Integrate a Data Source into a GoodData Workspace

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

  • Amazon Redshift
  • Snowflake
  • Google BigQuery

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

You will need an empty GoodData workspace. If you do not know how to create one, see Create a Workspace.

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

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    Log in to your Redshift cluster with the account that you plan to use with GoodData. Ensure that the user that you configured in the data source has all necessary privileges and that your Redshift cluster can be accessed by GoodData. For more information about the required privileges, see GoodData-Redshift Integration Details.

    Ensure, that you have the following information ready:

    • Redshift username and password
    • Redshift database and schema
    • GoodData workspace's Project ID (Find the Project ID)
  • Snowflake

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    Log in to your Snowflake instance with the account that you plan to use with GoodData. Ensure that the user that you configured in the data source has all necessary privileges and that your Snowflake instance can be accessed by GoodData. For more information about the required privileges, see GoodData-Snowflake Integration Details.

    Ensure, that you have the following information ready:

    • Snowflake username and password
    • Snowflake database, source, and schema
    • GoodData workspace's Project ID (Find the Project ID)

    In the next section, you will publish the logical data model into your workspace.

  • BigQuery

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    Log in to your BigQuery workspace with the account that you plan to use with GoodData. Ensure that the user that you configured in the data source has all necessary privileges and that your BigQuery workspace can be accessed by GoodData. For more information about the required privileges, see GoodData-BigQuery Integration Details.

    BigQuery and GoodData integration requires the following role levels: bigquery.dataViewer and bigquery.jobUser.

    Ensure, that you have the following information ready:

    • BigQuery project and dataset
    • BigQuery Service Account key in JSON format so you can extract

      • client e-mail

      • private key
    • GoodData workspace's Project ID (Find the Project ID)

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

The screenshots in the following steps use the Snowflake data source, but the steps are the same for each data source.

  1. Click your name in the top right corner, select Data Integration Console, then click the Data sources tab.
  2. Select your data source. Alternatively, click Create data source in the bottom left corner.
    The connection parameter screen appears.
  3. Fill in the required fields.
  4. Click Test connection. If the connection succeeds, the green confirmation message appears.
  5. Click 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, click Connect.
  2. On the pop up that opens, select the workspace that you want to use, then click Select.

    LDM Modeler opens.
  3. On the left tool pane, select your workspace, then click Connect .

    After a while, LDM Modeler connects to your data source and the objects from your data source are displayed.
  4. 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.

  5. 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.

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