News, guides, and engineering deep dives.
News, guides, and engineering deep dives on Kafka, Flink, Iceberg, and real-time data.
How Goldman Sachs uses Apache Kafka in production
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
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 JPMorgan uses Apache Kafka in production
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.
How LinkedIn uses Apache Kafka in production
A deep-dive into LinkedIn's Kafka architecture, covering use cases, scale, engineering decisions, and key contributors.
How PayPal uses Apache Kafka in production
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 Reddit uses Apache Kafka in production
A deep-dive into Reddit's Kafka architecture — covering use cases, scale, engineering decisions and key contributors.
How Robinhood uses Apache Kafka in production
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.
How Spotify used Apache Kafka in production
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 The New York Times uses Apache Kafka in production
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.