Introduction
Data engineers face a fundamental challenge: getting data from diverse sources into a unified analytics platform efficiently and reliably. Whether your organization works with batch files in cloud storage, streaming events from Kafka, or transactional data from operational databases, you need ingestion methods that handle each scenario while maintaining data quality and governance. Unity Catalog in Azure Databricks provides the foundation for this unified approach, offering centralized governance for all your ingested data.
Azure Databricks offers multiple ingestion techniques, each optimized for specific data patterns and requirements. Lakeflow Connect provides managed connectors for common enterprise sources like SQL Server and Salesforce, eliminating the need for custom extraction code. Notebooks give you full control over ingestion logic using Python, SQL, or Scala. SQL commands like COPY INTO and CREATE TABLE AS SELECT offer declarative approaches for file-based ingestion. For real-time workloads, Spark Structured Streaming and Auto Loader process data continuously as it arrives, with exactly-once processing guarantees.
Understanding when to apply each technique enables you to build robust data pipelines that scale with your organization. You'll learn how to configure managed connectors for enterprise data sources, write notebook code for custom ingestion scenarios, use SQL commands for file-based batch loads, process change data capture feeds incrementally, stream data from message buses like Kafka and Event Hubs, configure Auto Loader for automatic file detection, and orchestrate ingestion workflows with Lakeflow Spark Declarative Pipelines.
By the end of this module, you'll be equipped to choose and implement the right ingestion approach for any data source, ensuring your data lands in Unity Catalog tables with proper governance and optimal performance.