Hands-On Data Warehousing with Azure Data Factory: ETL techniques to load and transform data from various sources, both on-premises and on cloud | Processing Big Data with Azure HDInsight: Building Real-World Big Data Systems on Azure HDInsight Using the Hadoop Ecosystem | Azure for Architects: Implementing cloud design, DevOps, IoT, and serverless solutions on your public cloud | IoT Solutions in Microsoft's Azure IoT Suite: Data Acquisition and Analysis in the Real World | Predictive Analytics With Microsoft Azure Machine Learning | Designing Data-intensive Applications | Hadoop
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.
Youâll 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)
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