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Table Of Contents
Building Modern Data Applications Using Databricks Lakehouse
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DLT makes ingesting data from a variety of input sources simple. For example, DLT can efficiently process new files landing in a cloud storage location throughout the day, ingest structured data by connecting to an external storage system, such as a relational database, or read static reference tables that can be cached into memory. Let’s look at how we can use DLT to incrementally ingest new data that arrives in a cloud storage location.
One of the key features of the Databricks Data Intelligence Platform is a feature called Auto Loader, which is a simple yet powerful ingestion mechanism for efficiently reading input files from cloud storage. Auto Loader can be referenced in a DataFrame definition by using the cloudFiles data source. For example, the following code snippet will use the Databricks Auto Loader feature to ingest newly arriving JSON files from a storage container:
df = (spark...