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Delta Lake and Tables Overview

Jul 10, 2025

Overview

This lecture introduces Delta Lake and Delta Tables in Databricks, explaining their features, differences from standard Data Lakes, and providing hands-on steps for creating and using Delta Tables.

Introduction to Delta Lake and Delta Tables

  • Delta Lake is an optimized storage layer built on top of Data Lakes, using Parquet format with transaction logs for reliability.
  • Data Lake (e.g., ADLS Gen2) stores files in formats like CSV, Parquet, or Avro, but lacks database-like features.
  • Delta Lake adds features such as ACID compliance, scalable metadata handling, and version control.
  • Data stored in Delta Lake is organized as Delta Tables, which behave similarly to traditional database tables.

Key Features of Delta Tables

  • Support for ACID (Atomicity, Consistency, Isolation, Durability) transactions.
  • Scalable metadata handling and efficient schema enforcement.
  • Capability for both streaming and batch processing.
  • Built-in version control and time travel using transaction (delta) logs.
  • Support for upserts (insert/update), schema changes, and slowly changing dimensions.

Hands-on: Creating and Managing Delta Tables

  • Connect Databricks to a Data Lake (e.g., ADLS Gen2) to access data files.
  • Use Spark APIs (e.g., spark.read.format("csv")) to read CSV files from the Data Lake.
  • Create a Delta Table from a DataFrame: df.write.format("delta").mode("overwrite").save("path").
  • Delta Tables store data as compressed Parquet files (snappy.parquet) alongside delta logs (_delta_log).
  • Delta logs (JSON & CRC files) store metadata and operation history for versioning and audit.
  • Read from a Delta Table using spark.read.format("delta").load("path").
  • Review Delta Table history with .history() to see previous operations and versions.
  • Delta Tables can be created as managed (Databricks controls storage location) or unmanaged (user-defined path).
  • Standard SQL queries (e.g., SELECT * FROM table) can be used to read or manipulate Delta Tables.
  • Tables can be dropped using SQL (DROP TABLE IF EXISTS table_name).*_

Key Terms & Definitions

  • Delta Lake — An ACID-compliant storage layer on top of Data Lake, optimized for big data analytics.
  • Delta Table — A data table in Delta Lake, supporting transactions, schema evolution, and versioning.
  • Data Lake — A repository for storing various file types (CSV, Parquet, etc.) without database features.
  • Parquet Format — Columnar file storage format optimized for analytics.
  • Snappy Compression — Fast, efficient compression used with Parquet files in Delta Lake.
  • Delta Log — Transaction log in Delta Tables that stores metadata, history, and enables version control.
  • Managed Table — Table whose storage location is managed by Databricks.
  • Unmanaged Table — Table with a user-specified data storage location.
  • ACID Compliance — Database properties guaranteeing reliable transaction processing.

Action Items / Next Steps

  • Watch the suggested videos on Data Lakes, Delta Lake, and managed vs unmanaged tables for deeper understanding.
  • Practice creating, reading, and managing Delta Tables using Spark and Databricks.
  • Review upcoming lessons for details on Delta Table features such as schema enforcement, upserts, and time travel.

Full transcript