Skip to main content

9. Query and explore

You'll learn how to query and explore your lakehouse data using the Databricks SQL Editor in ~5 min.

Prereqs: Automation & Orchestration

Why this matters​

A pipeline that writes data is only half the story. Analysts and engineers need to query that data interactively — validate transformations, explore distributions, and answer ad-hoc questions. The Databricks SQL Editor paired with a serverless warehouse gives you a zero-infrastructure query environment that starts in seconds.

Journey checklist​

  • Get started.
  • Before you start.
  • Infra setup.
  • Cost monitoring.
  • Data Governance Strategy.
  • Access your data.
  • Build the first pipeline.
  • Automation and orchestration.
  • Query and explore.
  • Databricks AI/BI.
  • Business semantics.

How it works​

Every workspace comes with a Starter Serverless Warehouse — a managed compute endpoint optimized for interactive SQL. Open the SQL Editor, write a query, and run it against any table your identity has access to through Unity Catalog grants.

tip

You can drag and drop tables from the schema browser directly into the editor. No need to type fully qualified names by hand.

Video walkthrough​

When to use / when not to​

Use the SQL Editor when:

  • You need to validate pipeline output or inspect table contents.
  • You want to prototype a query before embedding it in a dashboard or notebook.
  • You are running one-off exploratory analysis.

Use notebooks instead when:

  • You need Python, Scala, or R alongside SQL.
  • The analysis spans multiple steps that benefit from cell-by-cell execution.

Common pitfalls​

  • Running queries on a pro warehouse when serverless is available — serverless warehouses start faster and scale automatically. Use them for interactive work unless your admin has configured a specific warehouse for your team.
  • Querying without schema context — always select a catalog and schema in the editor's context bar. Without it, every query needs fully qualified three-part names (catalog.schema.table).

Next​