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Cloud Scale Analytics with Azure Data Services
By :

Cloud Scale Analytics with Azure Data Services
By:
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)
Preface
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Section 1: Data Warehousing and Considerations Regarding Cloud Computing
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Chapter 1: Balancing the Benefits of Data Lakes Over Data Warehouses
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Chapter 2: Connecting Requirements and Technology
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Section 2: The Storage Layer
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Chapter 3: Understanding the Data Lake Storage Layer
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Chapter 4: Understanding Synapse SQL Pools and SQL Options
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Section 3: Cloud-Scale Data Integration and Data Transformation
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Chapter 5: Integrating Data into Your Modern Data Warehouse
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Chapter 6: Using Synapse Spark Pools
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Chapter 7: Using Databricks Spark Clusters
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Chapter 8: Streaming Data into Your MDWH
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Chapter 9: Integrating Azure Cognitive Services and Machine Learning
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Chapter 10: Loading the Presentation Layer
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Section 4: Data Presentation, Dashboarding, and Distribution
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Chapter 11: Developing and Maintaining the Presentation Layer
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Chapter 12: Distributing Data
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Chapter 13: Introducing Industry Data Models
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Chapter 14: Establishing Data Governance
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