What is Databricks?
Databricks is a data analytics platform built on lakehouse architecture that unifies data engineering, data warehousing, machine learning, and AI workloads on a single open platform. It combines the flexibility of a data lake with the reliability and performance of a data warehouse, eliminating the need to move data between separate systems.
Organizations use Databricks as a data analytics platform to run ETL pipelines, business intelligence queries, real-time streaming analytics, machine learning experiments, and generative AI applications against the same governed data layer.
Features & Benefits
- Data lakehouse architecture — stores structured, semi-structured, and unstructured data in one place using Delta Lake, an open storage layer that adds ACID transactions, schema enforcement, and time travel to cloud object storage
- Apache Spark and Photon engine — runs batch and streaming data processing workloads at scale using Apache Spark with Photon, Databricks’ native vectorized query engine for faster SQL and ETL performance
- Lakeflow pipelines and orchestration — builds and orchestrates ETL and data engineering pipelines using Lakeflow Spark Declarative Pipelines and Lakeflow Jobs, with support for SQL, Python, notebooks, and dbt
- Lakeflow Connect — ingests data from enterprise SaaS applications and databases directly into the lakehouse using built-in connectors
- SQL warehouses and BI analytics — runs high-performance SQL queries directly on lakehouse storage through SQL warehouses; supports notebooks in Python, R, Scala, and SQL with embedded visualizations and dashboards
- Mosaic AI and model serving — trains, fine-tunes, and serves large language models and machine learning models; supports RAG pipelines, vector search, AI agents, and LLM gateway with built-in routing, monitoring, and guardrails
- MLflow — tracks machine learning experiments, manages model versions, and handles end-to-end MLOps lifecycle from development to production deployment
- Genie and AI/BI — conversational AI interface that lets users ask questions about data in natural language and receive answers as tables, charts, or explanations; Genie Code extends this to agentic data engineering and data science workflows
- Unity Catalog — centralizes data governance, access control, lineage tracking, and data quality monitoring across all data assets, models, and AI workloads in one governance layer
- Lakebase — an OLTP database built on Postgres and fully integrated with the lakehouse for transactional workloads that sync with analytical processing
- Delta Sharing — shares live data securely across organizations and cloud platforms without copying or moving data
- Multi-cloud deployment — runs on AWS, Microsoft Azure, and Google Cloud; integrates with cloud-native services including AWS Glue, Azure Data Factory, and Google BigQuery
Real-World Applications
Data engineering teams building large-scale ETL pipelines can use Lakeflow Declarative Pipelines to define transformations in SQL or Python and have Databricks handle orchestration, dependency management, and infrastructure scaling. Running pipelines on Delta Lake may improve data reliability through ACID transactions and schema enforcement across the full pipeline.
Data science and ML teams can use MLflow to track experiments, compare model versions, and deploy models to production through Mosaic AI model serving. Keeping training data, feature stores, and model artifacts in the same governed data analytics platform may reduce the data quality issues that commonly degrade model performance in production.
Business analysts who need to query large datasets can use Databricks SQL warehouses and the Genie natural language interface to run complex queries without writing code. Connecting BI tools directly to lakehouse storage through SQL warehouses may eliminate the need to export data to separate reporting databases.
Enterprises building generative AI applications can use Databricks vector search and the AI agent framework to build RAG pipelines that retrieve context from governed enterprise data before generating responses. Keeping vector indexes synchronized with Delta tables may ensure AI applications always query current data without additional pipeline complexity.
