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Reprocessing inefficient formats like JSON can be prohibitive with these simple transformations and are a perfect fit for incremental tables.īy contrast, the final tables in a pipeline, commonly referred to as gold tables, often require complicated aggregations that are not supported by Spark structured streaming. For example, the initial datasets in a pipeline, commonly referred to as bronze tables, often perform simple transformations. You can mix different types of tables in a single pipeline. Mixing complete tables and incremental tables SQL CREATE INCREMENTAL LIVE TABLE streaming_silver_table

Return dlt.read_stream("streaming_bronze_table").where(.)

You can read incrementally from other tables in a pipeline: Python streaming_silver_table: SQL CREATE INCREMENTAL LIVE TABLE streaming_bronze_tableĪS SELECT * FROM cloud_files("/databricks-datasets/structured-streaming/events/", "json") For example, you can read external data as a stream with the following code: Python inputPath = streaming_bronze_table():

You can define an incremental dataset by reading one or more inputs to your query as a stream. Incremental datasets with external data sources Data already processed is automatically tracked by the Delta Live Tables runtime. When an update is triggered for a pipeline, incremental tables process only new data that has arrived since the last update. Delta Live Tables supports incremental computations to reduce the cost of ingesting new data and the latency at which new data is made available. However, as data sizes grow, the resources required to reprocess data with each update can become prohibitive. Many applications require that tables be updated based on continually arriving data. To sign up for access, see Request Access to Delta Live Tables.