# 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.

## Pages

- [Page not found | Factor House](https://factorhouse.io/404): Factor House builds tooling for real-time data: Kpow for Apache Kafka, Flex for Apache Flink, and Iglu for Apache Iceberg.
- [About | 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: advanced filters, streaming search, and AI-powered 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: Review, pricing, and best alternatives in 2026 | 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 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 with High-Fidelity Metrics | Factor House](https://factorhouse.io/articles/beyond-jmx-supercharging-grafana-dashboards-with-high-fidelity-metrics/): Move beyond raw JMX noise and unlock business-relevant observability for your Kafka environment. This guide explores how to feed high-fidelity, pre-calculated metrics, such as consumer group lag in seconds, directly from Kpow into your Grafana dashboards for proactive capacity planning and incident response.
- [Beyond Kafka: Sharp Signals from Current London 2025 | Factor House](https://factorhouse.io/articles/beyond-kafka-sharp-signals-from-current-london-2025/): The real-time ecosystem has outgrown Kafka alone. At Current London 2025, the transition from Kafka Summit was more than a name change — it marked a shift toward streaming-first AI, system-level control, and production-ready Flink. Here's what Factor House saw and learned on the ground.
- [Beyond Reagent: Migrating to React 19 with HSX and RFX | Factor House](https://factorhouse.io/articles/beyond-reagent-migrating-to-react-19-with-hsx-and-rfx/): Introducing two new open sources Clojure UI libraries by Factor House. HSX and RFX are drop-replacements for Reagent and Re-Frame, allowing us to migrate to React 19 while maintaining a familiar developer experience with Hiccup and similar data-driven event model.
- [Building a Real-Time Leaderboard 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 from scratch using a modern data stack. This open-source project guides you through using Apache Kafka, Apache Flink, and Streamlit to ingest, process, and visualize live data, turning a continuous stream of events into actionable insights on an interactive dashboard.
- [How Bytedance uses Apache Kafka in production | Factor House](https://factorhouse.io/articles/bytedance-kafka-architecture/)
- [Clone to topic for Dead Letter Queues 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: Review, pricing, and best alternatives in 2026 | 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: Review, pricing, and best alternatives in 2026 | 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: Review, pricing, and best alternatives in 2026 | 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 recent move to v2 cgroups by a number of Linux distributions (including Amazon Linux 2022 and Red Hat Enterprise Linux 9) highlights an issue in Amazon Corretto 11 where the JVM process can cause a Docker container to exit with OOMKilled errors.
- [Data governance for Kafka: introducing 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 options. This release also adds high-performance streaming for large datasets and expands kJQ with new transforms and functions—testable on our new interactive examples page. These updates provide deeper insights and more granular control over your Kafka data streams.
- [Improvements to Data Inspect in Kpow 94.3 | Factor House](https://factorhouse.io/articles/data-inspect-improvements-94-3/): Kpow's 94.3 release is here, transforming how you work with Kafka. Instantly query topics using plain English with our new AI-powered filtering, automatically decode any message format without manual setup, and leverage powerful new enhancements to our kJQ language. This update makes inspecting Kafka data more intuitive and powerful than ever before.
- [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 for transparent governance | 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 with Leiningen | 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/): Streamline your Kpow deployment on Amazon EKS with our guide, fully integrated with the AWS Marketplace. We use eksctl to automate IAM Roles for Service Accounts (IRSA), providing a secure integration for Kpow's licensing and metering. This allows your instance to handle license validation via AWS License Manager and report usage for hourly subscriptions, enabling a production-ready deployment with minimal configuration.
- [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 the engineering decisions behind hundreds of billions of daily events.
- [Enhanced Under-Replicated Partition Detection in Kpow | Factor House](https://factorhouse.io/articles/enhanced-urp-detection/): Kpow now offers enhanced under-replicated partition (URP) detection for more accurate Kafka health monitoring. Our improved calculation correctly identifies URPs even when brokers are offline, providing a true, real-time view of your cluster's fault tolerance. This helps you proactively mitigate risks and ensure data durability.
