Databricks vs Snowflake: what’s the actual difference?

Where each one came from

Databricks (covered in more depth in our what is Databricks explainer) grew out of Apache Spark and the data-science and engineering world: notebooks, distributed processing, and — more recently — machine learning and AI model development, all built on the “lakehouse” architecture it popularised. Snowflake (see our what is Snowflake explainer) grew out of the SQL data-warehousing world: structured tables, fast analytical queries, and an architecture that separates storage from compute so each can scale on its own. Neither history is a value judgement — they simply reflect the problem each company set out to solve first.

Where that heritage still shows

Databricks still tends to feel most natural to teams doing data engineering, data science or machine learning work — writing code in notebooks, running Spark-based pipelines, and training or serving models alongside the data that feeds them. Snowflake still tends to feel most natural to teams whose primary need is SQL analytics and business intelligence — dashboards, reporting, and ad-hoc querying by analysts who may never write a line of Spark code. Neither platform is limited to its origin any more, but the tooling, defaults and surrounding ecosystem of each still lean toward the workload it was built for first.

Where they’ve converged

Both platforms have spent years closing the gap. Databricks has invested heavily in SQL-based analytics and warehouse-style performance for BI workloads, while Snowflake has added support for more flexible, semi-structured data, Python-based data processing, and machine-learning-oriented features that used to be Databricks territory. As a result, a task that once clearly belonged to one platform — say, running a SQL dashboard, or training a model on raw data — can now often be done reasonably well on either. Our data lake vs data warehouse explainer and what is a data lakehouse explainer cover the two architectural patterns this convergence blurs together.

A compact comparison

Neither column below is “winning” — they describe emphasis, not a verdict:

• Origin — Databricks: Apache Spark and the lakehouse. Snowflake: SQL cloud data warehousing.
• Strongest early fit — Databricks: data engineering, data science, ML/AI. Snowflake: BI and SQL analytics.
• Primary interface — Databricks: notebooks and code (Python, SQL, Scala). Snowflake: SQL, with growing support for other languages.
• Architecture — Databricks: lakehouse over open file formats. Snowflake: warehouse with storage separated from compute.
• Cloud reach — both run on AWS, Azure and Google Cloud, so cloud portability is not a differentiator between them.
• Current overlap — both now offer SQL analytics, ML tooling and semi-structured data support to varying degrees.

When to choose which — and using both

If a team’s work is mostly SQL-based analytics and reporting handled by analysts, Snowflake’s SQL-first design and ease of use are usually the more comfortable starting point. If the work is mostly data engineering, data science or machine learning, with engineers writing code against large or messy datasets, Databricks’ notebook-and-Spark model tends to fit more naturally. Many organisations, though, use both: raw and semi-structured data processed and modelled in Databricks, with curated results loaded into Snowflake for the analysts and BI tools that expect a clean SQL warehouse — or the reverse, depending on which system a team standardised on first. Treating this as “which one wins” misses how these platforms are actually deployed in practice.

Where this appears in cert study

Neither platform is the focus of a certification in the exam-prep catalogue here, but the underlying concepts — lakehouse architecture, data warehousing, and storage/compute separation — are core material in the AWS Data Engineer Associate and Microsoft Fabric Data Engineer Associate exams. Our /revision library covers that syllabus lesson by lesson.