Streaming Data

Description

Streaming Data is an idea-rich tutorial that teaches you to think about efficiently interacting with fast-flowing data. Through relevant examples and illustrated use cases, you’ll explore designs for applications that read, analyze, share, and store streaming data. Along the way, you’ll discover the roles of key technologies like Spark, Storm, Kafka, Flink, RabbitMQ, and more. This book offers the perfect balance between big-picture thinking and implementation details.

Table of Contents

1. PART 1 – A NEW HOLISTIC APPROACH
   1. Introducing streaming data
   2. Getting data from clients: data ingestion
   3. Transporting the data from collection tier: decoupling the data pipeline
   4. Analyzing streaming data
   5. Algorithms for data analysis
   6. Storing the analyzed or collected data
   7. Making the data available
   8. Consumer device capabilities and limitations accessing the data
2. PART 2 – TAKING IT REAL WORLD
   1. Analyzing Meetup RSVPs in real time

Author Biography

Andrew Psaltis is a software engineer and architect focused full time on
building massively scalable real-time analytics systems using Spark, Kafka,
Storm, Hadoop, and WebSockets.