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Databricks SQL

Databricks SQL is SQL analytics built on top of the Databricks "lakehouse" platform, blending data-warehouse-style querying with massive raw-file data-lake storage. It sits around #20 in real-world usage (roughly 3.4%), popular with data engineering and ML teams already using Databricks/Spark.

📌 Quick facts
Type: Lakehouse SQL analytics
Made by: Databricks Inc.
License: Proprietary/managed-cloud (usage-based)
Hosting: Fully managed, multi-cloud
Primary use case: Data engineering/ML teams querying huge lakehouse datasets with SQL

Core concepts & example

Databricks SQL queries Delta Lake tables (an open storage format built on Parquet) with standard SQL:

SELECT customer_id, COUNT(*) AS orders
FROM delta.`/mnt/lake/orders`
WHERE order_date >= '2026-01-01'
GROUP BY customer_id;

Getting started

Databricks SQL is accessed through the Databricks workspace UI or its SQL connector — there's no local install, only a workspace account:

# Python: connect via the Databricks SQL connector
pip install databricks-sql-connector

🎯 Best for: data engineering and ML teams already working in the Databricks/Spark ecosystem who need SQL access to lakehouse data.