Databricks Lakehouse
The leading AI and data platform combining data engineering, ML, and AI on an open lakehouse architecture.
What it does
Databricks is the leading AI and data platform - built by the creators of Apache Spark and Delta Lake, it unifies data engineering, machine learning, and generative AI on an open lakehouse architecture that works across AWS, Azure, and GCP. Core capabilities include optimized Spark-based data processing, Delta Lake for ACID-compliant data storage, MLflow for ML experiment tracking and model management, Unity Catalog for unified data governance, and Mosaic AI for building and serving machine learning and LLM applications. AI capabilities include Databricks Assistant, a natural language interface for writing and explaining SQL and Python code, AutoML that automatically builds and optimizes ML models, vector search for AI application development, and Model Serving for production AI deployment. Databricks is the platform of choice for data engineering teams building modern data and AI infrastructure.
Why AI-NATIVE
Databricks Lakehouse is AI-native in its platform architecture - built by the teams behind Apache Spark and MLflow to natively support the full AI and ML lifecycle from data preparation through model training, evaluation, and production deployment.
Best for
Mid-market data teams use Databricks as their data and AI platform - Spark-based data processing replacing fragile custom ETL pipelines and MLflow providing ML experiment tracking without building MLOps infrastructure from scratch.
Large enterprises use Databricks for enterprise data and AI at scale - Delta Lake powering data lakehouse architecture, Unity Catalog providing enterprise governance, and Mosaic AI enabling production LLM and ML applications across the organization.
Limitations
Databricks is a powerful platform for skilled data engineers and data scientists — organizations without data engineering talent cannot self-serve Databricks deployment and need professional services or specialized hiring.
Databricks bills on DBU (Databricks Unit) consumption — poorly optimized workloads, always-on clusters, and large-scale jobs can generate significant monthly costs that require careful capacity management.
Organizations with primarily SQL-based analytics and BI needs often find Snowflake's SQL-native architecture simpler to operate — Databricks' Python/Spark-first design is more natural for ML-heavy teams.
Alternatives by segment
| If you need… | Consider instead |
|---|---|
| SQL-native cloud data warehouse | Snowflake |
| dbt for SQL transformation | dbt |
| Microsoft-integrated analytics | Azure Synapse |
Databricks Community Edition free. Pay-as-you-go on DBU consumption - Jobs compute from $0.10/DBU. All-Purpose compute higher. Premium and Enterprise tiers add governance and security features. Volume discounts negotiated. Annual commitments available.
✓ Free tier available
