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Head-to-head · Query Engine Software

Trino vs Apache Impala

  • Updated Oct 2026
  • Both researched from official sources
  • 1 check side by side
Higher score Trino #3 in Query Engine Software 9.0/10 Free plan Free plan✓ 0 of 4 features Visit Trino
Apache Impala #7 in Query Engine Software 8.6/10 Free plan Free plan✓ 0 of 4 features Visit Apache Impala

Trino 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 scoreTrino · 9.0/10
  • Free planboth

Trino scores higher on our rubric for query engine software: 9.0 against 8.6 out of 10; our editors rank them #3 and #7.

Trino is the better fit for open-source federated SQL infrastructure. Apache Impala is the better fit for interactive Hadoop-lake SQL.

  • Trino fits best

    Open-source federated SQL infrastructure

  • Apache Impala fits best

    Interactive Hadoop-lake SQL

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Side by side

Feature Trino 9.0/10 Visit ↗ Apache Impala 8.6/10 Visit ↗
At a glance
Editor score 9.0 8.6
Ranking #3 in Query Engine Software #7 in Query Engine Software
Best for Open-source federated SQL infrastructure Interactive Hadoop-lake SQL
Pricing model Free Free
Starting price Free plan Free plan
Free plan ✓ ✓
Free trial — —
Deployment Cloud, Self-hosted Self-hosted
Platforms Linux Linux
Support Community, Docs Docs
Integrations 6 integrations 9 integrations
Built for Mid-market, Enterprise Mid-market, Enterprise
Features Trino 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
  • Queries and joins data across databases, warehouses, object storage, and streams
  • Distributed execution supports interactive analytics, ETL, and high-volume applications
  • Fine-grained controls include row filtering and column masking through Ranger
  • Interactive distributed SQL for analytic workloads
  • Hive compatibility with shared Hive Metastore metadata
  • Broad storage, format, JDBC, and ODBC support
Cons
  • Deployment and operations require self-hosted infrastructure or managed cloud setup
  • Materialized views require explicit refresh commands
  • Query-result caching is connector-specific rather than engine-wide
  • 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

Trino is an open-source, distributed SQL query engine for analyzing data across multiple systems without copying it into one store. It is suited to data teams building federated analytics, centralized data access, batch ETL, interactive…

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. TrinoQuery Engine Software 9.0Free plan
  2. Apache ImpalaQuery Engine Software 8.6Free plan

Strengths and trade-offs

  • Trino — where it wins

    • Queries and joins data across databases, warehouses, object storage, and streams
    • Distributed execution supports interactive analytics, ETL, and high-volume applications
    • Fine-grained controls include row filtering and column masking through Ranger

    Where it doesn't

    • Deployment and operations require self-hosted infrastructure or managed cloud setup
    • Materialized views require explicit refresh commands
    • Query-result caching is connector-specific rather than engine-wide

  • 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