Choosing Lakeflow Connect, notebooks, or Azure Data Factory for ingestion

Azure Databricks offers multiple ingestion paths, and choosing the right one depends on the data source type, required latency, and whether you need managed connectors or custom orchestration. Lakeflow Connect provides simple, managed ingestion from common SaaS and database sources, notebooks give full programmatic control using Spark and Auto Loader, and Azure Data Factory (or Synapse pipelines) is best for enterprise-scale orchestration and integrating with a broad range of on-premises and cloud systems.

1 · Learn the must-know

• 1Lakeflow Connect offers built-in, low-code connectors (e.g., for Salesforce, SQL Server, Workday) that handle schema and incremental sync automatically, reducing custom pipeline code.
• 2Notebooks (using PySpark, SQL, or Auto Loader with cloudFiles) are best when you need custom transformation logic, fine-grained control, or ingestion of files landing in cloud storage.
• 3Auto Loader within notebooks is the recommended pattern for incrementally and efficiently ingesting new files from cloud object storage (ADLS Gen2, S3) with schema inference and evolution.
• 4Azure Data Factory (or Synapse Pipelines) is preferred for enterprise orchestration, ingesting from 90+ external systems, and coordinating multi-step workflows that include but are not limited to Databricks notebook execution.
• 5ADF can trigger and pass parameters to Databricks notebooks via the Azure Databricks linked service, making it useful for orchestrating ingestion plus downstream processing in one pipeline.
• 6For simple, source-specific, fully managed ingestion with minimal maintenance, prefer Lakeflow Connect; for custom logic or file-based streaming ingestion, prefer notebooks; for broad enterprise orchestration and non-Databricks-native sources, prefer ADF.

2 · Check your understanding