Redshift vs Databricks

Amazon Redshift and Databricks represent the data warehouse versus lakehouse divide. Redshift is a battle-tested columnar MPP warehouse. Databricks pioneered the lakehouse architecture, unifying data lakes and warehouses on open formats.

Architecture Comparison

Redshift's Warehouse Model

Redshift distributes data across compute nodes using columnar storage and zone maps for efficient scan pruning. It excels at complex analytical SQL — joins, aggregations, window functions — with automatic workload management (WLM) and result caching. Redshift Serverless removes cluster management but maintains the same query engine.

Databricks' Lakehouse Model

Databricks runs on Apache Spark with Delta Lake as the storage layer. Delta Lake adds ACID transactions, schema enforcement, and time travel to Parquet files on object storage. The Photon engine (a C++ vectorized engine) accelerates SQL queries to compete with traditional warehouses. This architecture lets you run ETL, SQL analytics, streaming, and ML on the same data without copying it.

Performance Analysis

For pure SQL analytics on structured data, Redshift generally delivers lower latency due to decades of MPP optimization. Databricks with Photon has closed the gap significantly and can match Redshift for many workloads, especially when queries also involve semi-structured data or ML feature generation.

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Cost Considerations

Redshift offers reserved instances that can reduce costs by 60-75% for predictable workloads. Databricks charges per DBU, and costs can spike with always-on clusters. However, Databricks' ability to use spot instances and auto-scaling clusters can optimize costs for bursty workloads. Review pricing models for both.

The Bottom Line

Choose Redshift for SQL-heavy BI workloads within the AWS ecosystem where cost predictability matters. Choose Databricks when you need a unified platform for ETL, analytics, streaming, and ML across multiple clouds.