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The Data Intelligence Platform
The lakehouse platform from the creators of Apache Spark, unifying data engineering, analytics, machine learning, and generative AI on one open foundation across AWS, Azure, and Google Cloud.
Apache Spark
Distributed compute engine for large-scale data processing and analytics
Delta Lake
Open storage layer adding ACID transactions to data lakes
MLflow
Open-source platform for experiment tracking, model registry, and deployment
Unity Catalog
Unified governance for data, models, and AI, open-sourced in 2024
Mosaic AI
The generative AI and ML layer for serving, training, agents, and vector search on your own data
Databricks, Inc. is a San Francisco software company founded in 2013 by the creators of Apache Spark, who came out of the UC Berkeley AMPLab. Its cloud platform, marketed as the Data Intelligence Platform, unifies data engineering, analytics, business intelligence, machine learning, and AI. It runs natively on AWS, Microsoft Azure, and Google Cloud. Independent reporting puts the company at roughly $5.4 billion in annual recurring revenue as of January 2026 (vendor and independent figures, fast-moving).
The data lakehouse combines the structure of a data warehouse with the flexibility of a data lake. Databricks processes data while leaving files in the customer's own cloud storage in open formats such as Delta Lake and Apache Iceberg. Independent analysts note that separating compute from storage helps mitigate vendor lock-in and avoid egress fees.
Databricks is built on open technologies its team created or stewards: Apache Spark for distributed compute, Delta Lake for ACID transactions on data lakes, MLflow for AI engineering, and Unity Catalog for governance, which the company open-sourced in June 2024. Open formats keep data portable rather than locked to a single vendor.
Databricks bills pay-as-you-go with no up-front cost, metered per second in DBUs (Databricks Units), a normalized unit of processing power. Storage and networking are billed separately by your cloud provider. On Azure, pricing is set and billed by Microsoft. Rates vary by cloud, region, and workload, so confirm current figures on the Databricks pricing page (verified June 2026).
$5.4B
ARR (reported, Jan 2026)
2013
Founded by Spark Creators
3
AWS, Azure & GCP
Open
Spark, Delta, MLflow, UC
In-depth coverage of the Databricks lakehouse: what the platform is, how it compares to Snowflake, Azure Databricks, Mosaic AI, DBU pricing, certifications, free editions, and the architecture underneath it all. Verified facts, vendor-reported figures labeled, honest trade-offs.
The lakehouse platform explained: how Databricks unifies data engineering, analytics, ML, and generative AI on Spark, Delta Lake, and Unity Catalog. Start here.
Lakehouse versus warehouse: open formats and compute-storage separation against a more managed model. Architecture, lock-in, and which fits your data team.
A first-party Microsoft Azure service since 2017: the same Databricks platform, but with pricing set and billed by Microsoft under your Azure subscription.
Databricks' generative AI and ML layer, born from the MosaicML acquisition: model serving, training, agent frameworks, vector search, and AI-judge evaluation.
How DBU pricing works: pay-as-you-go, per-second billing, and per-DBU rates by workload. What you pay Databricks versus your cloud provider, with figures to verify.
From Data Engineer Associate to Generative AI Engineer: the full certification track across data engineering, analysis, ML, and platform roles, and how to choose.
Community Edition, Free Edition, and the full-platform free trial compared: what each gives you for learning Spark, and where you still pay your cloud provider.
How the lakehouse merges warehouse and lake: open formats on your own cloud storage, ACID via Delta Lake, Unity Catalog governance, and serverless compute.
Explore the open-source ML ecosystem, LLM infrastructure, and the broader AI Tools Hub.
PyTorch Hub
The deep learning framework behind much of the models Databricks teams train.
Hugging Face Hub
Open models and datasets that plug into Mosaic AI training and serving.
Anthropic Claude Hub
A frontier model partner Databricks hosts within its own platform perimeter.
Microsoft Hub
Azure hosts Databricks as a first-party service with Microsoft-set pricing.
AI Tools Hub
Breakdowns, comparisons, and guides across the AI vendor landscape.
AI Governance
Responsible AI, the EU AI Act, and data governance for analytics platforms.
Important context for responsible AI adoption
The Databricks platform processes data within your own cloud account, leaving files in your storage in open formats, and the company markets Unity Catalog for governance, access controls, and data lineage. When your workloads call external LLM APIs or use models hosted within the platform, that data is subject to the relevant provider's terms and any data processing agreements in place. Enterprise deployments carry contractual data handling commitments. Review Databricks' current privacy policy and your cloud provider's terms before processing confidential or personally identifiable information.
Data and AI platforms automate analytics and generative AI workflows, but they should not replace human expertise or judgment in critical decisions. Models served through these systems can produce plausible but incorrect results. If you are experiencing distress:
AI systems can produce plausible-sounding but incorrect guidance. For mental health, medical, legal, or financial decisions, always consult a qualified professional.
See the NIST AI Risk Management Framework for structured guidance on AI risk assessment.
Under GDPR (EU) and CCPA (California), you have the right to access, correct, and delete your personal data. Because the lakehouse keeps data in your own cloud storage, your organization retains direct control over the data its workloads process. Hosted enterprise features operate under Databricks Inc.'s data processing terms.
The EU AI Act classifies general-purpose AI models above certain capability thresholds under transparency and risk obligations. AI systems built and deployed within the EU are subject to these provisions, with compliance responsibilities falling on the deploying organization under the EU AI Act's provider liability framework.
This publication is editorially independent. AI tool coverage reflects independent research, verified benchmarks, and editorial judgment. Where affiliate links are present, they are clearly disclosed and do not influence conclusions.