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Microsoft Azure has over 20 platform-as-a-service (PaaS) offerings that can act in support of a big data analytics solution. So which one is right for your project? This practical book helps you understand the breadth of Azure services by organizing them into a reference framework you can use when crafting your own big data analytics solution.
You’ll not only be able to determine which service best fits the job, but also learn how to implement a complete solution that scales, provides human fault tolerance, and supports future needs.
Understand the fundamental patterns of the data lake and lambda architecture
Recognize the canonical steps in the analytics data pipeline and learn how to use Azure Data Factory to orchestrate them
Implement data lakes and lambda architectures, using Azure Data Lake Store, Data Lake Analytics, HDInsight (including Spark), Stream Analytics, SQL Data Warehouse, and Event Hubs
Understand where Azure Machine Learning fits into your analytics pipeline
Gain experience using these services on real-world data that has real-world problems, with scenarios ranging from aviation to Internet of Things (IoT)
Table of Contents
Chapter 1. Enterprise Analytics Fundamentals
Chapter 2. Getting Data into Azure
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