Databricks Tutorial: Get Started in 5 Minutes [2026]

What it does:

Delta Lake is the open-source storage foundation of Databricks, bringing reliability to data lakes with ACID transactions, scalable metadata handling, and unified batch and streaming processing. It stores data in Parquet format with a transaction log that enables time travel (querying historical data snapshots), schema evolution, and data versioning. This eliminates the traditional two-tier architecture of separate data lakes and warehouses, reducing data duplication and pipeline complexity while maintaining the cost advantages of cloud object storage.

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What it does:

Unity Catalog is Databricks' unified governance layer for all data and AI assets across workspaces and clouds. It provides a three-level namespace (catalog.schema.table), fine-grained access control down to the row and column level, automated data lineage tracking, and a searchable data discovery interface. Unity Catalog governs not just tables but also ML models, notebooks, files, and volumes, enabling organizations to enforce consistent security policies and compliance requirements across their entire data estate from a single control plane.

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What it does:

Delta Live Tables is a declarative ETL framework that simplifies building and managing data pipelines. Engineers define transformations as SQL or Python queries, and DLT automatically manages task orchestration, cluster infrastructure, monitoring, data quality enforcement, and error handling. Built-in expectations allow users to define data quality constraints that can warn, drop, or fail on invalid records. DLT supports both batch and streaming workloads with the same code, and provides pipeline observability through event logs and lineage graphs.

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What it does:

Databricks provides a fully managed MLflow implementation for end-to-end machine learning lifecycle management. Data scientists can track experiments with automatic logging of parameters, metrics, and artifacts; register models with stage transitions (staging, production, archived); and deploy models to production endpoints. Databricks Model Serving offers real-time and batch inference with serverless compute, auto-scaling, and A/B testing capabilities. The integration with Feature Store ensures consistent feature computation between training and serving environments.

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