Infer the output columns
If you already ran the walkthrough, reusepaid_orders_sink. Otherwise, edit these values and register the sink once.
table_id. The transformation supplies the sink’s column names and types when infer_schema is enabled. Declare useful aliases for calculated columns in your SELECT.
The connector writes to analytics.paid_orders in the selected warehouse. Registering the connector does not publish output rows. Publication happens when a pipeline runs and its sink completes the required checkpoint and commit work.
Declare columns explicitly
Use an explicit schema when you want the sink definition to fix the expected output columns. This alternative points to a separate test table.format option because the Iceberg sink writes files through its own writer.
Connection and write options
The file rotation threshold controls file writing. The pipeline’s
checkpoint_interval controls how often it requests a checkpoint. Neither setting is a promise that each interval will make new rows visible.
These examples append records. They do not define updates or deletes based on a Kafka record’s business key.
Catalog authentication
Add the relevant options to the sink’sWITH clause. The following fragments show the option names; replace their values for your catalog.
For a bearer token:
Warehouse storage access
Catalog authentication grants access to catalog operations. The writer also needs access to the files in warehouse storage. Use the runtime’s storage identity or catalog-vended credentials where your catalog supports them. For S3-compatible storage, these option names can be supplied in the sink’sWITH clause when required by your deployment:
Read the destination
paid_orders_sink is the Streaming connection name. Read analytics.paid_orders through an Iceberg catalog connection when checking output. Connecting the sink does not register a read catalog in Unified.
Choose the matching catalog recipe for a Unified read connection to the same warehouse.