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Head-to-head · OLAP Software

Google BigQuery vs Apache Doris

  • Updated Oct 2026
  • Both researched from official sources
  • 4 checks side by side
Higher score Google BigQuery #1 in OLAP Software 9.4/10 Free plan Free plan✓ 0 of 3 features Try Google BigQuery
Apache Doris #6 in OLAP Software 8.9/10 Free plan Free plan✓ 3 of 3 features Visit Apache Doris

Google BigQuery leads on 0 checks, Apache Doris on 0, and 4 are even. Who comes out ahead on the 4 yes/no, price and count checks where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreGoogle BigQuery · 9.4/10
  • Free planboth
  • Most featuresApache Doris · 3 of 3

Google BigQuery scores higher on our rubric for olap software: 9.4 against 8.9 out of 10; our editors rank them #1 and #6.

Google BigQuery is the better fit for cloud-native analytics teams. Apache Doris is the better fit for teams wanting open-source OLAP.

  • Google BigQuery fits best

    Cloud-native analytics teams

  • Apache Doris fits best

    Teams wanting open-source OLAP

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Side by side

Feature Google BigQuery 9.4/10 Visit ↗ Apache Doris 8.9/10 Visit ↗
At a glance
Editor score 9.4 8.9
Ranking #1 in OLAP Software #6 in OLAP Software
Best for Cloud-native analytics teams Teams wanting open-source OLAP
Pricing model Free Free
Starting price Free Free plan
Free plan ✓ ✓
Free trial — —
Deployment Cloud Self-hosted
Support Docs, Live chat, Phone, Tickets, Community Community, Docs
Integrations 10 integrations 50+ integrations
Built for Small business, Mid-market, Enterprise Small business, Mid-market, Enterprise
Features Google BigQuery 0/3 · Apache Doris 3/3
Real-time ingestion Not checked yet ✓ (best)
Row-level security Not checked yet ✓ (best)
Embedded analytics Not checked yet ✓ (best)
Specs
Deployment Not checked yet Self_hosted
Query language Not checked yet Sql
Included users Not checked yet Not checked yet
Data source coverage Not checked yet databases, streaming, lakehouse, BI, AI, observability
Our review
Pros
  • Separate, independently scalable storage and compute
  • SQL-based BigQuery ML, vector search, and generative AI functions
  • Federated queries, streaming ingestion, CDC, and broad integrations
  • Real-time streaming and CDC ingestion with MPP query execution
  • Federated queries across databases, catalogs, and lakehouse sources
  • Materialized views, search, row-level policies, and embedded analytics
Cons
  • Usage-based costs vary by region and operation
  • Additional storage, ingestion, and extraction charges can apply
  • Cloud deployment limits it to web and API-based workflows
  • Self-hosted deployment places operational responsibility on the team
  • Broad capabilities may require more architecture than focused BI tools
  • No product pricing tiers are presented for the open-source deployment
Our verdict

Google BigQuery is a fully managed, serverless cloud data warehouse for organizations that store, query, and analyze large datasets. It supports data engineering, business intelligence, data science, real-time analytics, and migration from…

Read the review →

Apache Doris is an open-source, MPP-based analytical database for teams building real-time analytics, data warehouses, lakehouse queries, observability workloads, and AI applications. It uses standard SQL through MySQL protocol…

Read the review →
  1. Google BigQueryOLAP Software 9.4Free plan
  2. Apache DorisOLAP Software 8.9Free plan

Strengths and trade-offs

  • Google BigQuery — where it wins

    • Separate, independently scalable storage and compute
    • SQL-based BigQuery ML, vector search, and generative AI functions
    • Federated queries, streaming ingestion, CDC, and broad integrations

    Where it doesn't

    • Usage-based costs vary by region and operation
    • Additional storage, ingestion, and extraction charges can apply
    • Cloud deployment limits it to web and API-based workflows

  • Apache Doris — where it wins

    • Real-time streaming and CDC ingestion with MPP query execution
    • Federated queries across databases, catalogs, and lakehouse sources
    • Materialized views, search, row-level policies, and embedded analytics

    Where it doesn't

    • Self-hosted deployment places operational responsibility on the team
    • Broad capabilities may require more architecture than focused BI tools
    • No product pricing tiers are presented for the open-source deployment

More comparisons

All OLAP software comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update