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Amazon Athena vs Google BigQuery

Amazon Athena: Serverless AWS query service running Trino-based SQL directly over data in S3. Google BigQuery: Google's serverless, pay-per-query data warehouse built on Dremel and Colossus.

key differences

  • Amazon Athena is primarily a relational database; Google BigQuery is columnar / OLAP.
  • Transactions: Amazon Athena offers limited ACID; Google BigQuery offers full.
  • Only Google BigQuery has transactions, full-text search, vector search, change data capture, stored procedures.

choose amazon-athena for

  • +Ad-hoc SQL over S3 logs (CloudTrail, ALB, VPC flow)
  • +Serverless lakehouse queries on Iceberg
  • +Lightweight BI on curated S3 data

best at · Lakehouse, OLAP, Warehouse

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

overview
Primary model
Relational
Columnar / OLAP
Also supports
Columnar / OLAP
Relational, Document, Vector, Spatial
Type
Managed service
Managed service
Status
Active
Active
Developer
Amazon Web Services
Google
First release
2016
2010
Latest version
–
–
License
ProprietaryProprietary
ProprietaryProprietary
Free tier
—
✓
popularity
WhichDB Score
19.5
65.3
Rank
#166
#12
GitHub stars
–
–
Stack Overflow questions
3.6K
26.1K
scale & guarantees
Comfortable data size
Massive (Multi-PB)
Massive (Multi-PB)
Horizontal scaling
Native
Native
Multi-region
None
Read replicas
ACID
Limited
Full
Consistency
Strong
Strong
Schema
Strict
Flexible
Architecture
Serverless, Distributed
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 (Trino dialect), PySpark
GoogleSQL, Legacy SQL, Python (BigQuery DataFrames)
Client languages
Python, Java, JavaScript, Go, .NET
Python, Java, Go, JavaScript, C#, Ruby, PHP
Best for
Lakehouse, OLAP, Warehouse
Warehouse, OLAP, Lakehouse, Real-time analytics

Amazon Athena vs Google BigQuery: FAQ

What is the difference between Amazon Athena and Google BigQuery?

Amazon Athena: Serverless AWS query service running Trino-based SQL directly over data in S3. Google BigQuery: Google's serverless, pay-per-query data warehouse built on Dremel and Colossus. Amazon Athena is primarily a relational database; Google BigQuery is columnar / OLAP. Transactions: Amazon Athena offers limited ACID; Google BigQuery offers full. Only Google BigQuery has transactions, full-text search, vector search, change data capture, stored procedures.

When should I choose Amazon Athena over Google BigQuery?

Choose Amazon Athena for ad-hoc SQL over S3 logs (CloudTrail, ALB, VPC flow); serverless lakehouse queries on Iceberg; and lightweight BI on curated S3 data. Choose Google BigQuery for cloud data warehouse on GCP; marketing and product analytics (GA4 export); and log and event analytics.

Is Amazon Athena or Google BigQuery more popular?

Google BigQuery ranks #12 on the WhichDB Score (65.3/100) and Amazon Athena ranks #166 (19.5/100). Popularity is not fit: check the requirements that matter for your project.

Are Amazon Athena and Google BigQuery open source?

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