What is Auto Loader?

Auto Loader incrementally and efficiently processes new data files as they arrive in cloud storage without any additional setup.

How does Auto Loader work?

Auto Loader incrementally and efficiently processes new data files as they arrive in cloud storage. Auto Loader can load data files from AWS S3 (s3://), Azure Data Lake Storage Gen2 (ADLS Gen2, abfss://), Google Cloud Storage (GCS, gs://), Azure Blob Storage (wasbs://), ADLS Gen1 (adl://), and Databricks File System (DBFS, dbfs:/). Auto Loader can ingest JSON, CSV, XML, PARQUET, AVRO, ORC, TEXT, and BINARYFILE file formats.


Auto Loader provides a Structured Streaming source called cloudFiles. Given an input directory path on the cloud file storage, the cloudFiles source automatically processes new files as they arrive, with the option of also processing existing files in that directory. Auto Loader has support for both Python and SQL in Delta Live Tables.

You can use Auto Loader to process billions of files to migrate or backfill a table. Auto Loader scales to support near real-time ingestion of millions of files per hour.

How does Auto Loader track ingestion progress?

As files are discovered, their metadata is persisted in a scalable key-value store (RocksDB) in the checkpoint location of your Auto Loader pipeline. This key-value store ensures that data is processed exactly once.

In case of failures, Auto Loader can resume from where it left off by information stored in the checkpoint location and continue to provide exactly-once guarantees when writing data into Delta Lake. You don’t need to maintain or manage any state yourself to achieve fault tolerance or exactly-once semantics.

Incremental ingestion using Auto Loader with Delta Live Tables

Databricks recommends Auto Loader in Delta Live Tables for incremental data ingestion. Delta Live Tables extends functionality in Apache Spark Structured Streaming and allows you to write just a few lines of declarative Python or SQL to deploy a production-quality data pipeline with:

You do not need to provide a schema or checkpoint location because Delta Live Tables automatically manages these settings for your pipelines. See Load data with Delta Live Tables.

Databricks also recommends Auto Loader whenever you use Apache Spark Structured Streaming to ingest data from cloud object storage. APIs are available in Python and Scala.

Examples: Common Auto Loader patterns

For examples of common Auto Loader patterns, see Common data loading patterns.

Configure Auto Loader options

You can tune Auto Loader based on data volume, variety, and velocity.

For a full list of Auto Loader options, see:

If you encounter unexpected performance, see the FAQ.

Configure Auto Loader file detection modes

Auto Loader supports two file detection modes. See:

Benefits of Auto Loader over using Structured Streaming directly on files

In Apache Spark, you can read files incrementally using spark.readStream.format(fileFormat).load(directory). Auto Loader provides the following benefits over the file source:

  • Scalability: Auto Loader can discover billions of files efficiently. Backfills can be performed asynchronously to avoid wasting any compute resources.

  • Performance: The cost of discovering files with Auto Loader scales with the number of files that are being ingested instead of the number of directories that the files may land in. See What is Auto Loader directory listing mode?.

  • Schema inference and evolution support: Auto Loader can detect schema drifts, notify you when schema changes happen, and rescue data that would have been otherwise ignored or lost. See How does Auto Loader schema inference work?.

  • Cost: Auto Loader uses native cloud APIs to get lists of files that exist in storage. In addition, Auto Loader’s file notification mode can help reduce your cloud costs further by avoiding directory listing altogether. Auto Loader can automatically set up file notification services on storage to make file discovery much cheaper.