News, guides, and engineering deep dives.
Practical guidance on Kafka, Flink, Iceberg, and real-time data.
How Apple uses Apache Kafka in production
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
A deep-dive into Barclays' Kafka architecture — covering dual-environment deployment on AWS and IBM Z-Linux, operating practices, and the broader streaming stack.
How Datadog uses Apache Kafka in production
A deep-dive into Datadog's Kafka architecture — covering use cases, scale, engineering decisions, and key contributors across hundreds of clusters.
How Netflix uses Apache Kafka in production
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
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
A deep-dive into Notion's Kafka architecture — covering use cases, scale, engineering decisions, and key contributors across their data lake and AI pipelines.
How PagerDuty uses Apache Kafka in production
A deep-dive into PagerDuty's Kafka architecture, covering event ingestion, notification scheduling, task execution, and the engineering decisions behind each.
How Pinterest uses Apache Kafka in production
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 Salesforce uses Apache Kafka in production
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.