Explaining Data Warehouse Cloud

After completing this lesson, you will be able to:

After completing this lesson, you will be able to:

  • Describe SAP Data Warehouse Cloud

SAP Data Warehouse Cloud

The concept of data warehousing was initially defined in the late 1980s. A popular definition originates from Bill Inmon, who described it as "a subject-oriented, integrated, time-variant, and non-volatile collection of data in support of management's decision making process". These four key properties mean the following:

  1. Data is collected about entities that have an impact on the future success of the organization (for example, vendor, product, customer) and their relationships (for example, key figures representing processes like purchasing, production or sales).
  2. Data is integrated from many different sources, and as a result, they are available in a consolidated way in a separate system.
  3. Data is collected for different points of time to enable time series analysis and comparisons. So, the time dimension has a crucial significance.
  4. Data is not changed anymore. It is stable and permanent once it is transferred to the data warehouse.

The SAP data warehouse offerings are SAP Business Warehouse (BW) (in maintenance until end of 2027), SAP BW/4HANA (at least supported until end of 2040), and last but not least, SAP Data Warehouse Cloud (SAP DWC). The classic solutions, SAP BW and SAP BW/4HANA, are widely in use with customers due to the high amount of standard content and the easy integration with other SAP solutions. Both are classic on-premise applications, but they are now also enabled to be deployed in private cloud environments.

To close the gap towards a full public cloud offering, SAP launched SAP Data Warehouse Cloud in the end of 2019. SAP Data Warehouse Cloud is a pure SaaS approach, sometimes also referred to as DWaaS (Data Warehouse as a service). It is deployed by the well-known hyperscalers in different continents and it receives updates in a biweekly cycle currently.

There are three major use cases for SAP Data Warehouse Cloud:

  1. Self-Service Data Modeling and Analytics targets experienced key business users and empowers them with self-service, top-down driven analytics in an IT governed environment via so-called SAP Data Warehouse Cloud Spaces.. In this dedicated environment, these users can integrate data, model, and analyze it independently from IT.
  2. Data Democratization describes the requirement to accelerate time-to-insight via rapid onboarding of resources. A lower adoption barrier is achieved with a business semantic layer, which abstracts from the underlying physical data sources. An important value added is provided by delivered business content which enables setting up industry-specific data models including core KPIs quickly.
  3. End-to-End Data Warehousing focuses on reusing existing SQL skills, development environments, data integration tools, governance, and harmonization to provide a consolidated view across the enterprise.

It is important for SAP to keep customers' investment from existing on-premise investments: First of all, customers can leverage so-called hybrid scenarios based on integration with existing on-premise landscapes. In addition, SAP introduced the SAP BW Bridge in SAP Data Warehouse Cloud that provides a path to the public cloud for on-premise landscapes. It is basically a dedicated SAP ABAP stack in the SAP Business Technology Platform, into which customers can move their on-premise BW applications to the public cloud in their own pace by keeping their past investments. Recommended reading: https://blogs.sap.com/2021/11/17/sap-data-warehouse-cloud-sap-bw-bridge-overview-and-technical-deep-dive/

SAP Data Warehouse Cloud Architecture Level 1

SAP Data Warehouse Cloud is deeply integrated with other SAP cloud solutions and relies on following components:

1. SAP HANA Cloud
Under the hood, SAP HANA Cloud powers SAP Data Warehouse Cloud. Depending on the type of data source and the business requirements, it offers a variety of ways to collect and connect with sources data. There are several capabilities for virtualization approaches (remote access) or replication scenarios (physical copy of data) available. On top of SAP HANA Cloud, SAP Data Warehouse Cloud unifies the data and provides the foundation for enterprise data warehousing and analytics.
2. SAP Analytics Cloud
SAP Analytics Cloud serves as the overall SAP Data Warehouse Cloud consumption layer with the prime responsibility to support all kinds of analytics and planning enterprise-wide.

SAP Data Warehouse Cloud Architecture Level 2

This exemplary use case for SAP Data Warehouse Cloud covers the business area of sales. The use case is simplified with focus on the relation between sources of data, SAP Data Warehouse Cloud, and SAP Analytics Cloud.

In our case, sales data is not integrated in one system, which would obviously be the ideal state. In reality, data is often distributed in different non-integrated heterogeneous systems. In our example, sales data is managed in following three sources:

  1. SAP BW on-premise for master data, which is already consolidated there from different other sources
  2. SAP S/4HANA Cloud, which is the core ERP system managing the complete sales process
  3. External 3rd party CRM system, which manages enhanced customer master data like a customer specific ABC-rating, which has been generated based on specific rules of this organization

In our use case, the business requirement is to provide sophisticated sales reporting and planning on these three sources of data in an SAP solution. A suitable approach would be the following:

  • Data is integrated and modeled in SAP Data Warehouse Cloud and finally prepared for consumption in SAP Analytics Cloud.
  • Customer master data is directly connected from SAP BW based on the SAP Data Warehouse Cloud remote access capabilities to InfoObjects in SAP BW.
  • All sales transactions are read from SAP S/4HANA Cloud based on the SAP Data Warehouse Cloud remote access capabilities to the standard CDS Views.
  • CRM attributes are replicated into SAP Data Warehouse Cloud to enhance customer master data.
  • These three sources of data are represented in SAP Data Warehouse Cloud Data Layer artifacts like Remote Tables, Dimensions, and Analytical Datasets.
  • The SAP Data Warehouse Cloud Business Layer enhances our scenario by customer specific KPIs as well as calculated attributes and prepares the SAP Data Warehouse Cloud artifacts in so-called Analytic Models for consumption.
  • In the end, SAP Analytics Cloud stories provide the interfaces for the business users to access and analyze the data.

More information about SAP Data Warehouse Cloud

Learn more about SAP Data Warehouse Cloud on the official product page: https://www.sap.com/products/technology-platform/data-warehouse-cloud.html

Get product assistance from the official help documentation: https://help.sap.com/docs/SAP_DATA_WAREHOUSE_CLOUD?locale=en-US

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