November 19, 2020

KPI Dashboards: Zoom into Insights

On KPI Dashboards, you can now zoom to display your data in greater detail.

Zooming is supported on all types of charts, except for tables, headlines, pie charts, and donut charts.

With the Shift key, you can also move through your zoomed insight.


Learn more:
Edit KPI Dashboards


LDM Modeler: Create a Logical Data Model Directly from Your Data Warehouse

You can now create a logical data model (LDM) in your workspace directly from your data warehouse (such as Snowflake, Redshift, or BigQuery). The LDM Modeler will read tables and views from the schema that you specify, and you can use those tables and views to create datasets for your LDM.

Creating an LDM directly from your data warehouse maps the fields in a dataset to the columns in the table/view that the dataset was created from and stores it in the GoodData platform. The mapping allows you to load data directly from your data warehouse to your workspace without the need to create the Output Stage as an interim step.

What if you are already using the Output Stage?
You can keep using the Output Stage. Nothing is going to change in your workflow.

Creating an LDM directly from a data warehouse is just another method of data modeling, and you can use either.

How does it work in a nutshell?

  1. Create a Data Source for your data warehouse.
  2. Go to LDM Modeler in your workspace, and connect this Data Source to the LDM Modeler.
    The LDM Modeler fetches the tables and views from the data warehouse.
    List_of_Data_Sources.png  -->  Tooltip.png
  3. Drag-and-drop tables/views to the LDM Modeler to create datasets.
    To check the mapping between a dataset's fields and the columns in the data warehouse table/view, go to the dataset's details and switch to the Load configuration tab.
  4. When the LDM is ready, publish it to the workspace.
  5. Set up a data loading process as usual.

Learn more:
Create a Logical Data Model from Your Cloud Data Warehouse


Direct Data Distribution from Amazon S3 Object Storage Service: Support for Source Files Compressed with gzip

In addition to raw CSV files and compressed CSV files (.zip files), Automated Data Distribution (ADD) v2 now supports source CSV files compressed by gzip.

If you use an S3 bucket to load data to your workspaces, you can provide the source data as .gz files.

Learn more:
GoodData-S3 Integration Details

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