Databricks vs Snowflake

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.

Snowflake's multi-cluster warehouse adds compute clusters automatically when concurrent BI user queries exceed capacity, preventing queue buildup that causes dashboard load times to spike on Monday mornings.

Snowflake Secure Data Sharing gives a partner or subsidiary access to a live data subset without copying it, eliminating the ETL pipeline and the stale data problem.

Snowflake Time Travel restores a table to any point within the retention window with a single SQL statement, recovering from accidental deletes or incorrect UPDATE operations without a backup restore process.