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Return (("json").load("/databricks-datasets/wikipedia-datasets/data-001/clickstream/raw-uncompressed-json/2015_2_clickstream.json")) The following examples demonstrate loading JSON to create Delta Live Tables tables: Python clickstream_raw(): For a full list, see What data formats can you use in Azure Databricks?. Delta Live Tables supports all of the file formats supported by Apache Spark on Azure Databricks.

You can load small or static datasets using Apache Spark load syntax. Load small or static datasets from cloud object storage See Interact with external data on Azure Databricks. The following example declares a materialized view to access the current state of data in a remote Postgresql table: import postgres_raw(): Some data sources do not have full parity for support in SQL, but you can write a standalone Python notebook to define data ingestion from these sources and then schedule this library alongside other SQL notebooks to build a Delta Live Tables pipeline. Load data from external systemsĭelta Live Tables supports loading data from any data source supported by Azure Databricks. See Work with streaming data sources on Azure Databricks. You can write downstream operations in pure SQL to perform streaming transformations on this data, as in the following example: CREATE OR REFRESH STREAMING TABLE streaming_silver_tableįor an example of working with Event Hubs, see Use Azure Event Hubs as a Delta Live Tables data source. See What is Enhanced Autoscaling?.įor example, the following code configures a streaming table to ingest data from Kafka: import kafka_raw(): Databricks recommends combining streaming tables with continuous execution and enhanced autoscaling to provide the most efficient ingestion for low latency loading from message buses. You can configure Delta Live Tables pipelines to ingest data from message buses with streaming tables. See What is Auto Loader? and Auto Loader SQL syntax. SQL CREATE OR REFRESH STREAMING TABLE customersĪS SELECT * FROM cloud_files("/databricks-datasets/retail-org/customers/", "csv")ĬREATE OR REFRESH STREAMING TABLE sales_orders_rawĪS SELECT * FROM cloud_files("/databricks-datasets/retail-org/sales_orders/", "json") load("/databricks-datasets/retail-org/sales_orders/") option("cloudFiles.format", sales_orders_raw(): The following examples use Auto Loader to create datasets from CSV and JSON files: Python customers(): Auto Loader and Delta Live Tables are designed to incrementally and idempotently load ever-growing data as it arrives in cloud storage. Load files from cloud object storageĭatabricks recommends using Auto Loader with Delta Live Tables for most data ingestion tasks from cloud object storage. You can mix SQL and Python notebooks in a Delta Live Tables pipeline to use SQL for all operations beyond ingestion.įor details on working with libraries not packaged in Delta Live Tables by default, see Pipeline dependencies.