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

Section 1: Data Warehousing and Considerations Regarding Cloud Computing

This section will examine the question of whether data warehouses are still required given the rise of the enterprise data lake and provides a brief overview of the trends and development on the market of data and AI. As cloud computing adds flexible and scalable services to AI, there are no more limits in terms of source formats and volumes that can be processed for AI requirements, and given that AI and machine learning are on everybody's mind at the moment, the book attempts to ask what all this entails and where we are heading. In addition, we'll take a technology-agnostic look at the components that make up a successful analytical system. From an agnostic viewpoint, we will try to find the right Azure services to build a modern data warehouse.

This section comprises the following chapters:

  • Chapter 1, Balancing the Benefits of Data Lakes over Data Warehouses
  • Chapter 2, Connecting Requirements...