News, guides, and engineering deep dives.
Practical guidance on Kafka, Flink, Iceberg, and real-time data.
How DoorDash uses Apache Kafka in production
A deep dive into DoorDash's Kafka architecture, covering the Iguazu event platform, Flink-based ML feature pipelines, self-serve topic governance, and hundreds of billions of daily events.
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 decisions behind a large-scale deployment.
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, and engineering decisions. Robinhood processes 2.2 million messages per second across equities, crypto, and fraud detection.
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.