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Today, the editor will share with you how to use OLAP for advanced analysis of the data warehouse and SSAS-related knowledge points, detailed content, clear logic, I believe that most people still know too much about this knowledge, so share this article for your reference, I hope you can get something after reading this article, let's take a look at it.

SQL Server Analysis Services (SSAS) is used to create a high-level aggregate view of data, allowing users to quickly create dynamic reports and dashboards to centralize business measurable values, such as key performance indicators (KPI).

problem

After migrating 150 TB of business data to Snowflake, a large global retailer wants to continue to use SSAS for analytical processing and data mining. Based on their old data model, their business team created a set of OLAP cubes in SSAS (see below). When their team tried to make the same cube using Snowflake, they found that the native and open source connection options did not work with SQL Server.

An OLAP cube, also known as a multidimensional cube or super cube, is a data structure that stores aggregated data and allows near-instant data analysis due to pre-calculated value sets.

Solution

To rebuild their OLAP cubes, their team eventually chose CData to integrate Snowflake with SSAS. CData provides a direct SQL interface for Snowflake, allowing its engineers to quickly and efficiently connect cubes previously built by their team to their Snowflake data. Once the cubes are installed, their business units can evaluate, analyze, and mine the data.

Process

Creating data sources and views, building and deploying cubes from data in a SSAS project is as simple as installing a CData ADO.NET provider.

Create a data source for Snowflake

In your SSAS project, create a new data source, select the CData ADO.NET provider, and enter your Snowflake credentials.

Create a data source view

After you create the data source, create a new data source view, select the newly created data source, select the foreign key matching pattern, and select the table to add.

Create cubes for Snowflake

Finally, build a new cube and select the tables and metrics you want to include in the cube, as well as the dimensions to be generated. At this point, you have an OLAP cube for Snowflake that can be used for analysis, reporting, data mining, and so on.

Gain actionable insight from business data

The company chose to use CData Snowflake Adapter because it can integrate in real time with SSAS and standards-based connections, regardless of where the data is stored. This way, when users move the entire business data to the new data warehouse, they can continue to use their SSAS data cube.

These are all the contents of the article "how to use OLAP for Advanced Analysis and SSAS of data Warehouse". Thank you for reading! I believe you will gain a lot after reading this article. The editor will update different knowledge for you every day. If you want to learn more knowledge, please pay attention to the industry information channel.

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