News, guides, and engineering deep dives.
Practical guidance on Kafka, Flink, Iceberg, and real-time data.
Industry Apache Kafka 4.3.0: A guide for platform engineers
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.
Product Data 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.
Industry What the IBM-Confluent deal 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.
Kafka consumers in production
A Kafka consumer reads records from topic partitions, tracking its own offset. The configuration that decides message loss, rebalance troubleshooting, offset resets, and the poll-loop patterns that survive production.
Kafka in 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 offsets
A Kafka offset is a record's position in its partition, and a consumer group's committed offset is its bookmark. Reading lag from CURRENT-OFFSET and LOG-END-OFFSET, the reset strategies, and where offsets are stored.
Kafka producers in production
A Kafka producer appends records to topic partitions. Configuration and tuning with real numbers, idempotence and delivery guarantees, the error classes, and the client-library decision that quietly matters most.
Kafka Streams
Kafka Streams is a Java library for stream processing that runs inside your own application, with state in local RocksDB stores backed by changelog topics. The operational realities, and where Flink wins instead.
Kafka Streams documentation, mapped
The Kafka Streams documentation divides into four layers: the API reference, the configuration surface, state store internals, and the upgrade guide. Which layer answers which production question, and the error-handling interfaces.
A Kafka topic example, fully specified
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
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.
A production Kafka tutorial
A Kafka tutorial for people who run it in production: zero-downtime upgrades, broker tuning, layered security, the troubleshooting signals that matter, and the client settings that decide delivery guarantees.