- [Ensuring Your Data Streaming Stack Is Ready for the EU Data Act | Factor House](https://factorhouse.io/articles/ensuring-your-data-streaming-stack-is-ready-for-the-eu-data-act/): The EU Data Act takes effect in September 2025, introducing major implications for teams running Kafka. This article explores what the Act means for data streaming engineers, and how Kpow can help ensure compliance — from user data access to audit logging and secure interoperability.
- [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 a VPAT available to download in every release of Kpow since. Today, we are pleased to announce that we are extending that commitment to all future Factor House product releases - including Flex for Apache Flink and the Factor Platform.
- [A final goodbye to OperatrIO | Factor House](https://factorhouse.io/articles/final-goodbye-operatr-io/): 2025 is a pivotal moment at Factor House (formally Operatr.IO). We've announced our fundraise and have much more to announce about our roadmap this year. This is why we think that now is the perfect time to do a bit of spring cleaning and retire the io.operatr artifacts for good.
- [Foundational Kafka data inspection: shaping payloads and optimizing visibility | 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: A Hands-On CDC Project with Debezium, Kafka, and theLook eCommerce Data | 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 transforms the static "theLook" eCommerce dataset into a live data stream. It uses a Python generator to simulate user activity in PostgreSQL, while Debezium captures every database change and streams it to Kafka. This creates a hands-on environment for building and testing real-time CDC pipelines.
- [From Bootstrap to Blackbird: The Future of Factor House](https://factorhouse.io/articles/from-bootstrap-to-blackbird/): We are thrilled to announce that Factor House has closed a $5M seed round to accelerate the commercial release of our new product, the Factor Platform. Led by Blackbird Ventures, with OIF Ventures, Flying Fox Ventures, and LaunchVic’s Alice Anderson Fund as partners, this round brings our five-year bootstrapping journey to a happy conclusion and points to a bright future ahead!
- [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: 5 options | 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 schema registries with Kpow | Factor House](https://factorhouse.io/articles/integrate-confluent-compatible-registries-kpow/): This guide demonstrates how to address the operational complexity of managing multiple Kafka schema registries. We integrate Confluent-compatible registries—Confluent Schema Registry, Apicurio Registry, and Karapace—and manage them all through a single pane of glass using 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 through our market-leading engineering console.
- [Integrate Kpow with Google Managed Schema Registry | Factor House](https://factorhouse.io/articles/integrate-kpow-with-google-schema-registry/): Kpow 94.3 now integrates with Google Cloud's managed Schema Registry, enabling native OAuth authentication. This guide walks through the complete process of configuring authentication and using Kpow to create, manage, and inspect data validated against Avro schemas.
- [How to Integrate Kpow with OCI Streaming with Apache Kafka | Factor House](https://factorhouse.io/articles/integrate-kpow-with-oci-streaming/): Integrate Kpow with Oracle Cloud Infrastructure Streaming with Apache Kafka in minutes. Gain unified visibility and control over your OCI brokers and ecosystem components through our market-leading engineering toolkit.
- [Integrate Kpow with the 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 through our market-leading engineering console.
- [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 through our market-leading engineering console.
- [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 through our market-leading engineering console.
- [Introducing Factor House Docs | Factor House](https://factorhouse.io/articles/intro-factor-house-docs/): We're excited to launch the new Factor House Docs, a unified hub for all our product documentation. Discover key improvements like a completely new task-based structure, interactive kJQ examples, and powerful search, all designed to help you find the information you need, faster than ever. Explore the new home for all things Kpow, Flex, Factor Platform and more.
- [Introduction to Factor House Local | Factor House](https://factorhouse.io/articles/intro-to-factor-house-local/): Jumpstart your journey into modern data engineering with Factor House Local. Explore pre-configured Docker environments for Kafka, Flink, Spark, and Iceberg, enhanced with enterprise-grade tools like Kpow and Flex. Our hands-on labs guide you step-by-step, from building your first Kafka client to creating a complete data lakehouse and real-time analytics system. It's the fastest way to learn, prototype, and build sophisticated data platforms.
- [Introducing Kpow's new API | Factor House](https://factorhouse.io/articles/introducing-kpows-new-api/): With our new API, you can now leverage Kpow's capabilities directly from your own tools and platforms, opening up a whole new range of possibilities for integrating Kpow into your existing workflows. Whether you're managing topics, consumer groups, or monitoring Kafka clusters, our API provides a seamless experience that mirrors the functionality of our user interface.
- [Introducing Webhook Support in Kpow | Factor House](https://factorhouse.io/articles/introducing-webhook-support-in-kpow/): This guide demonstrates how to enhance Kafka monitoring and data governance by integrating Kpow's audit logs with external systems. We provide a step-by-step walkthrough for configuring webhooks to send real-time user activity alerts from your Kafka environment directly into collaboration platforms like Slack and Microsoft Teams, streamlining your operational awareness and response.
- [Releasing Software at Factor House: Our Java Compatibility and Evolution Strategy | Factor House](https://factorhouse.io/articles/java-compatibility-and-evolution-strategy/): At Factor House, delivering reliable software is at the heart of everything we do. A key aspect of this commitment lies in our approach to managing Java compatibility. This blog post outlines our current release process and future plans for evolving Java support, including our approach to deprecating older versions in a way that respects the needs of diverse customer bases.
- [Join the conversation: Factor House launches open Slack for the real-time data community | Factor House](https://factorhouse.io/articles/join-the-conversation-community-slack/): Factor House has opened a public Slack for anyone working with streaming data, from seasoned engineers to newcomers exploring real-time systems. This space offers faster peer-to-peer support, open discussion across the ecosystem, and a friendly on-ramp for those just getting started.
- [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 the engineering decisions behind one of the largest financial services deployments.
- [Kadeck: Review, pricing, and best alternatives in 2026 | 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: Review, pricing, and best alternatives in 2026 | Factor House](https://factorhouse.io/articles/kafbat-ui/): A practical review of Kafbat, the open-source kafka-ui fork — covering features, deployment, security, pricing, and best alternatives in 2026.
- [Kafdrop: Review, pricing, and best alternatives in 2026 | 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, this release marks a major milestone for the future of real-time data systems.
- [Kafka Alerting with Kpow, Prometheus and Alertmanager | Factor House](https://factorhouse.io/articles/kafka-alerting-with-kpow-prometheus-and-alertmanager/): This article covers setting up alerting with Kpow using Prometheus and Alertmanager. Introduction Kpow was built from our own need to monitor Kafka clusters and related resources (eg, Streams, Connect and Schema Registries). Through Kpow's user interface we can detect and even predict potential problems...
- [Apache Kafka architecture: a complete guide to internals, components, and deployment | 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: key JMX metrics, alerting thresholds, process monitoring scripts, and common issues with step-by-step diagnosis.
- [The Complete Guide to Kafka Change Data Capture (CDC) | 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 for engineers | 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 cluster monitoring | Factor House](https://factorhouse.io/articles/kafka-cluster-monitoring/): What to monitor at the Kafka cluster level: key JMX metrics, multi-broker collection, alerting thresholds, capacity signals, and a health check script.
- [Kafka consumer monitoring and performance tuning | 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: the features that matter in production | 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: Unlocking Engineering Productivity | Factor House](https://factorhouse.io/articles/kafka-data-management-with-kpow/): Enterprise Kafka adoption promises massive scalability and decoupled agility. However, interacting with complex streaming data at scale often bogs developers down in manual operational friction. By identifying four critical friction points across visibility, velocity, remediation, and compliance, this article introduces a comprehensive data management strategy to eliminate bottlenecks and unlock engineering productivity with Kpow.
- [Kafka management console: what to look for in a tool | 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 monitoring: a complete guide for platform engineers | Factor House](https://factorhouse.io/articles/kafka-monitoring/): A practical guide to Kafka monitoring for platform engineers: the metrics that matter, alert thresholds, JVM tuning, consumer lag, and KRaft changes.
- [Kafka Observability with Kpow: Driving Operational Excellence | Factor House](https://factorhouse.io/articles/kafka-observability-with-kpow-driving-operational-excellence/): Apache Kafka is the central nervous system of the modern enterprise, yet operating it at scale often leads to reactive maintenance cycles. Identifying three critical gaps in context, data quality, and governance, this article introduces a comprehensive strategy to transform reactive troubleshooting into proactive 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.
