Google BigQuery vs StarRocks

Google BigQuery9.4Visit StarRocks8.8Visit

Higher score Google BigQuery #1 in Columnar Databases 9.4/10 Free plan Free plan✓ 0 of 3 features Try Google BigQuery

vs

StarRocks #5 in Columnar Databases 8.8/10 Free plan Free plan✓ 0 of 3 features Visit StarRocks

• Google BigQuery fits best

  Serverless analytics and lakehouse teams

• StarRocks fits best

  Open-source lakehouse SQL analytics

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Side by side

Feature	Google BigQuery 9.4/10 Visit ↗	StarRocks 8.8/10 Visit ↗
At a glance
Editor score	9.4	8.8
Ranking	#1 in Columnar Databases	#5 in Columnar Databases
Best for	Serverless analytics and lakehouse teams	Open-source lakehouse SQL analytics
Pricing model	Free	Free
Starting price	Free	Free plan
Free plan	✓	✓
Free trial	—	—
Deployment	Cloud	Self-hosted
Platforms	Web	Linux
Support	Docs, Live chat, Phone, Tickets, Community	Community, Docs
Integrations	10 integrations	20 integrations
Built for	Small business, Mid-market, Enterprise	Small business, Mid-market, Enterprise
Features Google BigQuery 0/3 · StarRocks 0/3
Managed service	Not checked yet	Not checked yet
Object storage support	Not checked yet	Not checked yet
Streaming ingestion	Not checked yet	Not checked yet
Specs
Deployment model	Cloud	Not checked yet
Supported data formats	Not checked yet	Not checked yet
Included storage	Not checked yet	Not checked yet
Our review
Pros	Separates storage and compute for independent scaling Queries lake data and external sources through federated access Combines streaming, SQL-based ML, governance, and AI features	Vectorized MPP execution with cost-based query optimization Real-time upserts, deletes, and streaming or batch loading Queries data lakes through external catalogs without migration
Cons	Usage-based pricing varies by data scanned, storage, region, and operation Capacity-based editions require planning around query processing Cloud deployment may not suit teams needing another deployment model	Self-hosted deployment places infrastructure work on your team Primarily designed for analytical rather than general transactional workloads Managed SaaS users may prefer a hosted database alternative
Our verdict	Google BigQuery is a fully managed, serverless cloud data warehouse for organizations that store, query, and analyze large datasets. It is aimed at data engineering, business intelligence, data science, real-time analytics, and warehouse… Read the review →	StarRocks is an open-source massively parallel processing database for OLAP, real-time analytics, high-concurrency workloads, ad-hoc queries, and unified analytics. It is aimed at engineering and data teams building analytical warehouses,… Read the review →

Strengths and trade-offs

• Google BigQuery — where it wins

  • Separates storage and compute for independent scaling
  • Queries lake data and external sources through federated access
  • Combines streaming, SQL-based ML, governance, and AI features

  Where it doesn't

  • Usage-based pricing varies by data scanned, storage, region, and operation
  • Capacity-based editions require planning around query processing
  • Cloud deployment may not suit teams needing another deployment model

• StarRocks — where it wins

  • Vectorized MPP execution with cost-based query optimization
  • Real-time upserts, deletes, and streaming or batch loading
  • Queries data lakes through external catalogs without migration

  Where it doesn't

  • Self-hosted deployment places infrastructure work on your team
  • Primarily designed for analytical rather than general transactional workloads
  • Managed SaaS users may prefer a hosted database alternative

• Google BigQuery9.4/10 · Free plan

  A flexible serverless warehouse for teams combining lakehouse analytics, streaming, and ML.

  Try Google BigQueryFull verdict →
• StarRocks8.8/10 · Free plan

  A strong fit for teams building self-hosted, open-source lakehouse analytics.

  Visit StarRocksFull verdict →