As evolving cloud capabilities are transforming systems and IT landscapes for many companies, setting up a solid data and analytics platform to break their data silos typically takes precedence in their business transformation.
As part of their transformation journey, customers need to collect data from various sources within the organization to create complex reporting capabilities that can provide a “single version of the truth” about various business events to make informed decisions and innovate.
We will set up a data and analytics platform to consolidate SAP data along with non-SAP application data to get better insights into the frequency of customer service calls.
We will see how to use SAP Data Services – an application-level extractor – to extract the sales order data from SAP to Amazon Redshift for data warehousing, analyze the frequency of customer service calls with Amazon Connect data sets stored in Amazon Simple Storage Service (Amazon S3).
We will use AWS Glue to transform the service call data and load the data to Amazon Redshift.
For the reporting layer, we will use Amazon QuickSight and SAP Analytics Cloud to filter the frequency of customer service by sales order type.
Centralized lake house architecture using Amazon Redshift
➡️ For this blog, we will use SAP Operational Data Provisioning (ODP), a framework that enables data replication capabilities between SAP applications and SAP and non-SAP data targets using a provider and subscriber model. Let us start with the SAP source, and look at the steps involved.
For this tutorial, you should have the following prerequisites: