Streaming Analytics with IBM Streams: Analyze More, Act Faster, and Get Continuous Insights

CONTENTS

Acknowledgments

1    Big Data: At Rest and in Motion

Where Does Big Data Come From?

New Technologies

What about Relational Databases?

SQL

Big Data Architecture

Data in Motion

Eliminating Noise and Reducing Volume

Real-Time Processing

Summary

Endnotes

2    Big Data: In-Motion Use Cases

Processing Principles

Data exhaust

Aggregation

Transformation

Correlation

Continuous analysis

Big Data Use Cases

Big Data exploration

Enhanced 360° view of the customer

Security and intelligence

Operational analysis

Data warehouse augmentation

The Internet of Things

The power of habits

Connected cars

Smarter cities

Other possibilities

Use Cases by Industry

Aerospace and defense

Banking

Oil and gas

Electronics

Energy and utility

Government

Healthcare

Insurance

Telecommunication

Summary

Endnotes

3    Program, Framework, or Platform

Build Your Own

Using a Distributed Framework

Using a Streaming Platform

IDE

Toolkits

Programming constructs

Extensibility

Monitoring tools

Summary

4    Streams

Streams Background

Streams Runtime

Streams Deployment

The Streams Processing Language

Punctuations

Windowing

Data of all types

Expression language

Annotations

Writing operators

Streams Toolkits

The Standard toolkit

The Database (db) toolkit

The Geospatial toolkit

The HBase and HDFS toolkits

The inet and Messaging toolkits

The R-project toolkit

The Telecommunication Event Data Analytics toolkit

The Text toolkit

The Timeseries toolkit

Other toolkits

Open Source Toolkits

The Streams Development Environment