Redshift vs Synapse vs BigQuery — Data Warehouses Compared
Compare AWS Redshift, Azure Synapse Analytics, and Google BigQuery for cloud data warehousing. Pricing, performance, and architecture.
Feature Comparison
| Feature | Amazon Redshift | Azure Synapse Analytics | Google BigQuery |
|---|---|---|---|
| Serverless option | Redshift Serverless | Serverless SQL pool | Always serverless |
| Free tier | 2-month trial | No | 1TB queries + 10GB storage/mo |
| ML integration | Via SageMaker | Via Azure ML | BigQuery ML (built-in) |
| Streaming ingest | Kinesis + Redshift Streaming | Event Hubs + Synapse Link | BigQuery Storage Write API |
Service Details
Amazon Redshift
Petabyte-scale data warehouse with Redshift Serverless for on-demand analytics. RA3 nodes separate compute from storage.
- Mature ecosystem with deep AWS integration
- Serverless option for intermittent workloads
- RA3 nodes decouple compute/storage
- Redshift Spectrum queries S3 directly
- Cluster management overhead (non-serverless)
- Concurrency scaling costs can surprise
- Complex pricing across node types
Azure Synapse Analytics
Unified analytics platform combining data warehousing, big data, and data integration. Serverless SQL pool for ad-hoc queries.
- Unified analytics workspace
- Serverless SQL for ad-hoc S3/Blob queries
- Deep Power BI integration
- Apache Spark built-in
- Dedicated pool pricing is expensive
- Complex architecture to understand
- Performance tuning less straightforward
Google BigQuery
Fully serverless data warehouse with no infrastructure management. Slot-based pricing or pay-per-query model.
- True serverless (zero management)
- Best-in-class for ad-hoc SQL analytics
- ML built-in (BigQuery ML)
- Generous free tier
- On-demand pricing unpredictable for heavy use
- Slot-based capacity requires planning
- Less control over query optimization
When to Use Which
Choose BigQuery for zero-management serverless analytics with generous free tier. Choose Redshift for mature AWS ecosystem integration and RA3 flexibility. Choose Synapse for unified analytics with Power BI and Spark in one workspace.
Data warehouse costs can grow rapidly. CloudExpat helps optimize slot/RPU reservations, identifies expensive queries, and recommends commitment levels based on actual usage patterns.
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