How teams scaled Cassandra: 3 real case studies
3 of the 73 engineering write-ups in our database mention Cassandra in their stack. They come from 2 companies (Discord and Netflix) and were published between 2017 and 2026. Techniques that show up most often alongside Cassandra: database migration (2). 3 of the 3 state concrete scale numbers, for example "15+ PB compressed data in cold storage" (Netflix). Each card below links to our short summary and the original post.
TimeSeries Tiered Storage Journey: Kafka/Flink Streams to Native Cassandra Cold Reads
Netflix moved older data in its multi-petabyte TimeSeries Abstraction out of hot Cassandra storage in two steps. First, Kafka and Flink streamed events to S3 as Parquet files, which a daily job compacted into small, time-sorted row groups with Bloom filters cached in EVCache (p50 ~150 ms, p99 ~500 ms). The second design skips that streaming pipeline: a ReadOnlyCompactionStrategy aligns immutable SSTables to token ranges, and a custom pluggable Memtable (Cassandra 4.1) reads directly from existing Cassandra backups in S3. It now holds over 15 PB of compressed cold data, cut disk usage on affected clusters from 80% to 7%, and improved p90 latency by ~30%.
How Discord Stores Trillions of Messages
Discord moved its message store from Cassandra to ScyllaDB and put Rust data services in front of it to coalesce hot-partition requests.
How Discord Stores Billions of Messages
Early story of Discord outgrowing MongoDB and moving messages to Cassandra with a channel+time-bucket partition key.