- [Apache Kafka 3.2.0: Idempotent Producer Breaking Change | Factor House](https://factorhouse.io/articles/kafka-producer-breaking-change/): Apache Kafka KIP-679 changes the behaviour of default Producer configuration to enable idempotence by default. This change 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: best practices 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: Rethinking Storage and Cloud Costs in Kafka | 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: Bridging the Gap Between Streaming and Messaging | 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.
- [Kpow Community Edition 🚀 | Factor House](https://factorhouse.io/articles/kpow-community-edition/): Kpow Community Edition is a free, developer focused toolkit for Apache Kafka clusters, schema registries, and connect installations.
- [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 best alternatives in 2026 | 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.
- [Manage Kafka Consumer Offsets with Kpow | Factor House](https://factorhouse.io/articles/manage-kafka-consumer-offsets-with-kpow/): Kpow version 94.2 enhances consumer group management capabilities, providing greater control and visibility into Kafka consumption. This article provides a step-by-step guide on how to manage consumer offsets 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.
- [Manage Temporary Access to Kafka Resources | Factor House](https://factorhouse.io/articles/manage-temporary-access-to-kafka-resources/): Temporary policies allow Admins the ability to assign access control policies for a fixed duration. This blog post introduces temporary policies with an all-to-common real-world scenario.
- [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.
- [Operational Transparency: Real-Time Audit Trail Integrated with Webhooks | Factor House](https://factorhouse.io/articles/operational-transparency-audit-trail-integrated-with-webhooks/): Operating Kafka without a transparent audit trail creates a critical "Governance Gap", leaving teams blind to administrative changes and vulnerable during incidents. This guide demonstrates how to replace opaque log parsing and restrictive bureaucracy with automated governance by streaming Kpow's real-time audit log via webhooks directly into communication tools like Slack.
- [Operatr.IO has a new name: Meet Factor House](https://factorhouse.io/articles/operatr-io-has-a-new-name-meet-factor-house/): Meet Factor House, we build Kpow for Apache Kafka
- [Our Commitment to Engineers | Factor House](https://factorhouse.io/articles/our-commitment-to-engineers/): With our funding announcement and the upcoming launch of the Factor Platform, we know some of our existing customers might be wondering: What does this mean for Kpow and Flex? Will we be forced to upgrade? Will prices spike? Keep one thing in mind - 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 task for engineers working on data streaming applications, but it can often be a complex and time-consuming process. Enter Kpow's data inspect feature—designed to simplify and optimize Kafka topic queries, making it an essential tool for professionals working with Apache Kafka.
- [Rapid Kafka Diagnostics: A Unified Workflow for Root Cause Analysis | Factor House](https://factorhouse.io/articles/rapid-kafka-diagnostics-a-unified-workflow-for-root-cause-analysis/): The Context Gap caused by fragmented tools hinders effective Kafka monitoring and troubleshooting, as it forces engineers to manually piece together logs and metrics. This guide demonstrates how to close that gap using Kpow's unified workflow to identify the stall, inspect the data, and resolve the incident in a single interface.
- [RBAC for Kafka: How to Implement and Key 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 July Meetup Recap: Real-time Data Hosted by Factor House & Confluent | Factor House](https://factorhouse.io/articles/real-time-data-to-insights-meetup-july25/): From structuring data streams to spinning up full pipelines locally, our latest 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: Review, pricing, and best alternatives in 2026 | 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, engineering decisions, and key contributors. Learn how Robinhood processes 2.2 million messages per second across equities trading, crypto, fraud detection, and more.
- [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. Introduction Kpow is the all-in-one toolkit to manage, monitor, and learn about your Kafka resources. Helm is the package manager for Kubernetes. Helm deploys charts, which you can think of as a packaged application. We publish...
- [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: Streamlining Workflows with Kpow and 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 Managed Streaming for Apache Kafka | Factor House](https://factorhouse.io/articles/set-up-kpow-with-aws/): Integrate Kpow with Amazon Managed Streaming for Apache Kafka (MSK) in minutes. Gain unified visibility and control over your AWS brokers, MSK Connect, and Glue Schema Registry through our market-leading engineering toolkit.
- [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 through our market-leading engineering toolkit.
- [Set Up Kpow with Google Cloud Managed Service for Apache Kafka | Factor House](https://factorhouse.io/articles/set-up-kpow-with-gcp/): Integrate Kpow with Google Cloud Managed Service for Apache Kafka (MSAK) in minutes. Gain unified visibility and control over your managed Kafka brokers and Schema Registry through our market-leading engineering console.
