Book Image

Real Time Analytics with SAP Hana

By : Vinay Singh
Book Image

Real Time Analytics with SAP Hana

By: Vinay Singh

Overview of this book

SAP HANA is an in-memory database created by SAP. SAP HANA breaks traditional database barriers to simplify IT landscapes, eliminating data preparation, pre-aggregation, and tuning. SAP HANA and in-memory computing allow you to instantly access huge volumes of structured and unstructured data, including text data, from different sources. Starting with data modeling, this fast-paced guide shows you how to add a system to SAP HANA Studio, create a schema, packages, and delivery unit. Moving on, you’ll get an understanding of real-time replication via SLT and learn how to use SAP HANA Studio to perform this. We’ll also have a quick look at SAP Business Object DATA service and SAP Direct Extractor for Data Load. After that, you will learn to create HANA artifacts—Analytical Privileges and Calculation View. At the end of the book, we will explore the SMART DATA access option and AFL library, and finally deliver pre-packaged functionality that can be used to build information models faster and easier.
Table of Contents (16 chapters)
Real Time Analytics with SAP HANA
Credits
About the Author
About the Reviewers
www.PacktPub.com
Preface
Index

Summary


In this chapter, we progressed towards more complex concepts of information modeling. We learned how to create restricted measures and restricted columns. We went further in to the concepts of filters, variables and input parameters and created our own variables. Once the basic building blocks for calculation view were ready, we learned how to create the calculation view graphically. We also learned about analytical privileges and used them to restrict access to the calculation view that we created.

In the next chapter, we will learn about hierarchies and text search in SAP HANA; how to create and use them in our data models for real-time analytics. We will learn how to make use of hierarchies in business intelligence reporting to display characteristics across aggregated nodes. We will explore the benefits of text search in data mining and extracting useful information out of huge amounts of data.