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Head-to-head · OLAP Software

Google BigQuery vs Apache Druid

  • Updated Oct 2026
  • Both researched from official sources
  • 2 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 Druid #7 in OLAP Software 8.8/10 Open source ✓ 1 of 3 features Visit Apache Druid

Google BigQuery leads on 0 checks, Apache Druid on 0, and 2 are even. Who comes out ahead on the 2 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 planonly Google BigQuery
  • Most featuresApache Druid · 1 of 3

Google BigQuery scores higher on our rubric for olap software: 9.4 against 8.8 out of 10; our editors rank them #1 and #7.

Google BigQuery is the better fit for cloud-native analytics teams. Apache Druid is the better fit for real-time event analytics.

  • Google BigQuery fits best

    Cloud-native analytics teams

  • Apache Druid fits best

    Real-time event analytics

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Side by side

Feature Google BigQuery 9.4/10 Visit ↗ Apache Druid 8.8/10 Visit ↗
At a glance
Editor score 9.4 8.8
Ranking #1 in OLAP Software #7 in OLAP Software
Best for Cloud-native analytics teams Real-time event analytics
Pricing model Free Free
Starting price Free Open source
Free plan ✓ (best) Not checked yet
Free trial — —
Deployment Cloud Self-hosted
Platforms Web Web
Support Docs, Live chat, Phone, Tickets, Community Community, Docs
Integrations 10 integrations 6 integrations
Built for Small business, Mid-market, Enterprise Mid-market, Enterprise
Features Google BigQuery 0/3 · Apache Druid 1/3
Real-time ingestion Not checked yet ✓ (best)
Row-level security Not checked yet Not checked yet
Embedded analytics Not checked yet Not checked yet
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 streaming sources, object stores, databases, files
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
  • Sub-second OLAP queries for high-cardinality, high-dimensional data
  • Independent scaling for ingestion, queries, and orchestration
  • Streaming, batch loading, SQL, native queries, joins, and bitmap indexes
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 requires organizations to manage the platform
  • Narrower scope than general cloud data warehouses
  • Row-level security is not verified in the published security documentation
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 Druid is an open-source, distributed analytics database for large, event-oriented datasets. It is designed for teams building analytical application interfaces and high-concurrency APIs that need sub-second aggregations over…

Read the review →
  1. Google BigQueryOLAP Software 9.4Free plan
  2. Apache DruidOLAP Software 8.8Open source

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 Druid — where it wins

    • Sub-second OLAP queries for high-cardinality, high-dimensional data
    • Independent scaling for ingestion, queries, and orchestration
    • Streaming, batch loading, SQL, native queries, joins, and bitmap indexes

    Where it doesn't

    • Self-hosted deployment requires organizations to manage the platform
    • Narrower scope than general cloud data warehouses
    • Row-level security is not verified in the published security documentation

More comparisons

All OLAP software comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update