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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

Chapter 9: Integrating Azure Cognitive Services and Machine Learning

Advanced intelligence efforts are gathering speed and the potential for integrating machine learning models with data processing, for example, to detect fraud, predict device failure, or simply to warn a company before a customer moves on, is promising. The seamless integration of ML models into your data pipelines, and with this the shortest possible time to production, is therefore key.

In this chapter, you are going to examine predefined machine learning models that you can provision and instantly consume as services on Azure. You will learn how to implement them in a Synapse Spark notebook to use them with your data.

In the second part, we will have a look at the Azure Machine Learning service and the available features there. You will examine the graphical interface of Azure ML and learn how to implement your own ML model and expose it to your modern data warehouse environment.

This chapter will not...

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