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Head-to-head · Query Engine Software

Google BigQuery vs Apache Impala

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score Google BigQuery #1 in Query Engine Software 9.3/10 Free plan Free plan✓ 0 of 4 features Try Google BigQuery
Apache Impala #7 in Query Engine Software 8.6/10 Free plan Free plan✓ 0 of 4 features Visit Apache Impala

Google BigQuery leads on 0 checks, Apache Impala on 0, and 1 is even. Who comes out ahead on the 1 yes/no, price and count check where we have data for both products. The editor score weighs everything else too.

Our verdict

  • Highest scoreGoogle BigQuery · 9.3/10
  • Free planboth

Google BigQuery scores higher on our rubric for query engine software: 9.3 against 8.6 out of 10; our editors rank them #1 and #7.

Google BigQuery is the better fit for broad, low-friction cloud analytics. Apache Impala is the better fit for interactive Hadoop-lake SQL.

  • Google BigQuery fits best

    Broad, low-friction cloud analytics

  • Apache Impala fits best

    Interactive Hadoop-lake SQL

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Side by side

Feature Google BigQuery 9.3/10 Visit ↗ Apache Impala 8.6/10 Visit ↗
At a glance
Editor score 9.3 8.6
Ranking #1 in Query Engine Software #7 in Query Engine Software
Best for Broad, low-friction cloud analytics Interactive Hadoop-lake SQL
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 Docs
Integrations 10 integrations 9 integrations
Built for Small business, Mid-market, Enterprise Mid-market, Enterprise
Features Google BigQuery 0/4 · Apache Impala 0/4
Federated queries Not checked yet Not checked yet
Heterogeneous sources Not checked yet Not checked yet
Result caching Not checked yet Not checked yet
Streaming sources Not checked yet Not checked yet
Specs
Deployment Not checked yet Self_hosted
SQL support Not checked yet Not checked yet
Source connectors Not checked yet Not checked yet
Our review
Pros
  • Serverless SQL with storage and compute that scale independently
  • Federated queries, streaming ingestion, and data lake support
  • Built-in ML, governance, vector search, and generative AI functions
  • Interactive distributed SQL for analytic workloads
  • Hive compatibility with shared Hive Metastore metadata
  • Broad storage, format, JDBC, and ODBC support
Cons
  • Usage-based pricing can vary by query, storage, region, and operation
  • Capacity-based editions add planning beyond the free tier
  • Cloud-only deployment may not suit teams seeking local infrastructure
  • Self-hosted deployment on Linux requires cluster management
  • Best suited to Hadoop-centered data environments
  • Kudu capabilities depend on the Apache Kudu integration
Our verdict

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

Read the review →

Apache Impala is an open-source distributed analytic database for interactive SQL queries over Hadoop ecosystem data. It is designed for data analysts, engineers, and organizations operating large-scale data platforms, with support for…

Read the review →
  1. Google BigQueryQuery Engine Software 9.3Free plan
  2. Apache ImpalaQuery Engine Software 8.6Free plan

Strengths and trade-offs

  • Google BigQuery — where it wins

    • Serverless SQL with storage and compute that scale independently
    • Federated queries, streaming ingestion, and data lake support
    • Built-in ML, governance, vector search, and generative AI functions

    Where it doesn't

    • Usage-based pricing can vary by query, storage, region, and operation
    • Capacity-based editions add planning beyond the free tier
    • Cloud-only deployment may not suit teams seeking local infrastructure

  • Apache Impala — where it wins

    • Interactive distributed SQL for analytic workloads
    • Hive compatibility with shared Hive Metastore metadata
    • Broad storage, format, JDBC, and ODBC support

    Where it doesn't

    • Self-hosted deployment on Linux requires cluster management
    • Best suited to Hadoop-centered data environments
    • Kudu capabilities depend on the Apache Kudu integration

More comparisons

All query engine software comparisons → · Full ranking →

Reviewed by iTechGuides Editors · Editorial team · Updated Oct 2026

Last updated · How we research and update