# Netflix implements tiered storage for TimeSeries using Kafka, Flink, and Cassandra

> **Key takeaway:** Netflix reduced storage costs for petabyte-scale temporal data by migrating older datasets from hot storage to S3 using streaming and native Cassandra methods.

**Published:** 2026-10-14T19:56:08+00:00 
**Source:** Netflix Open Source & Technology 
**Category:** bigtech-foss 
**Canonical URL:** https://fosswire.org/news/netflix-implements-tiered-storage-for-timeseries-using-kafka.html 

## Summary
Netflix has detailed its evolution of the TimeSeries Abstraction platform to manage multi-petabyte datasets more cost-effectively. The company moved from a streaming approach using Apache Kafka and Apache Flink to write Parquet files to S3, to a more integrated solution leveraging Cassandra backups and pluggable Memtables for direct S3 cold reads. This tiered storage strategy allows recent data to remain on low-latency hardware while historical data resides on cheaper object storage.

## Evolution of Netflix's tiered storage
- **Solution 1** used Apache Kafka and Apache Flink to stream data into S3 as Parquet files, with a daily compaction job to merge small files into optimized daily files.
- **Solution 2** utilizes Cassandra backups in S3 and pluggable Memtables to enable direct reads from object storage.
- The system uses **Time Slices** to partition datasets into immutable windows for optimization.
- Data is hash-partitioned into buckets to allow dedicated Flink tasks to batch writes and reduce S3 throughput pressure.

## Why it matters
The implementation demonstrates how large-scale operators can combine multiple open source tools like Kafka, Flink, and Cassandra to manage the economic challenges of massive data retention.