Tiered storage: 3 real scaling case studies
3 of the 73 engineering write-ups in our database are tagged with the technique "tiered storage". They come from 3 companies (Dropbox, Netflix and Tiger Data (Timescale)) and were published between 2019 and 2026. 2 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 We Scaled PostgreSQL to 350 TB+
Timescale dogfooded its own Postgres extension to build a database-observability product, using hypertables, columnar compression and tiered storage to keep a 350 TB+ dataset queryable.
How we optimized Magic Pocket for cold storage
Dropbox added a cold tier to Magic Pocket that cuts replication overhead for rarely read data.