Databricks offers a variety of ways to help you load data into a lakehouse backed by Delta Lake. Databricks recommends using Auto Loader for incremental data ingestion from cloud object storage. The add data UI provides a number of options for quickly uploading local files or connecting to external data sources.
If you haven’t used Auto Loader on Databricks, start with a tutorial. See Run your first ETL workload on Databricks.
Auto Loader incrementally and efficiently processes new data files as they arrive in cloud storage without additional setup. 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.
You can simplify deployment of scalable, incremental ingestion infrastructure with Auto Loader and Delta Live Tables. Note that Delta Live Tables does not use the standard interactive execution found in notebooks, instead emphasizing deployment of infrastructure ready for production.
You can securely upload local data files or ingest data from external sources to create tables. See Load data using the add data UI.
Databricks validates technology partner integrations that enable you to load data into Databricks. These integrations enable low-code, scalable data ingestion from a variety of sources into Databricks. See Technology partners. Some technology partners are featured in Databricks Partner Connect, which provides a UI that simplifies connecting third-party tools to your lakehouse data.
COPY INTO allows SQL users to idempotently and incrementally load data from cloud object storage into Delta tables. It can be used in Databricks SQL, notebooks, and Databricks Jobs.
Here are a few things to consider when choosing between Auto Loader and
If you’re going to ingest files in the order of thousands, you can use
COPY INTO. If you are expecting files in the order of millions or more over time, use Auto Loader. Auto Loader requires fewer total operations to discover files compared to
COPY INTOand can split the processing into multiple batches, meaning that Auto Loader is less expensive and more efficient at scale.
If your data schema is going to evolve frequently, Auto Loader provides better primitives around schema inference and evolution. See Configure schema inference and evolution in Auto Loader for more details.
Loading a subset of re-uploaded files can be a bit easier to manage with
COPY INTO. With Auto Loader, it’s harder to reprocess a select subset of files. However, you can use
COPY INTOto reload the subset of files while an Auto Loader stream is running simultaneously.
For an even more scalable and robust file ingestion experience, Auto Loader enables SQL users to leverage streaming tables. See Load data using streaming tables in Databricks SQL.
For a brief overview and demonstration of Auto Loader, as well as
COPY INTO, watch the following YouTube video (2 minutes).
You can connect to a variety of data sources using Apache Spark. See Interact with external data on Databricks for a list of options and examples for connecting.
Apache Spark automatically captures data about source files during data loading. Databricks lets you access this data with the File metadata column.
Use the Create or modify table from file upload page to upload CSV, TSV, or JSON files. See Create or modify a table using file upload.
Migrate existing data applications to Databricks so you can work with data from many source systems on a single platform. See Migrate data applications to Databricks.