TL; DR

Snowflake and Databricks started differently in 2012 but have converged over the years. At Levi9, as partners with both platforms and over 20 certifications, we’ve learned the answer isn’t about which is better; it’s about matching capabilities to your specific needs. Ultimately, it’s a tie – the right choice depends entirely on your use case.

Why This Comparison Matters

Both Snowflake and Databricks launched around 2012 with different approaches. Over the years, their road maps have been converged. Today, it’s difficult to determine which platform is better because they both cover analytics, machine learning, and data engineering – just differently.

Snowflake: Built for Analytics

Snowflake reshaped data warehousing through its core architecture: separating storage and computing. This brings flexibility to handle different workloads and exceptional SQL performance. The platform optimizes your queries automatically.

Key strengths:

Near-zero maintenance: Infrastructure is managed for you. Set up in minutes, not months.

Built-in governance: Security, access control, and lineage from day one.

Zero-copy cloning and time travel: Share data seamlessly without duplication. Undo mistakes instantly.

Multi-cloud resilience: Run on AWS, Azure, or Google Cloud without vendor lock-in.

“Snowflake dramatically reduces the time needed to start working and deliver results. It just works.”, said Nikola Radosavljević, Data Architect at Levi9.

Companies choose Snowflake because it delivers quick time to insights with minimal setup. From startups to enterprise leaders, organizations benefit from seamless data sharing and collaboration at scale.

Recent additions include Cortex AI for machine learning, Streamlit for building data apps, and Iceberg support for open table formats.

Databricks: Unified Analytics Platform

Databricks was built on Apache Spark to unify data engineers, data scientists, and analysts in one environment. It’s designed for organizations that need both traditional analytics and advanced machine learning.

Key strengths:

Unified environment: Data engineers, ML engineers, and analysts work together without moving data across platforms.

Delta Lake: ACID transactions, schema enforcement, and versioning on data lakes.

Real-time streaming: Delta Live Tables automate pipeline management with built-in quality checks.

Developer control: Notebooks, CI/CD with asset bundles, infrastructure as code.

Our colleague Iulian Prodan, Data Tech Lead, summarized it well: “Databricks provides the flexibility developers need for engineering workloads – pipelines, orchestration, deployments.”

Unity Catalog enables fine-grained governance across clouds with role-level and attribute-based access control.

Head-to-Head: Where Each Platform Wins

Both handle ingestion well. Snowpipe streams data continuously with minimal setup. Delta Live Tables automate complex transformations and schema evolution. Different approaches, both effective.

When it comes to machine learning, Databricks was built for ML from the start. The unified environment lets data scientists and engineers collaborate directly. However, Snowflake’s Cortex AI is improving rapidly, making this gap narrower.

For analytics experience, this is where Snowflake really shines. Built as a cloud data warehouse, Snowflake excels at SQL analytics with seamless integrations with Power BI, Tableau, and Looker. Business analysts can start immediately with their preferred language: SQL. While Databricks’ Photon engine delivers fast queries and analysts can run SQL directly, Snowflake remains the clear winner for traditional analytical workloads.

One Platform to Rule Them All?

Is there one platform to rule them all? Well, it depends entirely on the organization’s complexity, scale, and specific use cases.

Snowflake covers end-to-end cases: ingestion via Snowpipe, transformations with tools like dbt and Airflow, analytics with any BI tool, plus new features like Cortex AI and Streamlit. Near-zero maintenance and zero-copy data sharing are core strengths.

Databricks excels at multidisciplinary collaboration in a unified environment. If you’re doing analytics, machine learning, and AI together, Databricks has the edge.