News, guides, and engineering deep dives.
Practical guidance on Kafka, Flink, Iceberg, and real-time data.
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.
How The New York Times uses Kafka
A deep-dive into The New York Times' Kafka publishing pipeline, covering the Monolog architecture, single-partition design, Kafka Streams usage, and treating Kafka as a permanent content store.
How Uber uses Apache Kafka in production
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.
How Walmart uses Apache Kafka in production
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.
Accelerating incident response with AI queries
Fix streaming data failures faster. Learn how Kpow uses advanced kJQ filtering, BYO AI, and Streaming Search to slash incident response times.
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.