- [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 market-leading engineering 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 Apache Kafka in production | 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 the engineering decisions behind treating Kafka as a permanent content store.
- [Top Kafka UI Tools in 2026: A Practical Comparison for Engineering Teams | 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: integrated Kafka remediation workflows | 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 both Kpow Community Edition and Flex Community Edition, meaning one license will unlock both products. This makes it even simpler to explore modern data streaming tools, create proof-of-concepts, and evaluate our products.
- [Updates to container specifics (DockerHub and Helm Charts) | 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 building accessible products is important to us, and how we've changed to ensure that accessibility is embedded in our development process.
- [What the IBM Confluent acquisition 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 | Factor House](https://factorhouse.io/authors/chad-harris/): Chad's expertise spans distributed systems, real-time data infrastructure, and enterprise application architecture. He has over six years of production experience with Apache Kafka and extensive background in NoSQL data stores including DynamoDB and Cassandra. His specialisation includes building highly scalable, tokenised security systems and high-volume transactional platforms built to PCI compliance standards. Beyond the technical, Chad is a seasoned engineering leader with a track record of mentoring teams and applying agile and lean methodologies in ways that deliver practical business outcomes.
- [Derek Troy-West | Factor House](https://factorhouse.io/authors/derek-troy-west/): Derek's expertise is rooted in the design and delivery of highly available, linearly scalable streaming and distributed systems. He has deep practical knowledge of Apache Kafka, Apache Flink, Apache Cassandra, and the broader real-time data ecosystem, accumulated across two decades of hands-on engineering and consulting work. A practitioner and advocate of Clojure, Derek has written extensively on the language, spoken about it at Clojure/Conj, and built much of Factor House's core tooling in it. As CEO, he shapes the product vision for Kpow and Flex, and regularly writes and speaks on topics spanning Kafka operations, stream processing, and data engineering platform design.
- [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 | Factor House](https://factorhouse.io/authors/gaurav-bhatt/): Gaurav's core expertise lies in Java-based enterprise systems, with deep experience in data standardisation, deduplication, cleansing, and consolidation. He has worked extensively with search and indexing technologies including Lucene and Elasticsearch, and has broad exposure to the full software delivery lifecycle from requirements analysis through to production deployment. More recently, he has developed skills in machine learning and generative AI, with a focus on convolutional neural networks, recommendation systems, and building AI applications on AWS.
- [Jaehyeon Kim | Factor House](https://factorhouse.io/authors/jaehyeon-kim/): Jaehyeon works 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.
- [Kylie Troy-West | Factor House](https://factorhouse.io/authors/kylie-troy-west/): Kylie's expertise spans startup operations, go-to-market strategy, and business development in the data and distributed systems space. She led the company's fundraising process, and has built and led teams across sales, marketing, and customer success. Her earlier career in large-scale event and festival management sharpened her ability to orchestrate complex projects, manage stakeholders, and deliver under pressure, capabilities she has applied directly to scaling Factor House as a fast-growing enterprise software company.
- [Lutz Hühnken | 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 | Factor House](https://factorhouse.io/authors/moslem-chalfouh/): Moslem's expertise spans enterprise application architecture, event-driven systems, and cloud infrastructure, with a particular focus on the insurance and finance sectors. He has extensive hands-on experience with Java, Spring Boot, and Apache Kafka, and has worked extensively with AWS services including Amazon Bedrock and generative AI tooling. His specialisations include hexagonal architecture, REST API design, document management system integration, and CI/CD pipeline delivery using Jenkins and Terraform. He is also an active practitioner of Claude Code, reflecting his interest in applying AI-assisted development in real-world engineering contexts.
- [Nicolas Venegas | 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 | 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 | 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 | 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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [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.
- [Blog | Factor House](https://factorhouse.io/blog/): Guides, tutorials and engineering deep dives on running Apache Kafka and Apache Flink in production.
- [Careers | 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 highly regulated FinTech environment. Chad from Block explains why Factor House became their go-to partner for operational insights and deep distributed systems expertise.
