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Amazon Redshift vs Google BigQuery

Amazon Redshift: AWS's MPP columnar data warehouse, available as provisioned RA3 clusters or Serverless. Google BigQuery: Google's serverless, pay-per-query data warehouse built on Dremel and Colossus.

key differences

  • Only Google BigQuery has full-text search, vector search, change data capture, ttl / expiry, 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 google-bigquery for

  • +Cloud data warehouse on GCP
  • +Marketing and product analytics (GA4 export)
  • +Log and event analytics
  • +Ad-hoc exploration of very large datasets

best at · Warehouse, OLAP, Lakehouse

Amazon RedshiftAWS's MPP columnar data warehouse, available as provisioned RA3 clusters or Serverless.
Google BigQueryGoogle's serverless, pay-per-query data warehouse built on Dremel and Colossus.
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
Google
First release
2012
2010
Latest version
–
–
License
ProprietaryProprietary
ProprietaryProprietary
Free tier
—
✓
popularity
WhichDB Score
55.8
65.3
Rank
#21
#12
GitHub stars
–
–
Stack Overflow questions
8.8K
26.1K
scale & guarantees
Comfortable data size
Massive (Multi-PB)
Massive (Multi-PB)
Horizontal scaling
Native
Native
Multi-region
Read replicas
Read replicas
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
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
GoogleSQL, Legacy SQL, Python (BigQuery DataFrames)
Client languages
Python, Java, JavaScript, Go, .NET, C
Python, Java, Go, JavaScript, C#, Ruby, PHP
Best for
Warehouse, OLAP, Lakehouse
Warehouse, OLAP, Lakehouse, Real-time analytics
Add more to this comparisonNot sure either fits? Try smart match

Amazon Redshift vs Google BigQuery: FAQ

What is the difference between Amazon Redshift and Google BigQuery?

Amazon Redshift: AWS's MPP columnar data warehouse, available as provisioned RA3 clusters or Serverless. Google BigQuery: Google's serverless, pay-per-query data warehouse built on Dremel and Colossus. Only Google BigQuery has full-text search, vector search, change data capture, ttl / expiry, time travel.

When should I choose Amazon Redshift over Google BigQuery?

Choose Amazon Redshift for enterprise data warehouse on AWS, BI dashboards over curated marts and lakehouse queries over S3 with Spectrum. Choose Google BigQuery for cloud data warehouse on GCP; marketing and product analytics (GA4 export); and log and event analytics.

Is Amazon Redshift or Google BigQuery more popular?

Google BigQuery ranks #12 on the WhichDB Score (65.3/100) and Amazon Redshift ranks #21 (55.8/100). Popularity is not fit: check the requirements that matter for your project.

Are Amazon Redshift and Google BigQuery open source?

No. Amazon Redshift is proprietary, closed-source software. No. Google BigQuery is proprietary, closed-source software.