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Working in a hands-on learning environment, led by our Real-time Data Processing and Analytics expert instructor, students will learn about and explore: Learn about the various challenges in real-time data processing and use the right tools to overcome them. This book covers popular tools and frameworks such as Spark, Flink, and Apache Storm to solve all your distributed processing problems. A practical guide filled with examples, tips, and tricks to help you perform efficient Big Data processing in real-time.

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Last Updated

January 6, 2021

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Course Description

With the rise of Big Data, there is an increasing need to process large amounts of data continuously, with a shorter turnaround time. Real-time data processing involves continuous input, processing and output of data, with the condition that the time required for processing is as short as possible. This book covers the majority of the existing and evolving open source technology stack for real-time processing and analytics. You will get to know about all the real-time solution aspects, from the source to the presentation to persistence. Through this practical book, you’ll be equipped with a clear understanding of how to solve challenges on your own. We’ll cover topics such as how to set up components, basic executions, integrations, advanced use cases, alerts, and monitoring. You’ll be exposed to the popular tools used in real-time processing today such as Apache Spark, Apache Flink, and Storm. Finally, you will put your knowledge to practical use by implementing all of the techniques in the form of a practical, real-world use case. By the end of this book, you will have a solid understanding of all the aspects of real-time data processing and analytics, and will know how to deploy the solutions in production environments in the best possible manner.

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Course Curriculum

    • Introducing Real-Time Analytics 00:00:00
    • What is big data? 00:00:00
    • Big data infrastructure 00:00:00
    • Real–time analytics – the myth and the reality 00:00:00
    • Near real–time solution – an architecture that works 00:00:00
    • Lambda architecture – analytics possibilities 00:00:00
    • IOT – thoughts and possibilities 00:00:00
    • Cloud – considerations for NRT and IOT 00:00:00
    • Real Time Applications – The Basic Ingredients 00:00:00
    • The NRT system and its building blocks 00:00:00
    • NRT – high-level system view 00:00:00
    • NRT – technology view 00:00:00
    • Understanding and Tailing Data Streams 00:00:00
    • Understanding data streams 00:00:00
    • Setting up infrastructure for data ingestion 00:00:00
    • Taping data from source to the processor – expectations and caveats 00:00:00
    • Comparing and choosing what works best for your use case 00:00:00
    • Do it yourself 00:00:00
    • Setting up the Infrastructure for Storm 00:00:00
    • Overview of Storm 00:00:00
    • Storm architecture and its components 00:00:00
    • Setting up and configuring Storm 00:00:00
    • Real-time processing job on Storm 00:00:00
    • Configuring Apache Spark and Flink 00:00:00
    • Setting up and a quick execution of Spark 00:00:00
    • Setting up and a quick execution of Flink 00:00:00
    • Setting up and a quick execution of Apache Beam 00:00:00
    • Balancing in Apache Beam 00:00:00
    • Integrating Storm with a Data Source 00:00:00
    • RabbitMQ – messaging that works 00:00:00
    • RabbitMQ exchanges 00:00:00
    • RabbitMQ – integration with Storm 00:00:00
    • PubNub data stream publisher 00:00:00
    • String together Storm-RMQ-PubNub sensor data topology 00:00:00
    • From Storm to Sink 00:00:00
    • Setting up and configuring Cassandra 00:00:00
    • Storm and Cassandra topology 00:00:00
    • Storm and IMDB integration for dimensional data 00:00:00
    • Integrating the presentation layer with Storm 00:00:00
    • Do It Yourself 00:00:00
    • Storm Trident 00:00:00
    • State retention and the need for Trident 00:00:00
    • Basic Storm Trident topology 00:00:00
    • Trident internals 00:00:00
    • Trident operations 00:00:00
    • DRPC 00:00:00
    • Do It Yourself 00:00:00
    • Working with Spark 00:00:00
    • Spark overview 00:00:00
    • Distinct advantages of Spark 00:00:00
    • Spark – use cases 00:00:00
    • Spark architecture – working inside the engine 00:00:00
    • Spark pragmatic concepts 00:00:00
    • Spark 2.x – advent of data frames and datasets 00:00:00
    • Working with Spark Operations 00:00:00
    • Spark – packaging and API 00:00:00
    • RDD pragmatic exploration 00:00:00
    • Shared variables – broadcast variables and accumulators 00:00:00
    • Spark Streaming 00:00:00
    • Spark Streaming concepts 00:00:00
    • Spark Streaming – introduction and architecture 00:00:00
    • Packaging structure of Spark Streaming 00:00:00
    • Connecting Kafka to Spark Streaming 00:00:00
    • Working with Apache Flink 00:00:00
    • Flink architecture and execution engine 00:00:00
    • Flink basic components and processes 00:00:00
    • Integration of source stream to Flink 00:00:00
    • Flink processing and computation 00:00:00
    • Flink persistence 00:00:00
    • FlinkCEP 00:00:00
    • Pattern API 00:00:00
    • Gelly 00:00:00
    • DIY 00:00:00
    • Case Study 00:00:00
    • Introduction 00:00:00
    • Data modeling 00:00:00
    • Tools and frameworks 00:00:00
    • Setting up the infrastructure 00:00:00
    • Implementing the case study 00:00:00
    • Running the case study 00:00:00

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