- [Kpow Delivers Big Gains for Fintech Giant Pepperstone | Factor House](https://factorhouse.io/case-studies/how-kpow-is-delivering-for-pepperstone/): Melbourne is the beating heart of Australia's quietly surging fintech industry and for almost a decade, local tech-enabled trading company Pepperstone has been carving its own niche, developing a burgeoning startup into one of the largest Forex and CFD brokers in the world. The company's...
- [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.
- [Customer Obsession Driving Innovation with Kafka at Pickles | Factor House](https://factorhouse.io/case-studies/pickles-kafka-and-kpow/): Innovation and evolution are central to the Pickles delivery model. Find out how Kafka and Kpow helps the Pickles team deliver.
- [Verrency on Fintech, Kafka, and Kpow | Factor House](https://factorhouse.io/case-studies/verrency-fintech-kafka-and-kpow/): Verrency is one of Melbourne's most exciting fintech start-ups. The Verrency platform allows banks to include value-added services to their payment pipeline without a significant internal IT spend and provides solutions to personalize aspects of the banking experience so consumers can tailor products to their own...
- [Changelog - Page 10 | Factor House](https://factorhouse.io/changelog/10/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Changelog - Page 2 | Factor House](https://factorhouse.io/changelog/2/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Changelog - Page 3 | Factor House](https://factorhouse.io/changelog/3/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Changelog - Page 4 | Factor House](https://factorhouse.io/changelog/4/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Changelog - Page 5 | Factor House](https://factorhouse.io/changelog/5/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Changelog - Page 6 | Factor House](https://factorhouse.io/changelog/6/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Changelog - Page 7 | Factor House](https://factorhouse.io/changelog/7/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Changelog - Page 8 | Factor House](https://factorhouse.io/changelog/8/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Changelog - Page 9 | Factor House](https://factorhouse.io/changelog/9/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [Changelog | Factor House](https://factorhouse.io/changelog/): Every fix, feature, and improvement shipped across Kpow, Flex, Iglu, and Factor Platform.
- [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 | 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.
- [Events | Factor House](https://factorhouse.io/events/): Webinars, conferences, and meetups where you can learn how platform teams run real-time data streaming in production.
- [Kafka User Group - Americas | Factor House](https://factorhouse.io/events/kafka-user-group-americas-july-2026/): A free online session for Kafka practitioners. Each session kicks off with a lightning talk from a community member, followed by open group discussion — a chance to share what you are building, work through challenges together, and connect with engineers facing the same problems.
- [Kafka User Group - Asia Pacific | Factor House](https://factorhouse.io/events/kafka-user-group-asia-pacific-july-2026/): A free online session for Kafka practitioners across Asia Pacific. Each session kicks off with a lightning talk from a community member, followed by open group discussion — a chance to share what you are building, work through challenges together, and connect with engineers facing the same problems.
- [Kafka User Group - Europe | Factor House](https://factorhouse.io/events/kafka-user-group-europe-sep-2026/): A free online session for Kafka practitioners across Europe. Each session kicks off with a lightning talk from a community member, followed by open group discussion — a chance to share what you are building, work through challenges together, and connect with engineers facing the same problems.
- [Things that go bump in the night: Kafka operational issues and how to survive them | 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 | 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.
- [Factor House expands to Europe | 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 | Factor House](https://factorhouse.io/press/): Press releases and news coverage of Factor House.
- [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 | Factor House](https://factorhouse.io/privacy/): How Factor House collects, uses and protects your information.
- [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 | Factor House](https://factorhouse.io/products/iglu/changelog/): Release notes for Iglu.
- [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 - Page 2 | Factor House](https://factorhouse.io/products/kpow/changelog/2/): Release notes for Kpow.
- [Kpow changelog - Page 3 | Factor House](https://factorhouse.io/products/kpow/changelog/3/): Release notes for Kpow.
- [Kpow changelog - Page 4 | Factor House](https://factorhouse.io/products/kpow/changelog/4/): Release notes for Kpow.
- [Kpow changelog - Page 5 | Factor House](https://factorhouse.io/products/kpow/changelog/5/): Release notes for Kpow.
- [Kpow changelog - Page 6 | Factor House](https://factorhouse.io/products/kpow/changelog/6/): Release notes for Kpow.
