What is Databricks? The Lakehouse Platform, Defined

Databricks is a unified data and AI platform built on Apache Spark and Delta Lake, founded by the creators of Spark, that combines data engineering, data warehousing, ML, and AI on a single lakehouse architecture.

Databricks is a unified data and AI platform built on Apache Spark, Delta Lake, and the lakehouse architecture. Founded in 2013 by the original creators of Spark at UC Berkeley, it runs as a SaaS service on AWS, Azure, and GCP, and serves as the analytical platform for data engineering, data warehousing, machine learning, and generative AI inside the same workspace.

What Databricks actually is

The platform has four major surfaces:

• Data engineering: PySpark, Scala, and SQL notebooks running on managed Spark clusters, with Delta Lake as the storage format and Unity Catalog as the governance layer.
• Data warehousing: Databricks SQL, the warehouse-style query engine on top of Delta tables, with materialized views, query result caching, and SQL warehouse compute pools.
• Machine learning: managed MLflow for experiment tracking, Feature Store, model serving, and the AutoML and AutoLog features.
• AI: Mosaic AI for foundation model training and serving, vector search, agent framework, and the AI Gateway for cross-provider model routing.

The shared property: every surface composes with Delta Lake as the storage primitive and Unity Catalog as the access-control plane.

What people confuse it with

• "Databricks is just managed Spark." Was true in 2014. By 2026, Databricks ships SQL warehousing, ML serving, agent frameworks, and AI gateways that have nothing to do with the original Spark notebook surface.
• "Databricks is the same as Snowflake now." Convergent but not identical. Snowflake is SQL-first and serverless-friendly. Databricks is code-first and cluster-aware. The team's primary language usually decides.
• "Databricks is open-source." Spark, Delta Lake, MLflow, and Unity Catalog (open-sourced in 2024) are open-source. The Databricks SaaS platform is proprietary.

When Databricks matters

Databricks matters when:

• The workload includes significant unstructured data (text, images, audio, video).
• The team is code-first (PySpark, Scala, or SQL with notebook ergonomics).
• ML training and serving need to live in the same platform as analytical workloads.
• Delta Lake or Iceberg interop is the storage requirement.

When Databricks does not help

Databricks does not help when:

• The workload is pure SQL analytics with high concurrent dashboard load, where a warehouse's concurrency scaling wins.
• The team has no Spark or ML culture and a SQL-only warehouse covers the workload.
• The organization is fully Microsoft-stack-aligned and Fabric provides the analytical surface.

How Thinklytics works on Databricks

We ship Databricks for AI-heavy and unstructured-data workloads, often in parallel with Snowflake or as a Spark-and-ML successor to legacy Hadoop or Synapse stacks. See Databricks AI consulting for the engagement scope.

Frequently asked questions

What is Databricks in one sentence?

Databricks is a unified data and AI platform built on Apache Spark, Delta Lake, and the lakehouse architecture, founded by the original creators of Spark and used for data engineering, data warehousing, ML, and AI workloads on a single platform.

How does Databricks differ from Snowflake?

Databricks is lakehouse-native and code-first (notebooks, PySpark, Scala). Snowflake is warehouse-native and SQL-first. The 2026 reality is convergence: Databricks ships SQL warehouses, Snowflake ships Iceberg interop and Cortex. The choice usually decides on team skills and existing tool investment.

What is Unity Catalog?

Unity Catalog is Databricks' unified governance layer for data, AI assets (models, features, vector indexes), and access control across the lakehouse. It is the 2026 successor to the older Hive Metastore. Unity Catalog plus Databricks Apps form the access-control and governance backbone of any production Databricks deployment.

How is Databricks licensed?

Per-DBU (Databricks Unit) consumption, with rates varying by compute type (Jobs Compute, All-Purpose Compute, SQL Warehouse, Serverless). DBU rates also vary by cloud provider (AWS, Azure, GCP) and edition (Standard, Premium, Enterprise). Most enterprises land at $400,000 to $5M per year for production Databricks usage.

What is Mosaic AI?

Mosaic AI is Databricks' AI platform, built on the MosaicML acquisition in 2023. It includes model serving, fine-tuning, vector search, agent framework, and the AI Gateway. Positioned against Snowflake Cortex and the major cloud providers' AI services.

Is Databricks an open-source platform?

Partially. Spark, Delta Lake, MLflow, and Unity Catalog (open-sourced in 2024) are open-source projects Databricks contributes to and maintains commercial versions of. The Databricks SaaS platform itself is proprietary, though customers can run open-source components outside the SaaS.

When does Databricks win over a warehouse?

When workloads include significant unstructured data (text, images, audio), when the team is code-first PySpark or Scala, when ML model training and serving live alongside analytics, and when Delta Lake or Iceberg interop matters. Warehouses still win for SQL-only analytical concurrency at large user count.

How does Thinklytics work on Databricks?

We ship Databricks for AI-heavy and unstructured-data workloads, often in parallel with Snowflake or as a Spark-and-ML successor to legacy Hadoop or Synapse stacks. See Databricks AI consulting for the engagement scope and Snowflake vs Databricks AI workloads for the cross-vendor comparison.