Introduction to Streams and Tasks

Snowflake supports continuous data pipelines with Streams and Tasks:

Streams:

A stream object records the delta of change data capture (CDC) information for a table (such as a staging table), including inserts and other data manipulation language (DML) changes. A stream allows querying and consuming a set of changes to a table, at the row level, between two transactional points of time.

In a continuous data pipeline, table streams record when staging tables and any downstream tables are populated with data from business applications using continuous data loading and are ready for further processing using SQL statements.

For more information, see Introduction to Streams.

Tasks:

A task object runs a SQL statement, which can include calls to stored procedures. Tasks can run on a schedule or based on a trigger that you define, such as the arrival of data. You can use task graphs to chain tasks together, definining directed acyclic graphs (DAGs) to support more complex periodic processing. For more information, see Introduction to tasks and Manage task dependencies with task graphs.

Combining tasks with table streams is a convenient and powerful way to continuously process new or changed data. A task can transform new or changed rows that a stream surfaces using SYSTEM$STREAM_HAS_DATA. Each time a task runs, it can either consume the change data or skip the current run if no change data exists.

For other continuous data pipeline features, see:

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