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 architecture for AI

Data is an essential ingredient for any AI scenario. One of the key elements for the success of an AI project is the data architecture – how to bring data together, store and process it, bring the results back, and integrate the insights and actions back into the applications. The following are some of the typical challenges of the data architecture for AI:

  • Data is located across different data sources based on different formats, systems, and structured and unstructured data types.
  • Data integration and consolidation require a common data model.
  • Replicating data involves how to address data privacy and data protection concerns, as well as other compliance requirements.
  • The data platform provides data for the AI execution engine and also needs to address data ingestion, data storage, and data lifecycle management.
  • AI requires metadata such as labeling for supervised learning.
  • AI lifecycle events can be tightly coupled with the...