Netflix Tech Stack: Databases

Netflix is a useful example of workload-driven database selection. At global streaming scale, no single database model fits billing, personalised recommendations, metrics, metadata, and media storage equally well. The database stack reflects that reality.

Relational systems still matter. MySQL is a sensible fit for strongly structured domains such as billing, subscriptions, revenue, and tax records, where transactional guarantees and predictable relational queries are more important than extreme write fan-out. CockroachDB appears in places where multi-region consistency and active-active operation are valuable. The key lesson is that relational databases remain central when correctness and transactional semantics are hard requirements.

For analytics, the shape of the workload changes completely. Batch queries, aggregation, and warehouse-style exploration are better served by column-oriented systems and large processing frameworks. Tools such as Redshift and Druid support high-volume analytical reads, while Spark handles transformation and large-scale data processing. Tableau then sits on top for consumption. This path exists because analytical workloads want compressed column scans and large aggregations, not row-by-row transactional access.

Netflix is also known for EVCache, a distributed key-value cache built on top of Memcached. Its job is not to replace durable storage but to reduce latency and origin load for hot data such as homepage content, recommendation artefacts, and service-level cached objects. This is a reminder that at large scale, caching becomes part of the primary architecture rather than an optional optimisation.

Cassandra fits workloads that need high write throughput, wide distribution, and operational resilience across many nodes. That makes it suitable for large volumes of metadata and user-related state where horizontal scale and availability matter more than complex joins. Wide-column stores are often selected not because they are universally better, but because they absorb scale patterns that would strain traditional relational systems.

Metrics form yet another workload. Atlas, Netflix's time-series system, exists because observability data has very different access patterns from user transactions. You ingest huge volumes of measurements, query them by time window and labels, and run aggregation continuously. Treating metrics like ordinary relational data would be unnecessarily expensive.

Finally, unstructured data such as media assets, images, logs, and large files fit object storage such as S3, often paired with table formats like Iceberg for large data lake use cases. Object storage is cheap, durable, and operationally simple compared with storing blobs in transactional databases.

The broader lesson is not that every company needs this exact stack. It is that database choice should follow access pattern, consistency need, and failure tolerance. Netflix uses many data systems because its workloads are different in ways that matter technically, not because variety is fashionable.