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Working with Azure SQL Data Warehouse

Building on the last step, Graeme will show us some of the benefits and features of working with an Azure SQL Data Warehouse.

Building on the last step, Graeme will show us some of the benefits and features of working with an Azure SQL Data Warehouse.

For the majority of its life, a data warehouse may lie dormant merely storing data, and thus doesn’t require a lot of processing power.

However, when the time comes to draw reports from the warehouse, it may be that you’re working with so much data or running so many calculations (or both!) that you need more processing power to decrease the amount of reporting time.

Azure SQL Data Warehouses can have their processing power increased as required, and scaled back down once the reporting has completed.

Star or Snowflake schema

As mentioned in the previous step, data warehouses take a star or snowflake schema.

a star schema and snowflake schema are shown side-by-side

The main difference (as shown above) is that in a snowflake schema, more dimension tables break away from the first set of dimension tables.

New SQL commands

  • ORDER BY (ASC | DESC) – another clause to sort the results from a query.
    • The addition of ASC or DESC causes the results to ascend or descend in value.
    • If the results are text values, this will mean alphabetical or reverse alphabetical order.
  • SUM – if the results of the query are numerical, this clause will present a total of that column.
Note: Why not explore more about ORDER BY, the new SQL command introduced in this step? You can find more information at W3Schools’ SQL site.

In the next step, you’ll put some of the concepts discussed in this activity into practice in the Working with a Relational Database in Microsoft Azure CloudSwyft Hands-On Lab. Once you’ve completed the lab, gauge your understanding of the content in the Knowledge Check that follows.

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