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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

Summary

In this chapter, you have seen how to provision a Synapse Spark pool. You have learned about Spark's architecture in general and Synapse Spark's architecture.

You have learned about the difference between Synapse Spark pools and Synapse Spark instances. You implemented your first Synapse notebook for interactive analytics and learned how to implement a Spark application that can be run as a batch job.

You have seen how to use a Spark pool from an IDE such as Visual Studio Code and you have investigated how to use additional libraries from public sources and your own libraries.

Finally, you saw how you can interact with storage securely, before learning how monitoring works with your Synapse Spark environment.

In Chapter 7, Using Databricks Spark Clusters, you will learn about an alternative Spark environment that Microsoft offers on Azure.

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