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

SAP Data Warehouse Cloud (DWC)

Before we introduce what SAP DWC is, first, let’s understand the differences between a data lake and a data warehouse.

A data lake is a data repository that holds a large amount of raw data in its natural format, often coming from disparate sources, including a mix of structured, semi-structured (CSV, XML, and JSON), and unstructured data formats. It offers an effective solution for collecting and storing large amounts of data but doesn’t necessarily need to process it until it is required for use.

In contrast, data warehousing has a more focused use case that is optimized to store and transform large amounts of data for advanced queries and analytics in a more structured relational database. It has two key functions: it acts as a federated repository of business data, and then as the query execution and processing engine of the data. The goal of a modern data warehouse...