Book Image

Architecting Solutions with SAP Business Technology Platform

By : Serdar Simsekler, Eric Du
Book Image

Architecting Solutions with SAP Business Technology Platform

By: Serdar Simsekler, Eric Du

Overview of this book

SAP BTP is the foundation of SAP’s intelligent and sustainable enterprise vision for its customers. It’s efficient, agile, and an enabler of innovation. It’s technically robust, yet its superpower is its business centricity. If you’re involved in building IT and business strategies, it’s essential to familiarize yourself with SAP BTP to see the big picture for digitalization with SAP solutions. Similarly, if you have design responsibilities for enterprise solutions, learning SAP BTP is crucial to produce effective and complete architecture designs. This book teaches you about SAP BTP in five parts. First, you’ll see how SAP BTP is positioned in the intelligent enterprise. In the second part, you’ll learn the foundational elements of SAP BTP and find out how it operates. The next part covers integration architecture guidelines, integration strategy considerations, and integration styles with SAP’s integration technologies. Later, you’ll learn how to use application development capabilities to extend enterprise solutions for innovation and agility. This part also includes digital experience and process automation capabilities. The last part covers how SAP BTP can facilitate data-to-value use cases to produce actionable business insights. By the end of this SAP book, you’ll be able to architect solutions using SAP BTP to deliver high business value.
Table of Contents (22 chapters)
1
Part 1 Introduction – What is SAP Business Technology Platform?
4
Part 2 Foundations
8
Part 3 Integration
12
Part 4 Extensibility
16
Part 5 Data to Value

Data Integration

You may remember from the previous chapters that we separated data integration from other integration use cases covered under cloud integration. Since data integration differs from the process integration style, the patterns and tools used for these two categories are distinct.

With data integration, we bring together data from different sources so that the combined data has a meaning for a specific purpose. This may be for operational requirements; however, it is mostly for analytics and reporting purposes such as producing business insights or replicating data into a data warehouse. Data integration is also naturally relevant to big data and artificial intelligence (AI)/machine learning (ML) use cases.

As you can remember, we covered master data integration in the previous chapter because its patterns and motivations are more akin to the process integration style.

We will cover the following topics in this chapter:

  • Why do we need data integration...