Redis

About Redis

Open-source, in-memory data structure store used as database, cache, and message broker. Supports various data structures including strings, hashes, lists, sets, sorted sets, and streams. Provides high performance, sub-millisecond latency, and is widely used for caching, session management, and real-time analytics.

Key Features

• 1Sub-millisecond read/write latency for in-memory data structures
• 2Support for strings, hashes, lists, sets, sorted sets, streams, and more
• 3Pub/Sub messaging and Redis Streams for event-driven pipeline patterns
• 4Built-in TTL expiry for automatic cache invalidation
• 5Redis Cluster for horizontal scaling and high availability

How Python Data Engineers Use Redis

Python data engineers use Redis via the redis-py library as a high-speed caching layer, rate-limit store, and task queue broker. Redis is the default broker for Celery-based task queues and is used by Apache Airflow for caching pipeline metadata. In data pipelines, Redis stores intermediate results, deduplication sets, and real-time leaderboards that would be too slow to query from a relational database on every request.