Databricks vs Redis

Databricks Delta Lake adds full ACID guarantees to Parquet files on S3 or ADLS, enabling concurrent reads and writes that corrupt data in plain Parquet pipelines without managing separate lock services.

Databricks integrates MLflow natively, auto-logging parameters, metrics, and model artifacts for every training run, reducing experiment comparison from hours of manual log parsing to a 30-second dashboard review.

Databricks Structured Streaming processes Kafka events with exactly-once semantics and checkpointed state, supporting stateful aggregations across time windows without losing events on job restart.

Redis stores user session data with consistent sub-millisecond GET operations, handling 100K+ requests per second with a single instance where a database session store would create a bottleneck.

Redis INCR with EXPIRE implements sliding window rate limiting in 2 lines of code, enforcing per-user API quotas without adding a separate rate limiting service.

Redis Pub/Sub broadcasts events to thousands of connected subscribers in under 1ms, enabling real-time dashboards or notifications without polling the database.