 90.4: Bulk actions and performance tuning | Factor House](https://factorhouse.io/releases/90-4/): Kpow v90.4 introduces a new Bulk Actions feature that allows you to take action on multiple resources in one click.
- [Release 90.5: Java 8 Build Fix | Factor House](https://factorhouse.io/releases/90-5/): Kpow v90.5 fixes a regression in the 90.4 Java 8 build, restoring compatibility with Java 8 deployments.
- [Release 90.6: Dark Mode | Factor House](https://factorhouse.io/releases/90-6/): Kpow v90.6 introduces our new Dark Mode UI, improved intellisense, and confiugrable persistence settings.
- [Release 91.1: ksqlDB UI and Broker Disk Telemetry | Factor House](https://factorhouse.io/releases/91-1/): Kpow v91.1 introduces a new ksqlDB UI, new disk usage telemetry, and wildcard filtering.
- [Release 91.2: Resume Connector Bulk Action | Factor House](https://factorhouse.io/releases/91-2/): Kpow v91.2 is a minor release that introduces the new 'Resume Connector' bulk action
- [Release 91.3: Schema Configurability | Factor House](https://factorhouse.io/releases/91-3/): Kpow v91.3 is a minor release that introduces new Schema and Connect features and resolves a number of small issues.
- [Release 91.4: Graviton Support | Factor House](https://factorhouse.io/releases/91-4/): Kpow v91.4 introduces ARM64 docker builds to support Graviton deployments and includes a range of general feature improvements.
- [Release 91.5: Expand and Explore | Factor House](https://factorhouse.io/releases/91-5/): Kpow v91.5 brings improved UX for expanding and exploring tabular data, provides support for importing messages with headers for production, and fixes a few minor bugs.
- [Release 92.1: Multi-topic create, time-since URP | Factor House](https://factorhouse.io/releases/92-1/): Kpow v92.1 closes a large number of support tickets, introduces new features like multi-topic create and time-since URP, and improves the efficieny of Kafka Connect and Schema Registry observation.
- [Release 92.2: Quotas, producers, and KRaft | Factor House](https://factorhouse.io/releases/92-2/): Release v92.2 introduces new UI for Kafka Quotas, Transactional Producers, and KRaft clusters, plus topic and consumer management features and Flink checkpoint and watermark improvements in Flex.
- [Release 92.3: Accessibility | Factor House](https://factorhouse.io/releases/92-3/): Release v92.3 introduces extensive UI accessibility improvements to Kpow and Flex along with new features, improvements, and bug fixes.
- [Release 92.4: Kpow WCAG 2.1 AA compliance | Factor House](https://factorhouse.io/releases/92-4/): Kpow for Apache Kafka is now WCAG 2.1 AA compliant, with an independently audited Voluntary Product Accessibility Template (VPAT) report confirming the certification.
- [Release 93.1: Kpow OpenAPI 3.1 Kafka API | Factor House](https://factorhouse.io/releases/93-1/): Kpow now offers a secure, vendor-agnostic OpenAPI 3.1 REST API for managing Kafka, Kafka Connect, and Schema Registry resources, ready to integrate with your product or GitOps pipeline.
- [Release 93.2: Introducing Connector Auto-Restart | Factor House](https://factorhouse.io/releases/93-2/): Kpow can now auto restart connectors when they fail. Read on to learn how to make connectors more reliable.
- [Release 93.3: Confluent Schema References | Factor House](https://factorhouse.io/releases/93-3/): Kpow now fully supports schema references in Confluent Schema Registry: create and edit AVRO, JSON Schema, and Protobuf schema with references, and produce or consume messages using them.
- [Release 93.4: Protobuf, Light Mode, and Community | Factor House](https://factorhouse.io/releases/93-4/): This minor release from Factor House improves protobuf rendering, sharpens light mode, simplifies Community Edition setup, fixes small bugs, and bumps Kafka clients to v3.7.0.
- [Release 94.1: Streams agent, offsets, Helm charts | Factor House](https://factorhouse.io/releases/94-1/): This major version release from Factor House improves consumer offset management, Kafka Streams telemetry, extra data inspect capabilities and new Helm Charts!
