Change data capture: 3 real scaling case studies
3 of the 73 engineering write-ups in our database are tagged with the technique "change data capture". They come from 3 companies (Figma, Notion and OpenAI) and were published between 2021 and 2026. 1 of the 3 state concrete scale numbers, for example "70 million+ requests/sec" (OpenAI). Each card below links to our short summary and the original post.
Rapidly scaling online storage to serve over 1 billion ChatGPT users
OpenAI's Habitat storage platform grew from a Python client library over Cosmos DB into a central service handling 70M+ requests per second. The team ran many low-concurrency Python processes to keep asyncio delay down, switched aiohttp connection reuse from LIFO to FIFO to stop a metastable failure, used Envoy for connection fan-in, kept a deliberately simple NoSQL API, then rewrote the service in Rust.
Building and scaling Notion's data lake
Notion replaced slow warehouse syncs with a CDC pipeline (Debezium to Kafka to Hudi on S3) processed with Spark.
LiveGraph: real-time data fetching at Figma
Figma built LiveGraph, which tails the Postgres replication log so its UI can subscribe to live query results.