Databricks vs Snowflake: The Differences a Data Engineer Is Asked About, and Which to Learn First

What is the difference between Databricks and Snowflake?

Snowflake stores your data itself, in its own compressed micro-partitions, and you query it with SQL through virtual warehouses that scale up and down independently of storage. Databricks stores nothing of yours: it runs Spark clusters over files in your own cloud storage, usually as Delta Lake tables, and adds notebooks, jobs, SQL warehouses and Unity Catalog on top. One is a warehouse that grew a data science layer; the other is a compute engine that grew a warehouse. The overlap is real. Snowflake runs Python through Snowpark and reads Iceberg tables; Databricks runs plain SQL through SQL warehouses. The distinction interviewers want is where the data lives and what engine touches it.

How is each one priced, in principle?

Snowflake bills credits for the seconds a virtual warehouse runs, plus storage per terabyte; stop the warehouse and the compute bill stops. Databricks bills Databricks Units for cluster time plus whatever your cloud charges for the machines and storage underneath, so there are two bills. The engineering consequence is the interview question: on Snowflake you size and auto-suspend warehouses; on Databricks you size clusters, use autoscaling and job clusters, and keep the small-file problem from inflating the read cost. No figures here, because both change their pricing and publish it.

Which interview questions compare the two?

The comparison appears in three forms: define the difference, pick one for a scenario, and explain a mechanism each has a different name for. Answers that name the mechanism win.

Which should you learn first in India?

Both appear in Indian data engineering postings, and the split follows the cloud: Azure-first employers, which is most Indian services companies, run Azure Databricks next to Data Factory; analytics-first teams and many product companies run Snowflake, often with dbt. Learn the one your target postings repeat. If they are split, Snowflake gets you productive faster and Databricks gets you deeper, and the cohort teaches both: Snowflake in the Modern Data Engineering module, Databricks and Delta Lake in the Big Data and Apache Spark module.

Is Databricks better than Snowflake?

Neither is better; they started from different ends. Snowflake is the easier, faster place for SQL analytics over data it manages. Databricks is the stronger place for Spark pipelines, streaming and machine learning over data you keep in open formats. Most large companies end up with both, which is why interviews ask you to compare them rather than pick a winner.

Can Snowflake do machine learning, and can Databricks do warehousing?

Yes to both, with caveats. Snowflake runs Python through Snowpark and hosts models, but heavy training usually happens elsewhere. Databricks SQL warehouses serve BI tools well, but the platform assumes engineers who are comfortable with clusters and files. The overlap grows every release; the centre of gravity of each has not moved.

Which is cheaper, Databricks or Snowflake?

It depends on the workload and on how well it is run, and both publish and change their prices, so this page quotes none. The structural difference is that Snowflake is one bill for credits and storage, while Databricks is DBUs plus your cloud's compute and storage. In both, the largest cost is compute left running; auto-suspend on Snowflake and job clusters with autoscaling on Databricks are the levers an interviewer wants to hear.

Do I need to learn Spark for Snowflake?

No. Snowflake is SQL-first, and most Snowflake roles are SQL plus dbt plus a loading tool. Spark matters for Databricks, where it is the engine, and for any role that mentions PySpark. If you are choosing what to learn with no target employer yet, SQL to window-function level serves both, and PySpark is the second step.

Which one do Indian data engineering job descriptions mention more?

Both appear, and the split follows the cloud rather than the platform: Azure-heavy employers, which is most Indian services companies, list Azure Databricks; analytics-led and product teams more often list Snowflake. Read five postings from the companies you want and count; that number beats any general claim.