Free Databricks SQL & Delta Lake Formatter & Beautifier Online

Databricks SQL extends Spark SQL for lakehouse workloads and frequently mixes relational transformations with Delta Lake and semi-structured data features. A table reference can include a catalog and schema, a Delta relation can use TIMESTAMP AS OF or VERSION AS OF time travel, and a JSON or STRUCT field may be accessed with colon notation. LATERAL VIEW, EXPLODE, and array operations are common when a bronze event payload becomes a silver table.

This Databricks formatter makes those transformations easier to follow. Time-travel clauses stay beside their table references, colon paths and typed casts remain intact, and LATERAL VIEW generators are indented as row-producing sources. CTEs, window functions, GROUP BY, HAVING, and Delta-oriented expressions are separated at useful boundaries so a long lakehouse query reads as a sequence of data-shaping stages rather than one flat string.

The editor starts with a Delta time-travel query that extracts nested fields and explodes an array of tags before ranking active users. Use it for dbt models, notebooks, production SQL views, and code review. Formatting is performed without retaining ordinary drafts; use the Git-style diff to validate the output before copying it into a Databricks workspace or pipeline definition.

The sample keeps both the historical snapshot and the nested data transformation visible.

The URL-specific preset also makes the intended engine explicit for readers arriving from a search result. That is useful when a team supports both SparkSQL and Databricks SQL, since the similar-looking colon paths, generators, and time-travel clauses can otherwise be mistaken for interchangeable syntax.

The formatter keeps the visual sequence of a lakehouse transformation visible: choose a historical Delta snapshot, project nested fields, expand repeated data, aggregate, and rank. That sequence is often more important to a review than compact keyword alignment.