- [Kpow changelog - Page 7 | Factor House](https://factorhouse.io/products/kpow/changelog/7/): Release notes for Kpow.
- [Kpow changelog - Page 8 | Factor House](https://factorhouse.io/products/kpow/changelog/8/): Release notes for Kpow.
- [Kpow changelog | Factor House](https://factorhouse.io/products/kpow/changelog/): Release notes for Kpow.
- [Kpow features | 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.
- [Kpow pricing | Factor House](https://factorhouse.io/products/kpow/pricing/): Kpow is priced per cluster, not per seat. Start free on a single cluster with Community Edition, or upgrade to Enterprise for governance and support.
- [Releases - Page 2 | Factor House](https://factorhouse.io/releases/2/): Release notes and downloads for Kpow, Flex, and Factor Platform.
- [Releases - Page 3 | Factor House](https://factorhouse.io/releases/3/): Release notes and downloads for Kpow, Flex, and Factor Platform.
- [Release 35: Support OKTA Authentication for Single Sign-On | Factor House](https://factorhouse.io/releases/35/): Support OKTA Authentication for Single Sign-On
- [Release 36: Improved Error Reporting | Factor House](https://factorhouse.io/releases/36/): Improved error reporting and performance
- [Release 37: Quick Search for Produced Messages | Factor House](https://factorhouse.io/releases/37/): Quickly search for produces 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
- [Releases - Page 4 | Factor House](https://factorhouse.io/releases/4/): Release notes and downloads for Kpow, Flex, and Factor Platform.
- [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
- [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
- [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
- [Releases - Page 5 | Factor House](https://factorhouse.io/releases/5/): Release notes and downloads for Kpow, Flex, and Factor Platform.
- [Release 50: Data Masking Policies 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 your Slack enabling you to more easily 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/): Kpow integrates more closely with Kafka Connect
- [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
- [Releases - Page 6 | Factor House](https://factorhouse.io/releases/6/): Release notes and downloads for Kpow, Flex, and Factor Platform.
- [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 that provides all the tooling required to consume Kafka topics and explore structured data with Klang -- a language that blends JQ and Clojure.
- [Release 66: UI Stability | Factor House](https://factorhouse.io/releases/66/): Resolves an issue that occasionally impacted compute/group views.
- [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.
- [Releases - Page 7 | Factor House](https://factorhouse.io/releases/7/): Release notes and downloads for Kpow, Flex, and Factor Platform.
- [Release 70: Azure and Confluent Support, Transparent CI/CD. | 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 Support | 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: Improved 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 – Staged Mutations and Temporary Policies | 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.
- [Releases - Page 8 | Factor House](https://factorhouse.io/releases/8/): Release notes and downloads for Kpow, Flex, and Factor Platform.
- [Release 80: Kafka Streams, Topic Regex Search, 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, Streaming Search, and Confluent Metrics | 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 Header Production and Protobuf Improvements | 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 and Encrypted Configuration | Factor House](https://factorhouse.io/releases/88-2/): Kpow v88.2 introduces improved support for monitoring Flink consumers, the ability to encrypt your Kpow configuration to avoid passwords in plaintext, new configuration options for connecting to Confluent Schema Registries that require mutual TLS for authentication, and more.
- [Release 88.4: Security, Performance and Resizable Columns | 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, easy configuration of JSON application logs, improved orphan schema detection, a JDK17 docker image, and improves integration with both Confluent Cloud and Confluent Schema Registry.
- [Release 88.7: Kafka Idempotent Producer Bug | Factor House](https://factorhouse.io/releases/88-7/): Kpow v88.7 resolves a minor issue introduced in Kafka 3.2.0
- [Release 89.1: Search Speed and Managed Connect | Factor House](https://factorhouse.io/releases/89-1/): Kpow v89.1 is a major feature release that includes up to 20x improvement to Data Inspect speed, support for Confluent Managed Connect, support for Confluent Managed Connect, and support for MSK Serverless.
