Introduction

Your installation came with a sample data source, and metadata that defines a data warehouse.

After installing Ajilius, you connected the metadata to a database, ready to build your data warehouse.

Now we’re going to deploy and build the warehouse from the sample data.

Create Tables

The deployment step is the execution of scripts to create the data warehouse tables and initial data values.

Select the Create Scripts option from the Schedule menu:

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Note the warning that deployment of a full data warehouse may take some time, and press the Create action button:

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We’re only deploying a small data warehouse, so execution of your scripts will only take a couple of seconds. Review the results for successful execution:

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Build Warehouse

Now that we’ve created all the tables in the data warehouse we’re ready for the final step, to build the warehouse using the ELT scripts generated from our metadata.

Select the Process Scripts option from the Schedule menu:

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Note the warning that processing the scripts might take some time to complete, and press the Execute action button:

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We’re only deploying a small data warehouse, so execution of your scripts will only take a couple of seconds. Review the results for successful execution:

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Ready for BI

Our sample data warehouse is now complete, and ready for use with your preferred BI tools.

For a quick validation, let’s view the contents of the fact_sale fact table.

Select the List Fact Tables option from the Transform menu:

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Next, select the View Data option from the context menu for the fact_sale table:

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And here is the data from your fact table:

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Next Steps

We’ve now completed the deployment and build of a sample data warehouse with Ajilius. If you’ve followed all the steps in the documentation to this point, you should have a good working knowledge of how Ajilius works, and it is time to concentrate on how to design and specify your data warehouse.

We recommend that you work through the remaining documentation sections, structured in the order you will build your data warehouse.