Book Image

Learning Apache Apex

By : Thomas Weise, Ananth Gundabattula, Munagala V. Ramanath, David Yan, Kenneth Knowles
Book Image

Learning Apache Apex

By: Thomas Weise, Ananth Gundabattula, Munagala V. Ramanath, David Yan, Kenneth Knowles

Overview of this book

Apache Apex is a next-generation stream processing framework designed to operate on data at large scale, with minimum latency, maximum reliability, and strict correctness guarantees. Half of the book consists of Apex applications, showing you key aspects of data processing pipelines such as connectors for sources and sinks, and common data transformations. The other half of the book is evenly split into explaining the Apex framework, and tuning, testing, and scaling Apex applications. Much of our economic world depends on growing streams of data, such as social media feeds, financial records, data from mobile devices, sensors and machines (the Internet of Things - IoT). The projects in the book show how to process such streams to gain valuable, timely, and actionable insights. Traditional use cases, such as ETL, that currently consume a significant chunk of data engineering resources are also covered. The final chapter shows you future possibilities emerging in the streaming space, and how Apache Apex can contribute to it.

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Table of Contents (17 chapters)
Title Page
Title Page
Credits
Credits
About the Authors
About the Authors
About the Reviewer
About the Reviewer
www.PacktPub.com
www.PacktPub.com
Customer Feedback
Customer Feedback
Preface
Preface
Free Chapter
1
Introduction to Apex
Introduction to Apex
Unbounded data and continuous processing
Use cases and case studies
Application Model and API
Value proposition of Apex
Summary
2
Getting Started with Application Development
Getting Started with Application Development
Development process and methodology
Setting up the development environment
Creating a new Maven project
Application specifications
Custom operator development
Application configuration
Testing in the IDE
Running the application on YARN
Working on the cluster
Summary
3
The Apex Library
The Apex Library
An overview of the library
Integrations
Transformations
Summary
4
Scalability, Low Latency, and Performance
Scalability, Low Latency, and Performance
Partitioning and how it works
Elasticity
Partitioning toolkit
Custom dynamic partitioning
Performance optimizations
Low-latency versus throughput
Sample application for dynamic partitioning
Performance – other aspects for custom operators
Summary
5
Fault Tolerance and Reliability
Fault Tolerance and Reliability
Distributed systems need to be resilient
Fault-tolerance components and mechanism in Apex
Checkpointing
Processing guarantees
Summary
6
Example Project – Real-Time Aggregation and Visualization
Example Project – Real-Time Aggregation and Visualization
Streaming ETL and beyond
The application pattern in a real-world use case
Analyzing Twitter feed
Running the application
The Pub/Sub server
Grafana visualization
Summary
7
Example Project – Real-Time Ride Service Data Processing
Example Project – Real-Time Ride Service Data Processing
The goal
Datasource
The pipeline
Simulation of a real-time feed using historical data
Parsing the data
Looking up of the zip code and preparing for the windowing operation
Windowed operator configuration
Serving the data with WebSocket
Running the application
Running the application on GCP Dataproc
Summary
8
Example Project – ETL Using SQL
Example Project – ETL Using SQL
The application pipeline
Building and running the application
Application configuration
The application code
Partitioning
Application testing
Understanding application logs
Calcite integration
Summary
9
Introduction to Apache Beam
Introduction to Apache Beam
Introduction to Apache Beam
Beam concepts
WordCount in Apache Beam
Running Apache Beam WordCount on Apache Apex
Summary
10
The Future of Stream Processing
The Future of Stream Processing
Lower barrier for building streaming pipelines
Summary

About the Reviewer

Ananth Gundabattula is a senior application architect in the Decisioning and Advanced Analytics architecture team for Commonwealth Bank of Australia. Ananth holds a PhD in Computer Science Security and is interested in all things related to data, including low latency distributed processing systems, machine learning, and data engineering domains. He holds three patents granted by USPTO and has one application pending.

Prior to joining CBA, Ananth was an architect at Threatmetrix and the member of the core team that scaled the Threatmetrix architecture to 100 million transactions per day, which runs at very low latencies using Cassandra, ZooKeeper, and Kafka. He also migrated Threatmetrix data warehouse into the next generation architecture based on Hadoop and Impala. Prior to Threatmetrix, he worked for the IBM software labs and IBM CIO labs, enabling some of the first IBM CIO projects onboarding HBase, Hadoop, and Mahout stack.

Ananth is a committer for Apache Apex and is currently working for the next generation architectures for the CBA fraud platform and Advanced Analytics Omnia platform at CBA.

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