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Amazon Redshift vs Databricks SQL

Amazon Redshift: AWS's MPP columnar data warehouse, available as provisioned RA3 clusters or Serverless. Databricks SQL: Serverless SQL warehouses on the Databricks lakehouse, powered by the Photon C++ engine.

key differences

  • Only Amazon Redshift has stored procedures.
  • Only Databricks SQL has vector search, change data capture, time travel.

choose amazon-redshift for

  • +Enterprise data warehouse on AWS
  • +BI dashboards over curated marts
  • +Lakehouse queries over S3 with Spectrum
  • +Near-real-time analytics on Aurora data via zero-ETL

best at · Warehouse, OLAP, Lakehouse

choose databricks-sql for

  • +Lakehouse SQL and BI on Delta Lake
  • +Consolidating a separate warehouse into Databricks
  • +dbt transformations on the lakehouse
  • +Serving features and aggregates for ML

best at · Lakehouse, Warehouse, OLAP

Amazon RedshiftAWS's MPP columnar data warehouse, available as provisioned RA3 clusters or Serverless.
Databricks SQLServerless SQL warehouses on the Databricks lakehouse, powered by the Photon C++ engine.
overview
Primary model
Columnar / OLAP
Columnar / OLAP
Also supports
Relational, Document, Spatial
Relational, Document, Vector, Spatial
Type
Managed service
Managed service
Status
Active
Active
Developer
Amazon Web Services
Databricks
First release
2012
2020
Latest version
–
–
License
ProprietaryProprietary
ProprietaryProprietary
Free tier
—
✓
popularity
WhichDB Score
55.8
20.8
Rank
#21
#147
GitHub stars
–
–
Stack Overflow questions
8.8K
543
scale & guarantees
Comfortable data size
Massive (Multi-PB)
Massive (Multi-PB)
Horizontal scaling
Native
Native
Multi-region
Read replicas
None
ACID
Full
Full
Consistency
Strong
Strong
Schema
Strict
Flexible
Architecture
Distributed, Shared storage, Serverless
Serverless, Distributed, Shared storage
deployment
Self-hosted
—
—
Vendor-managed cloud
✓
✓
Serverless
✓
✓
Embedded
—
—
Edge
—
—
Clouds
AWS
AWS, Azure, Google Cloud
capabilities
Transactions
✓
✓
Joins
✓
✓
Secondary indexes
—
—
Full-text search
—
—
Vector search
—
✓
Geospatial
✓
✓
JSON
✓
✓
Change data capture
—
✓
Triggers
—
—
Stored procedures
✓
—
Materialized views
✓
✓
TTL / expiry
—
—
Pub/Sub
—
—
Columnar storage
✓
✓
In-memory
—
—
Durable persistence
✓
✓
Compression
✓
✓
Encryption at rest
✓
✓
Role-based access
✓
✓
Time travel
—
✓
Branching
—
—
Online schema changes
✓
✓
Streaming ingest
✓
✓
Offline sync
—
—
Runs in WebAssembly
—
—
interfaces
Query languages
SQL (PostgreSQL dialect), PL/pgSQL
SQL (Databricks SQL / ANSI), Python
Client languages
Python, Java, JavaScript, Go, .NET, C
Python, Java, JavaScript, Go, .NET
Best for
Warehouse, OLAP, Lakehouse
Lakehouse, Warehouse, OLAP
Add more to this comparisonNot sure either fits? Try smart match

Amazon Redshift vs Databricks SQL: FAQ

What is the difference between Amazon Redshift and Databricks SQL?

Amazon Redshift: AWS's MPP columnar data warehouse, available as provisioned RA3 clusters or Serverless. Databricks SQL: Serverless SQL warehouses on the Databricks lakehouse, powered by the Photon C++ engine. Only Amazon Redshift has stored procedures. Only Databricks SQL has vector search, change data capture, time travel.

When should I choose Amazon Redshift over Databricks SQL?

Choose Amazon Redshift for enterprise data warehouse on AWS, BI dashboards over curated marts and lakehouse queries over S3 with Spectrum. Choose Databricks SQL for lakehouse SQL and BI on Delta Lake; consolidating a separate warehouse into Databricks; and dbt transformations on the lakehouse.

Is Amazon Redshift or Databricks SQL more popular?

Amazon Redshift ranks #21 on the WhichDB Score (55.8/100) and Databricks SQL ranks #147 (20.8/100). Popularity is not fit: check the requirements that matter for your project.

Are Amazon Redshift and Databricks SQL open source?

No. Amazon Redshift is proprietary, closed-source software. No. Databricks SQL is proprietary, closed-source software.