The following code example demonstrates processing SCD type 1 updates: Python import dltįrom import col, users(): If you uncomment the final row in the example data, it will insert the following record that specifies where records should be truncated: userIdĪll the following examples include options to specify both DELETE and TRUNCATE operations, but each of these are optional. The following are the input records for these examples: userId To run these examples, you must begin by creating a sample dataset. See Tutorial: Run your first Delta Live Tables pipeline. The following examples assume familiarity with configuring and updating Delta Live Tables pipelines. In the SCD type 1 example, the last UPDATE operations arrive late and are dropped from the target table, demonstrating the handling of out-of-order events. The following sections provide examples that demonstrate Delta Live Tables SCD type 1 and type 2 queries that update target tables based on source events that: SCD type 1 and SCD type 2 on Azure Databricks Add expectations on target data with a downstream table that reads input data from the target table.Add expectations on source data by defining an intermediate table with the required expectations and use this dataset as the source for the target table.To use expectations for the source or target dataset: Expectations are not supported in an APPLY CHANGES INTO query or apply_changes() function.A table that reads from the target of an APPLY CHANGES INTO query or apply_changes function must be a live table. The target of the APPLY CHANGES INTO query or apply_changes function cannot be used as a source for a streaming table.SCD type 2 updates will add a history row for every input row, even if no columns have changed.Metrics for the target table, such as number of output rows, are not available.If you add data manually to the table, the records are assumed to come before other changes because the version columns are missing. You can also query the raw data in the _apply_changes_storage_ table to see deleted records and extra version columns. To view the processed data, query the target view. Creating a view allows Delta Live Tables to filter out the extra information (for example, tombstones and versions) that is required to handle out-of-order data. This table is named by prepending _apply_changes_storage_ to the target table name.įor example, if you declare a target table named dlt_cdc_target, you will see a view named dlt_cdc_target and a table named _apply_changes_storage_dlt_cdc_target in the metastore. An internal backing table used by Delta Live Tables to manage CDC processing.A view using the name assigned to the target table.When you declare the target table in the Hive metastore, two data structures are created: What data objects are used for Delta Live Tables CDC processing? For syntax details, see Change data capture with SQL in Delta Live Tables or Change data capture with Python in Delta Live Tables. To create the statement defining the CDC processing, use the APPLY CHANGES statement in SQL or the apply_changes() function in Python. To create the target streaming table, use the CREATE OR REFRESH STREAMING TABLE statement in SQL or the create_streaming_table() function in Python. To perform CDC processing with Delta Live Tables, you first create a streaming table, and then use an APPLY CHANGES INTO statement to specify the source, keys, and sequencing for the change feed. There should be one distinct update per key at each sequencing value, and NULL sequencing values are unsupported. For SCD Type 2 changes, Delta Live Tables propagates the appropriate sequencing values to the _START_AT and _END_AT columns of the target table. Delta Live Tables automatically handles data that arrives out of order. You must specify a column in the source data on which to sequence records, which Delta Live Tables interprets as a monotonically increasing representation of the proper ordering of the source data. How is CDC implemented with Delta Live Tables? To learn how to record and query row-level change information for Delta tables, see Use Delta Lake change data feed on Azure Databricks. This article describes how to update tables in your Delta Live Tables pipeline based on changes in source data.
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