Skip to content
datastudyguides

DP-750 · 3.2.7 · Prepare and process data

Pipelines with Auto Loader

Ingest data by using Lakeflow Spark Declarative Pipelines, including Auto Loader

Use Auto Loader inside Lakeflow pipelines for scalable incremental file ingestion with schema handling and managed operations.

See Auto Loader file events

Microsoft Learn diagram showing Auto Loader file events with cloud storage, queues, the file events service, cache, and Auto Loader.

File events let Auto Loader discover new files incrementally without repeatedly scanning the entire source directory.

Put Auto Loader inside a managed pipeline

  1. 1Cloud filesNew files arrive
  2. 2Auto LoaderDiscovers only new files
  3. 3Lakeflow pipelineAdds orchestration and quality
  4. 4Streaming tableMaintains governed output
Incremental file ingestion

Lakeflow pipelines add managed execution, monitoring, schema handling, and data-quality capabilities around the ingestion logic.

Match trigger to latency

  • Process when new files arriveFile arrival trigger
  • Very low latency requires always-on processingContinuous mode
Execution mode

Do not pay for continuous execution when event-driven file arrival meets the freshness requirement.

Key points

  • Auto Loader: Incrementally discovers and ingests new cloud files
  • Lakeflow pipelines: Adds managed orchestration and data quality
  • File events: Efficiently detect new files
  • Streaming table: Natural governed destination for incremental ingestion

Exam trap

Continuous mode is not the default choice when file arrival triggers can meet latency at lower cost.

Sources · 2 Microsoft Learn pages Show ▾

Checked on October 4, 2026. Report a problem

Go deeper on Microsoft Learn

Checked against Microsoft Learn on October 4, 2026. Report a problem with this card

How well do you know this?