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Databricks

Serverless SQL warehouse on the Databricks lakehouse with Unity Catalog

Overview

Databricks SQL is the data-warehousing product of Databricks, which describes its platform as an open, serverless lakehouse that unifies data and natively integrates AI[4]. It runs SQL warehouses over open table formats in the customer’s cloud storage, with governance through Unity Catalog, which manages assets and access centrally and enforces row- and column-level security and attribute-based access control[4].

Three warehouse types exist. Serverless warehouses support all performance features, including the Photon engine, Predictive IO and Intelligent Workload Management; Pro warehouses support Photon and Predictive IO and are used where serverless is unavailable or custom networking is needed; Classic supports Photon only[5]. Other features include AI/BI dashboards with natural-language querying, an AI-assisted SQL editor, materialized views, Lakehouse Federation for querying MySQL, PostgreSQL, Snowflake and BigQuery in place, and Genie Migrate for AI-assisted migrations[4]. Databricks claims queries are five times faster than three years ago; this is a vendor claim[4].

Pricing is pay-as-you-go in Databricks units billed per second, with a 14-day trial and committed-use discounts by negotiation; the per-DBU rates did not load on the pricing page at the time of access[6]. Databricks SQL suits organisations already using Databricks for data engineering and AI; its drawback for pure BI teams is a broader platform to learn and administer.

Databricks features

  • Serverless, Pro and Classic SQL warehouses
  • Photon engine and Predictive IO
  • Unity Catalog governance
  • AI/BI and natural-language queries
  • Lakehouse Federation
  • Open table formats

Databricks pricing

Pay-as-you-go in DBUs, billed per second; 14-day free trial; committed-use discounts via sales. Per-DBU rates shown in Databricks' pricing calculator.

Databricks strengths and limitations

Strengths

  • Largest analytics platform in this list by disclosed revenue figure
  • Single platform spanning ETL, SQL warehousing and model development
  • Consumption pricing scales down for small workloads
  • Single platform for ETL, ML and SQL
  • Open formats in customer storage
  • Strong governance with Unity Catalog

Limitations

  • Revenue figures are company-reported run-rates, not audited accounts
  • Getting value usually requires data engineering skills in-house or through a partner
  • Usage-based costs can be hard to forecast without governance
  • Broad platform with steeper learning curve
  • DBU pricing harder to forecast
  • Best features need serverless availability

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