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Databricks
Unified lakehouse platform — data engineering, SQL analytics, and AI in one place.
0Free-Limited
Data Warehouse
Overview
Databricks is a unified data intelligence platform combining data engineering, analytics, and AI on top of Delta Lake, with collaborative notebooks, managed Apache Spark, and the Databricks Lakehouse architecture.
Key Features
- Delta Lake for ACID transactions on Parquet data with time travel
- Managed Apache Spark clusters with auto-scaling and serverless SQL
- Delta Live Tables for declarative ETL pipeline development
- Unity Catalog for unified data governance across workspaces
- Databricks AI/BI for no-code dashboard and GenAI-powered analysis
- MLflow integration for experiment tracking and model registry
Real-World Workflows
Build a lakehouse ETL with Delta Live Tables
- 1Create a Delta Live Tables pipeline pointing at raw data in S3
- 2Define Bronze (raw), Silver (cleansed), Gold (aggregated) tables
- 3DLT handles CDC, schema evolution, and data quality expectations
- 4Trigger pipeline on schedule or via Databricks Workflows
Fine-tune an LLM on private data
- 1Store training data in Delta Lake with Unity Catalog access control
- 2Use Databricks Model Training (GPU clusters) to fine-tune LLaMA or Mistral
- 3Track experiments and register models in MLflow Model Registry
- 4Serve the model as a REST endpoint via Databricks Model Serving
Getting Started
# No local install — web-based platform
# Sign up at https://databricks.com/try
# Connect from Python
pip install databricks-sdk
from databricks.sdk import WorkspaceClient
w = WorkspaceClient()
clusters = w.clusters.list()
for c in clusters:
print(c.cluster_name)Compare Alternatives
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