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Pro Spark Streaming

The Zen of Real-Time Analytics Using Apache Spark

  • Zubair Nabi
Book

Table of contents

  1. Front Matter
    Pages i-xix
  2. Zubair Nabi
    Pages 1-8
  3. Zubair Nabi
    Pages 9-27
  4. Zubair Nabi
    Pages 29-50
  5. Zubair Nabi
    Pages 99-123
  6. Zubair Nabi
    Pages 125-149
  7. Zubair Nabi
    Pages 151-175
  8. Zubair Nabi
    Pages 177-198
  9. Zubair Nabi
    Pages 199-225
  10. Back Matter
    Pages 227-230

About this book

Introduction

Learn the right cutting-edge skills and knowledge to leverage Spark Streaming to implement a wide array of real-time, streaming applications. This book walks you through end-to-end real-time application development using real-world applications, data, and code. Taking an application-first approach, each chapter introduces use cases from a specific industry and uses publicly available datasets from that domain to unravel the intricacies of production-grade design and implementation. The domains covered in Pro Spark Streaming include social media, the sharing economy, finance, online advertising, telecommunication, and IoT.

In the last few years, Spark has become synonymous with big data processing. DStreams enhance the underlying Spark processing engine to support streaming analysis with a novel micro-batch processing model. Pro Spark Streaming by Zubair Nabi will enable you to become a specialist of latency sensitive applications by leveraging the key features of DStreams, micro-batch processing, and functional programming. To this end, the book includes ready-to-deploy examples and actual code. Pro Spark Streaming will act as the bible of Spark Streaming.

What You'll Learn

  • Discover Spark Streaming application development and best practices
  • Work with the low-level details of discretized streams
  • Optimize production-grade deployments of Spark Streaming via configuration recipes and instrumentation using Graphite, collectd, and Nagios
  • Ingest data from disparate sources including MQTT, Flume, Kafka, Twitter, and a custom HTTP receiver
  • Integrate and couple with HBase, Cassandra, and Redis
  • Take advantage of design patterns for side-effects and maintaining state across the Spark Streaming micro-batch model
  • Implement real-time and scalable ETL using data frames, SparkSQL, Hive, and SparkR
  • Use streaming machine learning, predictive analytics, and recommendations
  • Mesh batch processing with stream processing via the Lambda architecture
Who This Book Is For

Data scientists, big data experts, BI analysts, and data architects.

Keywords

Spark Spark Streaming Spark Streaming Application Spark Streaming SQL Spark Streaming R Streaming Machine Learning

Authors and affiliations

  • Zubair Nabi
    • 1
  1. 1.LahorePakistan

Bibliographic information

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