Create the Output Stage based on Your Logical Data Model

For project administrators only

The Output Stage is a set of tables and views that serve as a source for loading data into your workspace. The Output Stage is the definition of your analytical data that the GoodData platform will download and process.

The Output Stage is generated for your cloud data warehouse based on the logical date model (LDM; see Create a Logical Data Model Manually or Create a Logical Data Model from CSV Files). The GoodData platform scans the LDM and suggests SQL queries that you then execute on your schema. The Output Stage follows the naming convention as described in Naming Convention for Output Stage Objects in Automated Data Distribution v2.

Before creating the Output Stage, make sure that your LDM is ready and there are no unpublished changes.

Creating the Output Stage as described in the following procedure (that is, directly from the LDM Modeler) is available only for cloud data warehouses (Amazon Redshift, Snowflake, Google BigQuery). If you are using the GoodData Agile Data Warehousing Service (ADS), you need to use the API to create the Output Stage.

Steps:

  1. In the LDM Modeler (to learn how to access the LDM Modeler, click here), click the menu button on the top right, and click Create Output Stage.
  2. Select the data source that you want to create the Output Stage for and the SQL command that you want to generate. You can choose from creating or altering tables and creating views.
  3. Click Create.
    The creating process starts. When the process completes, the dialog shows the SQL queries that you can use to create the Output Stage.
  4. Review the suggested queries and modify them as needed. Once done, execute the queries on your schema to create the Output Stage.
    Your LDM and the Output Stage are now synchronized.

If you do not have data load processes set up yet, you can now deploy processes for loading data (see Deploy a Process for Automated Data Distribution v2) and schedule them to load data regularly (see Scheduling a Process).

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