Direct data distribution from data warehouses covers extracting consolidated and cleaned data directly from a data warehouse and distributing it to your GoodData workspaces. This article outlines essential data warehouse integration practices and provides links to detailed integration guides and best practices.
Supported Data Warehouses
In addition to the GoodData ADS data warehouse (see Data Warehouse), GoodData platform supports direct Integration with the following third-party data warehouses:
- Amazon Redshift (see https://aws.amazon.com/redshift/)
- Google BigQuery (see https://cloud.google.com/bigquery/)
- Snowflake (see https://www.snowflake.com/)
You can integrate data from your Snowflake instance, Redshift cluster or BigQuery project directly into the GoodData platform. The Automated Data Distribution (ADD) process synchronizes data from the warehouse with your customers’ workspaces based on a defined schedule. This is a key approach in building optimized multi-tenant analytics for all your customers and users without the runaway costs associated with executing direct queries to your data warehouse. For more information about ADD benefits and usage, see Automated Data Distribution Reference.
Setting up direct data distribution from a warehouse requires actions that you perform both on your Snowflake instance, Redshift cluster or BigQuery project and in your GoodData workspace. Follow our step-by-step tutorials that will help you integrate your warehouse and GoodData and provide you with as much automation during integration as possible. Depending on your experience, you can start with your own data or you can first try using our sample data for your warehouse-GoodData integration to better understand the processes involved:
- Getting Started with Redshift and GoodData
- Getting Started with Snowflake and GoodData
- Getting Started with BigQuery and GoodData
If you have a GoodData workspace with the logical data model (LDM) that meets your business requirements for data analysis, see Integrate Data Warehouses Directly to GoodData based on an Existing LDM.
Components of Direct Data Distribution
A Data Source is an entity that stores data warehouse credentials and the location of the Output Stage.
The Data Source is the main reference point when you are performing the following tasks:
- Generating the Output Stage. During the process, we scan the data warehouse schema stored in your Data Source and generate recommended views for the Output Stage.
- Generating a logical data model (LDM). The process scans the Output Stage connected to your Data Source and provides a definition of the LDM, which can then be used for generating the LDM in your workspace.
- Validating the mapping between the Data Source and the LDM. This compares the Output Stage connected to your Data Source to the LDM and returns a list of inconsistencies. Validate the mapping after you have changed the Output Stage or the LDM to see what changes are required.
- Managing data mapping items. You can use data mapping if you want to override the way how data is loaded into workspaces. To do so, provide an alternative mapping scheme for project_id or client_id, respectively.
Note: You can perform all the above tasks using individual API calls. For more information about creating and listing Data Sources, see the API Reference.
The Output Stage is a set of tables and/or views that will serve as a source for loading data to the GoodData platform. You can prepare the Output Stage manually or generate the Output Stage for the Data Source.
If you decide to create the Output Stage manually, make sure that the following requirements are met:
- The Output Stage is located in the warehouse, database, and schema that you specified in your Data Source.
- All views and tables in your Output Stage have the prefix that you specified in the Data Source. All views and tables without the prefix are ignored during the data load.
- All columns are named according to the naming convention (see Naming Convention for Output Stage Objects). Column names can contain underscores (
__) only as part of the prefix.
- All columns in the Output Stage are compatible with the GoodData data types. For more information, see
- The names of the views and tables in the Output Stage do not contain special characters.
- There are no name collisions between your columns/tables and the GoodData technical columns and tables (see 'Special Columns in Output Stage Tables' in Naming Convention for Output Stage Objects).
If you decide to generate the Output Stage for the Data Source, review the resulting SQL code to check the following:
- All columns that should serve as connection points are prefixed with
- All columns that should serve as references are prefixed with
- Attributes that are represented by numeric values (for example, a customer tier that can be 1, 2, or 3) are prefixed with
a__. Unless this is done, the Data Source by default identifies all columns with a numerical data type (
FLOAT, and other) as facts (the prefix
You can generate the Output Stage from a different schema than the schema that will contain the Output Stage.
For better data load performance, we recommend that you apply the following best practices:
- Use tables/views to store data for all customers together with a
client_iddifferentiator. ADD can load such data to the customers’ workspaces much faster than when the data is stored per customer in dedicated tables/views.
- Use incremental loads instead of full loads.