Book Image

Cloud Scale Analytics with Azure Data Services

By : Patrik Borosch
Book Image

Cloud Scale Analytics with Azure Data Services

By: Patrik Borosch

Overview of this book

Azure Data Lake, the modern data warehouse architecture, and related data services on Azure enable organizations to build their own customized analytical platform to fit any analytical requirements in terms of volume, speed, and quality. This book is your guide to learning all the features and capabilities of Azure data services for storing, processing, and analyzing data (structured, unstructured, and semi-structured) of any size. You will explore key techniques for ingesting and storing data and perform batch, streaming, and interactive analytics. The book also shows you how to overcome various challenges and complexities relating to productivity and scaling. Next, you will be able to develop and run massive data workloads to perform different actions. Using a cloud-based big data-modern data warehouse-analytics setup, you will also be able to build secure, scalable data estates for enterprises. Finally, you will not only learn how to develop a data warehouse but also understand how to create enterprise-grade security and auditing big data programs. By the end of this Azure book, you will have learned how to develop a powerful and efficient analytical platform to meet enterprise needs.
Table of Contents (20 chapters)
1
Section 1: Data Warehousing and Considerations Regarding Cloud Computing
4
Section 2: The Storage Layer
7
Section 3: Cloud-Scale Data Integration and Data Transformation
14
Section 4: Data Presentation, Dashboarding, and Distribution

Chapter 13: Introducing Industry Data Models

A challenge that you already might have come across when modeling your analytical environment is to create not only the model but also all its details. When you need to integrate data from different source systems, you don't want to forget the necessary details when you create your target model.

There is nothing worse than identifying missing attributes or wrong data types in a target object when you are already two-thirds of the way through your implementation. Adjusting data mappings and cleansing routines and formulas at a late stage in your development process can be a cumbersome situation that slows down your development and extends your delivery time. Industry data models that reflect years of experience and best practices can help you accelerate your development.

This chapter will give you an overview of the industry data models that you can leverage using Microsoft's Common Data Model (CDM). You will discover the...