Databricks SQL Warehouse vs Apache Impala

Databricks SQL Warehouse9.1Visit AIApache Impala8.6Visit

Higher score Databricks SQL Warehouse #2 in Query Engine Software 9.1/10 Paid ✓ 0 of 4 features Visit Databricks

vs

AI Apache Impala #7 in Query Engine Software 8.6/10 Free plan Free plan✓ 0 of 4 features Visit Apache Impala

• Databricks SQL Warehouse fits best

  Enterprise lakehouse SQL and BI

• Apache Impala fits best

  Interactive Hadoop-lake SQL

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Side by side

Feature	Databricks SQL Warehouse 9.1/10 Visit ↗	AIApache Impala 8.6/10 Visit ↗
At a glance
Editor score	9.1	8.6
Ranking	#2 in Query Engine Software	#7 in Query Engine Software
Best for	Enterprise lakehouse SQL and BI	Interactive Hadoop-lake SQL
Pricing model	Paid	Free
Starting price	—	Free plan
Free plan	—	✓ (best)
Free trial	—	—
Deployment	Cloud	Self-hosted
Platforms	Web	Linux
Support	Community, Docs	Docs
Integrations	15 integrations	9 integrations
Built for	Mid-market, Enterprise	Mid-market, Enterprise
Features Databricks SQL Warehouse 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	Elastic serverless SQL warehouses with usage-based compute AI-assisted SQL, dashboards, alerts, and scheduled jobs Broad BI, database, ETL, and federation integrations	Interactive distributed SQL for analytic workloads Hive compatibility with shared Hive Metastore metadata Broad storage, format, JDBC, and ODBC support
Cons	No free plan for lightweight or occasional experimentation Usage-based billing can make spend vary with workloads Cloud deployment may not suit teams needing self-hosted software	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	Databricks SQL Warehouse is a cloud data warehousing service built on the Databricks lakehouse. It lets analysts and data teams query cloud data lakes with SQL while adding AI-assisted authoring, interactive AI/BI dashboards, scheduled… 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 →

Strengths and trade-offs

• Databricks SQL Warehouse — where it wins

  • Elastic serverless SQL warehouses with usage-based compute
  • AI-assisted SQL, dashboards, alerts, and scheduled jobs
  • Broad BI, database, ETL, and federation integrations

  Where it doesn't

  • No free plan for lightweight or occasional experimentation
  • Usage-based billing can make spend vary with workloads
  • Cloud deployment may not suit teams needing self-hosted software

• 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

• Databricks SQL Warehouse9.1/10 · Paid

  A flexible SQL warehouse for enterprise lakehouse analytics, BI, and governed data access.

  Visit DatabricksFull verdict →
• AIApache Impala8.6/10 · Free plan

  A strong fit for interactive SQL on self-hosted Hadoop and lake data.

  Visit Apache ImpalaFull verdict →