Getting Started with GoodData CSV Uploader

In this tutorial you will learn how to load a sample of sales data into your GoodData workspace and start building insights.

We assume that you have a GoodData account and a workspace. Workspace, also known as a project, is the place where you load your data, and where you create metrics, share data visualizations and dashboards, create ad hoc analyses, and much more.

In this tutorial, you will work with a sample CSV file. Before you upload your own CSV file, ensure that you read Adding Data from a File to a Workspace for CSV specifications.


Prerequisites - Before You Load the Data

Ensure that you are logged into your GoodData account.

Your GoodData Domain

This tutorial presumes that your GoodData domain is

  • GoodData Free users
    use the link you received in your GoodData Free confirmation email, for example:
  • GoodData Growth Users
    use the link you received in your GoodData Growth confirmation email, for example:
  • White-label customers
    use your own white-label domain

Your GoodData Workspace

Before you load any data to the GoodData platform, you must create at least one workspace (also known as a project). The method depends on your GoodData pricing plan.

For details, see Create a Workspace (Project).

Your GoodData workspace is the place where you load your data, create metrics, share data visualizations and dashboards, create ad hoc analyses, and much more. Each workspace has its own project ID.

Load the Sample Data into Your Workspace

Review the following animation to get familiar with the user interface and the process itself. You can follow the data loading procedure in a step-by-step guide further below.

To load the data:

  1. Download the order_lines.csv file. Ensure the downloaded file keeps its *.csv suffix.
  2. Select the workspace/project that you want to use to upload your CSV file.
    The Default dashboard page with your workspace opens.
  3. Click Load in the top navigation bar.
    The Load page opens.
  4. Click the blue + Add Data button, then select and upload the CSV file.
  5. On the Verify the data from “order_lines.csv” page that appear, click Done.
    Note: The Verify your data page give you an option to customize how the columns are presented in the GoodData workspace. You can annotate your columns as measures (also called also facts), attributes, and dates. For the purpose of this tutorial, leave the settings unchanged.

    GoodData will begin processing your data.

When the upload finishes, a green notification bar will prompt you to ‘Start analyzing’ your data. Alternatively, click the blue Analyze button in the My Data section on the Orders line. Both options will bring you to the Analytical Designer screen.

Review Your Logical Data Model

When you loaded data in your workspace, an elementary logical data model (LDM) was created. An LDM determines how the data are handled and displayed. If are curious to see how the CSV structure is interpreted by GoodData, review your logical data model.


  1. Go to your workspace.
  2. Click Manage in the top navigation tab.
  3. On the Data tab, click Model.
    The LDM outline appears:

Create Your First GoodData Insights

Now that you loaded the sample data into your GoodData workspace, you will learn how to create simple insights using Analytical Designer available in the Analyze tab.

To go to Analytical Designer from your Data Integration Console, click your account name in the upper right corner, then click Analyze data.

The goal of this tutorial is to break down raw sales figures by order category and status, and examine the pricing structure of your sales.

As you can see, the columns of the original csv sample file appear in the catalog panel on the left. This is possible thanks to the GoodData’s ability to work directly with human readable data modeling metadata (facts, attributes, column names).

Exercise 1 - Order Category and Order Status

To create your first insight:

  1. Drag and drop Order ID onto the Measures panel. This automatically creates a Count of unique Order IDs.
    Analytical Designer applied Count because the Order ID column was annotated as an attribute instead of as a numerical fact.
  2. In the Compare helper, select Product Category from the drop-down menu and click Apply.
    The number of orders is now split by the product category.
  3. Drag and drop Order Status to the Stack By panel to look into the data in more detail..
    The columns are now further split by the status of the orders.
  4. Click the Save button in the top right corner of the screen to save the insight and name the insight it Orders by Status and Category.
    You have just created your first insight! 

Exercise 2 - Sales Pricing Structure

In the following example, your insights will analyze the pricing structure of your sales - the highest priced items and the price range.

Follow these steps:

  1. Click the Clear button in the toolbar to clear the insight.
  2. Drag and drop Price onto the Measures panel.
    This displays the Sum of all prices on all order lines but it does not consider how many times the products were sold at their price.
    Note: You can apply different mathematical functions to this particular column, because the Price column was annotated as a numerical fact.
  3. In the Measures panel, click the arrow to the left of Sum of Price item, and from the drop-down menu select Average to display the average product price.
  4. Drag Category to the View By panel.

You see that the Outdoor category contains the highest priced items. But what is the range of prices?

  1. In the Measures panel, click the arrow to the left of Avg of Price and change Average to Minimum.
  2. Drag and drop Price to the Measures panel again.
    A new Sum of Price item appears.
  3. Click the arrow to the left of Sum of Price, and from the drop-down menu, change Sum to Maximum. You can now see the range of prices for each category.

You can easily handle many analytical queries without needing to write SQL for individual variations.

Next Steps

Now that you created your first insights using our sample data, you can either:

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