Databricks + Snowflake: better together
We see lots of roles mentioning both platforms. Why?
Databricks shines for big data engineering, streaming ingestion, complex transformations, and ML on an open lakehouse (Delta).Snowflake excels at governed analytics: fast SQL, RBAC + masking/row policies, easy data sharing, and broad BI connectivity.How they work together (typical pattern)
Ingest on Databricks → land raw data in S3/ADLS/GCS and store it as Delta (Bronze).Transform/ML on Databricks → clean/enrich (Silver), aggregate/curate (Gold), train features/models.Serve in Snowflake for BI & sharing:Govern & scale in Snowflake → warehouses per workload, fine-grained policies, secure sharing to consumers.Benefits
Use Databricks for open, scalable ETL/ELT + ML; use Snowflake for SQL-first, governed consumption.Independent scaling of compute on each side; choose latency vs. cost per domain.Watch-outs
Avoid double storage if you don’t need it (prefer External Tables when feasible).Align schemas & SLAs across both tools.Keep permissions/lineage consistent (tags, policies, docs). TL;DR: Databricks builds the lakehouse; Snowflake makes it easy to consume, govern, and share it—together, a modern data platform.
#Databricks #Snowflake #Lakehouse #DataEngineering #Analytics #ELT #MLOps #DataGovernance
