![]() ![]() Delta Live Tables infers the dependencies between these tables, ensuring updates occur in the correct order. What is a Delta Live Tables pipeline?Ī pipeline is the main unit used to configure and run data processing workflows with Delta Live Tables.Ī pipeline contains materialized views and streaming tables declared in Python or SQL source files. See What is a Delta Live Tables pipeline?. Tutorial: Declare a data pipeline with Python in Delta Live Tablesĭelta Live Tables separates dataset definitions from update processing, and Delta Live Tables notebooks are not intended for interactive execution.Tutorial: Declare a data pipeline with SQL in Delta Live Tables. ![]() To get started with Delta Live Tables syntax, use one of the following tutorials: Declare your first datasets in Delta Live Tablesĭelta Live Tables introduces new syntax for Python and SQL. Databricks recommends using views to enforce data quality constraints or transform and enrich datasets that drive multiple downstream queries. Views are useful as intermediate queries that should not be exposed to end users or systems. Delta Live Tables does not publish views to the catalog, so views can be referenced only within the pipeline in which they are defined. ViewsĪll views in Azure Databricks compute results from source datasets as they are queried, leveraging caching optimizations when available. Delta Live Tables implements materialized views as Delta tables, but abstracts away complexities associated with efficient application of updates, allowing users to focus on writing queries. Each time the pipeline updates, query results are recalculated to reflect changes in upstream datasets that might have occurred because of compliance, corrections, aggregations, or general CDC. Materialized views are powerful because they can handle any changes in the input. Materialized views are refreshed according to the update schedule of the pipeline in which they’re contained. Materialized viewĪ materialized view (or live table) is a view where the results have been precomputed. Streaming tables are designed for data sources that are append-only.Īlthough, by default, streaming tables require append-only data sources, when a streaming source is another streaming table that requires updates or deletes, you can override this behavior with the skipChangeCommits flag. Streaming tables can also be useful for massive scale transformations, as results can be incrementally calculated as new data arrives, keeping results up to date without needing to fully recompute all source data with each update. Streaming tables are optimal for pipelines that require data freshness and low latency. Because most datasets grow continuously over time, streaming tables are good for most ingestion workloads. Streaming tables allow you to process a growing dataset, handling each row only once. Streaming tableĪ streaming table is a Delta table with extra support for streaming or incremental data processing. To learn more about selecting dataset types to implement your data processing requirements, see When to use views, materialized views, and streaming tables. The following sections provide more detailed descriptions of each dataset type. Use views for intermediate transformations and data quality checks that should not be published to public datasets. Records are processed each time the view is queried. Materialized views should be used for data sources with updates, deletions, or aggregations, and for change data capture processing (CDC). ![]() Records are processed as required to return accurate results for the current data state. How are records processed through defined queries?Įach record is processed exactly once. The following table describes how each dataset is processed: Dataset type What are Delta Live Tables datasets?ĭelta Live Tables datasets are the streaming tables, materialized views, and views maintained as the results of declarative queries. To learn more about the benefits of building and running your ETL pipelines with Delta Live Tables, see the Delta Live Tables product page. You can also enforce data quality with Delta Live Tables expectations, which allow you to define expected data quality and specify how to handle records that fail those expectations. Delta Live Tables manages how your data is transformed based on queries you define for each processing step. Instead of defining your data pipelines using a series of separate Apache Spark tasks, you define streaming tables and materialized views that the system should create and keep up to date. ![]() Contact your Databricks account representative for more information. Delta Live Tables requires the Premium plan. ![]()
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