Summary
Throughout this module, you explored the comprehensive set of data ingestion techniques available in Azure Databricks for loading data into Unity Catalog. From managed connectors to custom notebook code, from declarative SQL commands to real-time streaming, each approach addresses specific data patterns and organizational requirements.
You learned how Lakeflow Connect simplifies ingestion from enterprise sources by providing managed connectors with built-in change data capture and SCD support. You explored notebook-based ingestion using DataFrames and the Spark Structured Streaming API for full control over your ingestion logic. You discovered how SQL commands like COPY INTO and CREATE TABLE AS SELECT provide declarative options for file-based batch loads with automatic file tracking.
For incremental processing, you implemented CDC flows using the AUTO CDC API to efficiently apply inserts, updates, and deletes to destination tables. You configured Spark Structured Streaming to process events from Kafka and Event Hubs in real time with exactly-once guarantees. You set up Auto Loader to automatically detect and ingest new files with schema inference and evolution capabilities. Finally, you used Lakeflow Spark Declarative Pipelines to orchestrate end-to-end ingestion workflows with automatic orchestration and error handling.
As you build data pipelines for your organization, consider which ingestion method best matches each source system's characteristics. Use managed connectors when available for common enterprise sources. Choose notebooks for complex transformations or sources that require custom logic. Apply Auto Loader for file-based streaming with automatic schema handling. Orchestrate your ingestion flows with Lakeflow Spark Declarative Pipelines to benefit from built-in reliability features. With these techniques, you can build robust ingestion pipelines that deliver high-quality data to your lakehouse efficiently and reliably.