- [Release 89.2: Data Inspect and SAML Auth Improvements | 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, new 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 configurability and tenancy performance improvements | 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, Performance Tuning, and Community Enhancements | 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 fixed a regression in our Java 8 Build
- [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 and 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, Transactional 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 along with new features for topic and consumer management and improvements to Flink checkpointing and watermarking 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 Accessibility Compliance | Factor House](https://factorhouse.io/releases/92-4/): Our mission at Factor House is to empower every engineer in the streaming tech space with superb tooling. We are pleased to report that Kpow for Apache Kafka is now compliant with WCAG 2.1 AA accessibility guidelines and has an independently audited Voluntary Product Accessiblity Template (VPAT) report.
- [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. Read on to learn how to integrate Kpow with your product or GitOps pipeline using Kpow's new REST API modules.
- [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/): Introducing full support for schema references in Confluent Schema Registry. With this new release you can now create and edit AVRO, JSONSchema, and Protobuf schema with references, as well as consume and produce messages with those schema.
- [Release 93.4: Protobuf, Light Mode, and Community | Factor House](https://factorhouse.io/releases/93-4/): This minor version release from Factor House improves protobuf rendering, sharpens light-mode, simplifies community edition setup, resolves a number of small bugs, and bumps Kafka client dependencies to v3.7.0.
- [Release 94.1: Streams Agent, Consumer Offset Management, and 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, and A+ Docker Health | Factor House](https://factorhouse.io/releases/94-2/): This minor release from Factor House introduces support for GCP MSK and new feature improvements such as data inspect display options, AVRO Date Logical Type formatting, flat CSV export, and fixes a bug in consumer offset reset!
- [Release 94.3: BYO AI, Topic data inference, and Data Inspect improvements | Factor House](https://factorhouse.io/releases/94-3/): This minor release from Factor House introduces BYO AI model support, topic data inference, and major enhancements to data inspect—such as AI-powered filtering, new UI elements, and AVRO date formatting. It also adds integration with GCP Managed Kafka Schema Registry, improved webhook support, updated policy handling, and several UI and error-handling 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: New Factor House docs, enhanced data inspection and URP & KRaft improvements | Factor House](https://factorhouse.io/releases/94-5/): This release introduces a new unified documentation hub - Factor House Docs. It also introduces major data inspection enhancements, including comma-separated kJQ Projection expressions, in-browser search, and over 15 new kJQ transforms and functions. Further improvements include more reliable cluster monitoring with improved Under-Replicated Partition (URP) detection, support for KRaft improvements, the flexibility to configure custom serializers per-cluster, and a resolution for a key consumer group offset reset issue.
- [Release 94.6: Factor Platform, Ververica integration, and kJQ enhancements | Factor House](https://factorhouse.io/releases/94-6/): The first Factor Platform release candidate is here, a major milestone toward a unified control plane for real-time data streaming technologies. This release also introduces Ververica Platform integration in Flex, plus support for Kafka Clients 4.1 / Confluent 8.0.0 and new kJQ operators for richer stream inspection.
- [Release 95.1: A unified experience across product, web, docs and licensing | Factor House](https://factorhouse.io/releases/95-1/): 95.1 delivers a cohesive experience across Factor House products, licensing, and brand. This release introduces our new license portal, refreshed company-wide branding, a unified Community License for Kpow and Flex, and a series of performance, accessibility, and schema-related improvements.
- [Release 95.2: Quality-of-life improvements across Kpow, Flex & Helm deployments | 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. Alongside bug fixes and usability improvements, this release adds new Helm options for configuring the API and controlling service account credential automounting.
- [Release 95.3: Memory leak fix for in-memory 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 data inspect, Strimzi support, and consumer group improvements | 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, performance improvements and new features in Kpow and Flex | 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: Inline schema references view and extensive Data Inspect improvements. | Factor House](https://factorhouse.io/releases/96-2/): Inline schema references view and extensive Data Inspect improvements.
- [Releases | Factor House](https://factorhouse.io/releases/): Release notes and downloads for Kpow, Flex, and Factor Platform.
- [Security Policy | Factor House](https://factorhouse.io/security/): Factor House's vulnerability disclosure policy and security practices.
- [Solutions | Factor House](https://factorhouse.io/solutions/): Explore Kpow solutions by industry, provider, and use case.
- [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 Kpow solutions by industry.
- [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 Kpow solutions by cloud and streaming provider.
- [Solutions by use case | Factor House](https://factorhouse.io/solutions/use-case/): Explore Kpow solutions by use case.
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