Integrate a Data Source into a GoodData Workspace

You can directly connect any of the following data sources to your GoodData workspace:

• Amazon Redshift
• Amazon S3
• Snowflake
• Google BigQuery
• PostgreSQL
• Microsoft Azure Blob Storage

Each data source has different integration requirements. Before you connect the data source to your workspace, ensure that GoodData can communicate with your data source. This article will explain how to connect the data source to your GoodData workspace.

Contents:

Prerequisites

• An active GoodData account that you are logged into with at least one active workspace.

• Access to a supported data source with data.

  To better understand how GoodData processes rows and columns, we recommend that you use our sample data for your first integration. For more information, see Import Sample Data to Your Data Source.

You will perform the following tasks:

1. Create a Data Source
   You will connect your GoodData workspace with your data source.
2. Create a logical data model (LDM)
   Use the structure of your source data to create 'a map' to arrange data in the workspace.
3. Load the data from the source to your GoodData workspace
4. Create a schedule to load your data

Create a Data Source

A Data Source is a place in your GoodData workspace that stores the information about the connection with your data source.

Select your source and learn what details you need to establish connection between your GoodData workspace and your data source.

Redshift Data Source

Snowflake Data Source

BigQuery Data Source

PostgreSQL Data Source

S3 Data Source

Azure Blob Storage Data Source

To connect your data source and your GoodData workspace, follow these steps:

1. On the top navigation bar, select Data.
   
2. Select Sources.
   
3. Select the data source to connect to your workspace.

4. Provide the required information.

   BigQuery only

   1. When prompted, upload your service account key file.
      
      The following fields will be populated automatically:
      1. Client email
      2. Private key
      3. Google project ID
   2. Fill in the field for the data source name.

5. Select Test connection. If the connection succeeds, the green confirmation message appears.
   

   Use the Output Stage if you cannot or do not want to download the data directly from the production tables in your data warehouse. For more information, see Direct Data Distribution from Data Warehouses and Object Storage Services.

6. Select Save.
   The screen with your connection details appears.

Connect the Data Source to LDM Modeler

Once you verified that the connection between the data source and your workspace works, follow these steps to connect to LDM Modeler.

1. On the Data Source Summary page, select Connect.
   
   LDM Modeler opens.
2. Drag and drop an object from Data Sources onto the Logical Data Modeler canvas.
   
   A preview of the data opens with the data processed according the following guidelines:
   • GoodData will attempt to match each column to the correct data model type based on the contents of the field. Numbers are automatically detected as measures; dates are automatically converted into a separated date dataset; and any columns with letters are automatically detected as attributes. Notice what the columns are set to in the following example:
   • The order_line_id, order_id, and order_status columns are correctly detected as attributes.
   • The date column is correctly detected as dates in the yyyy-MM-dd format and will be converted to a separate Date dataset.
   • The price and quantity columns are correctly detected as measures(or facts).
     

   • Columns that contain product identifiers or other numerical values that cannot be numerical constants in equations cannot be used as a measure. In the following example, Product ID is autodetected as a Measure but it should be changed to Attribute.

     

3. Select Import after you verify the information is correct.

   To avoid having to import data again, any numerical value that cannot be used as a numerical constant in an equation must be set as an Attribute before you select import.

   LDM Modeler opens and displays the structure of your dataset.

4. Select Import and repeat steps 3 through 5 for each additional dataset.
5. (Optional) Create a Relationship between Datasets.
6. Publish your Logical Data Model.