By Vaishnavi Medikundam, HEXstream data analyst

If you’ve spent most of your career writing SQL, whether in Snowflake or a traditional database, Databricks will feel familiar in many ways. The syntax is largely standard, the underlying logic is similar, and you can become productive quickly.

Here’s what I wish I’d known when I was coming in as someone who thinks in SQL first and was just starting to work with Databricks.

The Good News: Your SQL Still Works

Databricks SQL is built on Apache Spark and supports a dialect that is very close to ANSI SQL. Most of the queries we write day to day, including SELECT, JOIN, GROUP BY
functions, CTEs, and subqueries, will run without modification. If you have a library of SQL you’ve written elsewhere, a large portion of it will just work.

Databricks also has a dedicated SQL Editor, found under SQL in the left navigation, that feels like a standard query tool. You write SQL, run it, and see the results in a table. No notebooks, Python, or cluster configuration is required to get started.

SQL Warehouses Are the Starting Point

In traditional databases, you connect to a server and run queries. In Databricks, SQL queries run on a SQL Warehouse, which is a compute resource optimized specifically for SQL workloads. Consider it a database engine that lives in the cloud and runs only when you need it.

A few things are worth keeping in mind:

·      The first query after a warehouse has been idle can take 30 to 60 seconds while it warms up. This is normal. Once the warehouse is running, queries run quickly.

·      If your workspace has serverless SQL Warehouses available, use them. They start faster, and you don’t have to think about sizing.

·      Different teams or workloads can use different warehouses. If you’re running heavy queries, you can size up without affecting your team members.

A Few SQL Differences Worth Knowing

DESCRIBE TABLE: Before querying an unfamiliar table, run DESCRIBE TABLE my_table to see the schema, or DESCRIBE DETAIL my table for specific metadata like file location and table size.

Case sensitivity: Databricks SQL is case-insensitive for column names by default, but catalog and schema names can behave differently depending on the Unity Catalog settings. When in doubt, use lowercase consistently.

Date functions have some quirks

Spark SQL handles date and timestamp types slightly differently than other databases. If you’re doing date math and getting unexpected results, check whether your column is a DATE, TIMESTAMP, or STRING; it matters more here than in some other systems.

When You’ll Want to Step Outside SQL

SQL gets you a long way in Databricks, but there are moments when using Python is the right call:

·       Complex data transformations that would be heavy in SQL

·       Reading from external APIs or nonstandard file formats

·       Building reusable functions or libraries

·       Writing multistep pipelines with conditional logic

The good news is that in a notebook, you can write SQL in one cell and Python in the next. You don’t have to pick one; you can use whichever is cleaner for the task at hand.

Final Thought

Databricks is genuinely SQL friendly, and if SQL is your primary language, you'll feel at home faster than you expect. The main thing to adopt early is the SQL Warehouse model. Once that clicks, the rest will follow naturally.

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