What this book covers

Chapter 1, Introducing Stream Processing and Real-Time Insights, describes a paradigm shift that is underway in data processing, from a legacy of handling static data in batches to handling continuously moving data in streams. We explore the fundamental architectural concepts of stream processing as well as its benefits in Real-Time Insights.

Chapter 2, Introducing Azure Stream Analytics and Key Advantages, introduces Microsoft's Azure Stream Analytics, a real-time analytics service built for the stream processing era. We walk through a basic Stream Analytics job configuration and then discuss its key features that drive down the total cost of ownership of streaming solutions. 

Chapter 3, Designing Real-Time Streaming Pipeline, discusses the components of stream processing pipelines and how they differ from traditional batch pipelines, including temporal concepts such as windowing, hot and cold paths of data movement, and others. To see how streaming design concepts can be applied to a technical architecture, we then look at the canonical Azure streaming pipeline from data generation to intelligent action.

 Chapter 4, Developing Real-Time Event Processing with Azure Streaming, covers various tools for provisioning a Stream Analytics job. The integration steps of job input and output are demonstrated.

Chapter 5, Building Using Stream Analytics Query Language, explores the SQL-like query language used in Azure Stream Analytics to run transformations and computations on streaming data. Common and complex stream processing requirements can be met with straightforward queries.

Chapter 6, How to achieve Seamless Scalability with Automation, covers deploying at the enterprise-grade with features and patterns for scaling and deployment automation. After demonstrating automated deployment using Azure Resource Manager (ARM), we explore vertical and horizontal partitioning and scaling in Stream Analytics to increase job capacity and performance.

Chapter 7, Integration of Microsoft Business Intelligence and Big Data, discusses the modern data solution architectures Lambda and Kappa, how to use Stream Analytics to comport with these architectures, and compare it with a popular alternative, HDInsight Storm. We then walk through a sample pipeline, implementing a real-time dashboard based on the Power BI output connector for Stream Analytics.

Chapter 8, Designing and Managing Stream Analytics Jobs, explore solutions to complex challenges of managing streaming jobs, starting with the common need to integrate streams with static data. We then discuss integration with Azure Data Lake Store and Cosmos DB as examples of Azure services whose native integration with Stream Analytics offers unique opportunities to enhance streaming pipelines.

Chapter 9, Optimizing Intelligence in Azure Streaming, discusses building intelligence directly into Stream Analytics jobs so that extensible functions and machine learning calls execute in real time as data moves. We cover integration with the Azure Machine Learning service and implementing user-defined JavaScript functions in Stream Analytics queries. Finally, we walk through using the Azure .NET SDK to enhance job management.

Chapter 10, Understanding Stream Analytics Job Monitoring, looks into ongoing maintenance and job management. We discuss and demonstrate the job metrics, diagram, and logging features offered by Stream Analytics, as well as service health dashboarding and alerting.

Chapter 11, Use Cases of Real-World Data Streaming Architectures, is an end-to-end real-life use case demonstration using the Azure IoT suite with Stream Analytics with implementation steps as PoC for Social Sentiment Analytics, IoT Remote Monitoring telemetry solution, connected factory, and PoC on fraud detection Analytics from the telecom industry.