- [Release 94.2: Google MSK, Data Inspect, Docker A+ | Factor House](https://factorhouse.io/releases/94-2/): This minor release from Factor House adds support for GCP MSK, new data inspect display options, AVRO Date Logical Type formatting, flat CSV export, and a consumer offset reset fix.
- [Release 94.3: BYO AI and Data Inspect updates | Factor House](https://factorhouse.io/releases/94-3/): This minor release introduces BYO AI model support, topic data inference, and Data Inspect enhancements including AI-powered kJQ filtering and AVRO date formatting improvements.
- [Release 94.4: Auto SerDes improvements | Factor House](https://factorhouse.io/releases/94-4/): This minor hotfix release from Factor House resolves a bug when using Auto SerDes without Data policies, and adds support for UTF-8 String Auto SerDes inference.
- [Release 94.5: Factor House docs and inspect fixes | Factor House](https://factorhouse.io/releases/94-5/): This release introduces Factor House Docs, a new unified documentation hub, along with major data inspection enhancements and more reliable URP detection and KRaft support improvements.
- [Release 94.6: Factor Platform RC and Ververica | Factor House](https://factorhouse.io/releases/94-6/): The first Factor Platform release candidate is here. This release also introduces Ververica Platform integration in Flex, plus Kafka Clients 4.1 / Confluent 8.0.0 support and new kJQ operators.
- [Release 95.1: Unified Factor House experience | Factor House](https://factorhouse.io/releases/95-1/): 95.1 delivers a cohesive experience across Factor House products, licensing, and brand, introducing a new license portal, refreshed branding, and a unified Community License for Kpow and Flex.
- [Release 95.2: Kpow, Flex, and Helm improvements | Factor House](https://factorhouse.io/releases/95-2/): 95.2 focuses on refinement and operability, with improvements across the UI, consumer group workflows, and deployment configuration, plus new Helm options for API and credential automounting.
- [Release 95.3: Memory leak fix for compute users | Factor House](https://factorhouse.io/releases/95-3/): 95.3 fixes a memory leak in our in-memory compute implementation, reported by our customers.
- [Release 95.4: Concurrent inspect, Strimzi support | Factor House](https://factorhouse.io/releases/95-4/): Kpow 95.4 introduces concurrent data inspect queries, Strimzi support, and improved consumer group management and monitoring.
- [Release 95.5: UI performance improvements | Factor House](https://factorhouse.io/releases/95-5/): 95.5 is a minor bugfix release focusing on UI performance and resolving some regressions introduced by Kpow 95.4.
- [Release 96.1: Factor Platform, new Kpow features | Factor House](https://factorhouse.io/releases/96-1/): Factor House release v96.1 brings significant new product features and performance enhancements to our Kafka and Flink tooling, and makes Factor Platform publicly available for early-access users.
- [Release 96.2: Schema refs, Data Inspect updates | Factor House](https://factorhouse.io/releases/96-2/): Kpow 96.2 adds an inline schema references view and extensive Data Inspect improvements.
- [Release 96.3: Kpow Signals (Alpha) preview | Factor House](https://factorhouse.io/releases/96-3/): Kpow 96.3 introduces Signals (Alpha), providing constant monitoring for clusters and early insight into critical issues, potential problems, and future degradations.
- [Release 96.4: Avro data inspect improvements | Factor House](https://factorhouse.io/releases/96-4/): Kpow 96.4 improves Avro data inspection, including key ordering for arrays of records and a fix for producing null values on optional primitive types.
- [Factor CLI 1.0: Factor CLI, Terminal UI and Agent Skills | Factor House](https://factorhouse.io/releases/fh-1-0/): fh 1.0, the Factor House command-line interface and terminal UI for Kpow: every REST endpoint as a command, a live terminal UI over the Kpow data feed, and agent skills for coding agents.
- [Kpow, Flex, and Iglu releases | Factor House](https://factorhouse.io/releases/): Release notes and downloads for Kpow, Flex, Iglu, and Factor Platform, covering every version we ship.
- [Security Policy | Factor House](https://factorhouse.io/security/): Factor House's vulnerability disclosure policy and security practices.
- [Solutions for Kafka teams | Factor House](https://factorhouse.io/solutions/): Explore Kafka and Kpow solutions by industry, cloud provider, and use case, tailored to how your team runs streaming infrastructure.
