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Build an Azure SAP Data Warehouse with AnalyticsCreator
Short answer
- How do you build an Azure SAP data warehouse with AnalyticsCreator?
- How does AnalyticsCreator connect to SAP metadata?
- What modelling approaches are supported by the Data Warehouse Wizard?
- How are Power BI measures and semantic models generated?
- What is included in an AnalyticsCreator deployment package?
- How do you deploy and load SAP data into Azure and Power BI?
Build an Azure SAP Data Warehouse with AnalyticsCreator
- AnalyticsCreator automates the creation of Azure-based SAP data warehouses.
- SAP metadata is imported using the SAP FI metadata connector.
- The Data Warehouse Wizard supports Kimball, Data Vault 2.0 and mixed modelling approaches.
- Import filters reduce the amount of source data loaded.
- Calendar dimensions and measures can be added with reusable macros.
- Synchronisation validates the model before creating the data warehouse.
- Deployment packages generate all required deployment assets automatically.
- Generated assets include DACPAC files, SQL scripts, XMLA scripts, Azure Data Factory ARM templates, SSIS packages and Power BI semantic models.
- SAP data is loaded into Azure through the generated ETL process.
- The generated semantic model can immediately be used to build Power BI reports and dashboards.
Transcript
[00:00:04]
AnalyticsCreator makes it easy to create a data warehouse that integrates data from any number of sources, from ERP and CRM systems to on-premises and cloud environments.
This video illustrates how quickly you can build an Azure data warehouse to analyse and visualise SAP data in Power BI.
With AnalyticsCreator open, let us walk through the steps required to build a draft data warehouse.
The first step is to create a new repository.
[00:00:37]
The next step is to add a connector.
Here, the SAP FI Metadata Connector is selected. This retrieves the necessary information about the SAP tables.
[00:00:51]
The Data Warehouse Wizard is launched by right-clicking the Sources folder.
The wizard offers several options for generating an initial data warehouse template, including different modelling approaches such as Data Vault 2.0, Kimball and mixed designs.
From here, the tables required for more detailed analysis can be selected.
For example, all tables related to customer bookings can be selected.
[00:01:26]
Further customisation options are also available in the wizard.
[00:01:34]
Here is the first draft of the data warehouse.
[00:01:45]
To restrict the amount of imported data, filters are added to the import definitions.
For example, the year in the Accounts Header table is limited to 2013.
The Accounts Segment table is also limited to 2013.
[00:02:17]
To modify the definition of the fact transformation, a reference to the Calendar Dimension is added using a macro.
[00:02:41]
Additional measures, such as Amount and Quantity, can also be added to the fact transformation.
AnalyticsCreator includes a powerful feature that allows macros to be defined.
These reusable transformations, or building blocks, can be used anywhere in the data warehouse.
[00:03:00]
The changes now need to be synchronised with the data warehouse.
AnalyticsCreator automatically runs a series of checks before creating the data warehouse.
[00:03:13]
The fact transformation is now renamed to Fact Bookings for the Power BI model.
[00:03:28]
The names of the measures for the Power BI model can be defined either manually or automatically based on a template.
[00:03:54]
The data warehouse model is now ready.
[00:04:00]
In the next step, a package is created to deploy the data warehouse to Azure and Power BI.
[00:04:08]
The deployment package is a Visual Studio solution containing DACPAC files, SQL and XMLA scripts, Azure Data Factory ARM templates, SSIS packages, Power BI models and all other required elements.
This makes it possible to deploy the data warehouse either on-premises or in the cloud.
[00:05:10]
The deployment package has now been created.
Using SQL Server Management Studio, the Azure data warehouse created during deployment can be opened.
[00:05:44]
The SAP connection parameters can now be defined in the SSIS configuration table.
[00:05:57]
The data warehouse is now configured to run the ETL process.
[00:06:19]
The Visual Studio package created during deployment is opened, and the ETL process can now be started.
[00:06:59]
With all checks complete, the data from the SAP system has been loaded into the data warehouse.
[00:07:04]
Power BI is opened.
The semantic model created by AnalyticsCreator during deployment can now be seen.
[00:07:14]
When the semantic model is refreshed, a Quick Insights report can be created.
[00:07:25]
These are only a few of the examples generated by Power BI.
The full functionality of Power BI can be used to create intuitive dashboards and informative reports based on SAP data.
[00:08:09]
As you can see, AnalyticsCreator provides a fast and straightforward way to build a data warehouse for reporting on and analysing data from any number of sources.
To experience the full functionality of AnalyticsCreator, visit analyticscreator.com and select the trial option.
Thank you for watching.
