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Microsoft provides a common dataset called AdventureWorks when learning how to use SQL Server. Using SquaredUp, the AdventureWorks dataset can easily be visualized in any organization.

Shawn Williams, SquaredUp


For each release of SQL Server, Microsoft also provides a sample database called AdventureWorks. Is it possible to visualize data from the AdventureWorks data warehouse database using SquaredUp?


Microsoft provides a sample database for various workload types like OLTP, Data Warehouse, and Lightweight workloads for each release of SQL Server. Since each release of SQL Server results in a new version of AdventureWorks, the user may be required to make a few alterations to the data for visualization purposes. For example, the database doesn’t have any current data in some cases. The initial target for this dashboard was the AdventureWorks 2017 dataset. In several instances, the data for the tile was duplicated so that the graph appears continuous for visualization purposes.

Dashboard walk-through

Each tile on the dashboard is a different SquaredUp visualization. Here is how it breaks down:

  1. Tile 1 (Bar) – Simple SQL query using a Group By to count customers by region.
  2. Tile 2 (Graph) – A more complex query to duplicate records to create a continuous graph (since the last record in the dataset is from 2017). Page Timeframe is also used to demonstrate a different approach to browsing the dataset.
  3. Tile 3 (Scalar) – A simple query to show which Page Timeframe was applied for the dashboard.
  4. Tiles 4, 5, and 6 (Scalar) – A scalar tile that shows health information based on criteria within the query.
  5. Tiles 7 (Status Icon) – Summarized results are assigned a health state and then placed on an image.
  6. Tiles 8 (Status Blocks) – Similar to the Scalar tiles, but demonstrates how to use a complex Case statement to assign health state to a result set.
  7. Tiles 9 (Donut) – A simple summary query.
  8. Tile 10 (Sparkline) – The same query used in Tile 2, but the results are shown in a Sparkline instead of a single graph.

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