News, guides, and engineering deep dives.
Practical guidance on Kafka, Flink, Iceberg, and real-time data.
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.
How Shopify uses Apache Kafka in production
A deep-dive into Shopify's Kafka architecture — covering CDC at 100,000 records/sec, Kubernetes deployment, the Sarama Go client library, and BFCM scale engineering.
How Tencent uses Apache Kafka in production
A deep-dive into Tencent's Kafka architecture — covering their federated cluster design, 20 trillion messages per day, KIP contributions, and tiered storage at Tencent Cloud.
How Wix uses Apache Kafka in production
A deep-dive into Wix's Kafka architecture: 66 billion daily messages, 2,200+ microservices, the Greyhound SDK, Confluent Cloud migration, and operating 500,000+ partitions across 4 regions.
Conduktor: pricing and alternatives
Conduktor review for 2026: pricing, strengths, deployment trade-offs, and how it compares to alternatives for enterprise Kafka governance teams.
How Airbnb uses Apache Kafka in production
A deep-dive into Airbnb's Kafka architecture — covering six production systems, 35+ billion daily events, SpinalTap CDC, Flink-based personalisation, and Kafka as a write-ahead log.
How Bytedance uses Apache Kafka in production
ByteDance ran Kafka at tens of TB/s before replacing it with ByteMQ, a Kafka-compatible platform that separates storage from compute. How the architecture works, and why the migration cut resource cost by roughly 70%.
How Cloudflare uses Apache Kafka in production
A deep-dive into Cloudflare's Kafka architecture: use cases at trillion-message scale, 14 clusters, internal tooling decisions, and the engineering lessons behind a decade of Kafka operations.