Databricks vs MySQL

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.

MySQL replication routes heavy analytical queries to a read replica, removing contention with write-heavy OLTP traffic and keeping application response times below 100ms during peak loads.

MySQL's InnoDB full-text index supports natural language and boolean search queries on text columns without a separate Elasticsearch deployment for basic in-app search needs.

ProxySQL in front of MySQL pools thousands of application connections into tens of database connections, preventing connection exhaustion on deployments that scale web processes horizontally.