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Cloud Scale Analytics with Azure Data Services

Cloud Scale Analytics with Azure Data Services

By : Borosch
4.9 (7)
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Cloud Scale Analytics with Azure Data Services

Cloud Scale Analytics with Azure Data Services

4.9 (7)
By: 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)
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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

Implementing a data model in your Data Lake

You can argue that this should already be done in your Data Lake Storage. But if you examine the capabilities of the services when it comes to interconnectivity and the options to query data directly from the Data Lake without loading it into a database, we can set up a hybrid data model and not only use these functionalities to save a lot of money, but also reduce complexity and loading time windows in your data warehouse.

The rising data volumes that you might face can impact your loading and query strategy heavily. And even with the Azure backbone network, you will come to a point where the latency for loading your data into your data warehouse will no longer be sufficient.

Understanding interconnectivity between your data lake and the presentation layer

Later in this book, we will dive into the Synapse SQL pools (Chapter 4 and Chapter 11) and Synapse SQL on-demand (Chapter 11). These databases – or better, SQL services...

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