To find table settings, click on your pipeline > Edit > Tables > Table settings
If your BigQuery table is partitioned, you can specify the partitioned columns here. Click here for instructions on how to partition your table in BigQuery.
Artie merges data into your Snowflake based on your source table’s primary key(s). If you have an additional cluster key defined, you can specify that here.
For example, if the table was clustered by created_at, you can specify that column here.
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ALTER TABLE foo CLUSTER BY (TO_DATE(created_at))
This setting allows you to define a new table alias. If your source table is named foo, you can name it bar.
When enabled, Artie will skip processing deletes. This setting is useful if you want to keep a smaller dataset in your source and have a complete archive in your destination.
For high volume tables, Artie also has an ability to specify a different set of flush rules. We will automatically do this for you based on your ingestion lag.
History tables provide a complete audit trail of all changes to your data. When enabled, Artie creates a separate table named {TABLE}__HISTORY that records every change made to the original table.
The history table includes these additional columns:
__artie_operation - The type of change (CREATE, UPDATE, or DELETE)
__artie_updated_at - When Artie processed the change
__artie_db_updated_at - When the change occurred in your source database
Here are some examples of what you can do with history tables:
