The Microsoft Azure cloud is an ideal platform for data-intensive applications. Designed for productivity, Azure provides pre-built services that make collection, storage, and analysis much easier to implement and manage.

Azure Storage, Streaming, and Batch Analytics

teaches you how to design a reliable, performant, and cost-effective data infrastructure in Azure by progressively building a complete working analytics system.

Summary

The Microsoft Azure cloud is an ideal platform for data-intensive applications. Designed for productivity, Azure provides pre-built services that make collection, storage, and analysis much easier to implement and manage. Azure Storage, Streaming, and Batch Analytics teaches you how to design a reliable, performant, and cost-effective data infrastructure in Azure by progressively building a complete working analytics system.

About the technology

Microsoft Azure provides dozens of services that simplify storing and processing data. These services are secure, reliable, scalable, and cost efficient.

About the book

Azure Storage, Streaming, and Batch Analytics shows you how to build state-of-the-art data solutions with tools from the Microsoft Azure platform. Read along to construct a cloud-native data warehouse, adding features like real-time data processing. Based on the Lambda architecture for big data, the design uses scalable services such as Event Hubs, Stream Analytics, and SQL databases. Along the way, you’ll cover most of the topics needed to earn an Azure data engineering certification.

What's inside

Configuring Azure services for speed and cost

Constructing data pipelines with Data Factory

Choosing the right data storage methods

About the reader

For readers familiar with database management. Examples in C# and PowerShell.

About the author

Richard Nuckolls is a senior developer building big data analytics and reporting systems in Azure.

Table of Contents

1 What is data engineering?

2 Building an analytics system in Azure

3 General storage with Azure Storage accounts

4 Azure Data Lake Storage

5 Message handling with Event Hubs

6 Real-time queries with Azure Stream Analytics

7 Batch queries with Azure Data Lake Analytics

8 U-SQL for complex analytics

9 Integrating with Azure Data Lake Analytics

10 Service integration with Azure Data Factory

11 Managed SQL with Azure SQL Database

12 Integrating Data Factory with SQL Database

13 Where to go next

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