- [Kafka for Financial Services | Kpow by Factor House](https://factorhouse.io/solutions/industry/financial-services/): Data engineers at Worldpay, Block, and TD Bank use Kpow to inspect messages, enforce access controls, and prove DORA compliance across Confluent, MSK, and self-hosted clusters.
- [Solutions by industry | Factor House](https://factorhouse.io/solutions/industry/): Explore how Kpow supports Kafka teams across financial services, retail, logistics, and other industries.
- [Kafka for Logistics | Kpow by Factor House](https://factorhouse.io/solutions/industry/logistics/): Data engineers at Adidas, Mercadona, and HPE use Kpow to trace message flows, diagnose connector failures, and monitor consumer lag across Confluent, MSK, and self-hosted clusters.
- [Kafka for Retail | Kpow by Factor House](https://factorhouse.io/solutions/industry/retail/): Data engineers at Adidas, Mercadona, and Square use Kpow to trace consumer lag, enforce access controls, and inspect live message data across Confluent, MSK, and self-hosted clusters.
- [Solutions by provider | Factor House](https://factorhouse.io/solutions/provider/): Explore how Kpow works with your cloud and streaming provider, including AWS MSK, Confluent, GCP, and self-managed Kafka.
- [Solutions by use case | Factor House](https://factorhouse.io/solutions/use-case/): Explore how Kpow supports common Kafka use cases, from cluster monitoring to governance, migration, and incident response.
- [Schema quality across multi-producer Kafka topics | Factor House](https://factorhouse.io/talks/agreed-vs-validated-kafka-schema-quality-siemens/): Stefan Baer, Senior Key Expert in Data Integration at Siemens, on how an iterative, AI-assisted process uncovered silent schema drift across a multi-producer Kafka topology, and the staged fix.
- [Implementing Kafka at Belong | Factor House](https://factorhouse.io/talks/implementing-kafka-at-belong-telstra/): Nagaraj Ballapuram Gopal, Head of Enablement at Belong, part of Telstra, shares how the team implemented Kafka and the issues they ran into along the way.
- [Kafka talks and recorded sessions | Factor House](https://factorhouse.io/talks/): Real technical talks from the people building and operating real-time data systems at scale — recorded and hosted on-site.
- [Journey to an open lakehouse with Iceberg | Factor House](https://factorhouse.io/talks/journey-to-an-open-lakehouse-kafka-iceberg/): Karel Sague, Staff Software Engineer at Factor House, shares real-world lessons migrating a production data platform from Snowflake to Apache Iceberg, streaming Kafka into Iceberg with Kafka Connect.
- [Migrating to open source Kafka: cutting TCO | Factor House](https://factorhouse.io/talks/migrating-to-open-source-kafka/): Chad Harris (Factor House) and Justin George (NetApp Instaclustr) work a real total-cost-of-ownership comparison across Confluent, Amazon MSK, and managed open source Kafka.
- [New at Factor House: September 2026 | Factor House](https://factorhouse.io/talks/new-at-factor-house-september-2026/): Chad Harris and Derek Troy-West demo the new Factor House CLI, terminal UI and agent skills for Kpow, then cover CVE hygiene, new hardened Docker images, Signals, and the redesigned Kpow UI coming in October.
- [Reduce Kafka spend with cluster consolidation | Factor House](https://factorhouse.io/talks/reduce-kafka-spend-operational-risk-cluster-consolidation/): In this recorded webinar, Karel Sague and Chad Harris from Factor House share a practical framework for cutting Kafka costs and operational risk through cluster consolidation and migration.
- [Running Kafka at bank scale | Factor House](https://factorhouse.io/talks/running-kafka-at-bank-scale-td-bank/): Sandy Yang, Staff Software Engineer on TD's Event Streaming Platform, shares how one of North America's largest banks runs Kafka in production.
- [Kafka operational issues: how to survive them | Factor House](https://factorhouse.io/talks/things-that-go-bump-in-the-night-kafka-operational-issues/): In this recorded session, Chad Harris, Solutions Architect at Factor House, walks through real Kafka operational failures and the debugging workflows that actually help in production.