Streaming Architecture

Ted Dunning is Chief Applications Architect at MapR Technologiesand active in the open source community. He currently serves as VP for Incubator at the Apache Foundation, as a champion and mentor for a large number of projects, and ascommitter and PMC member of the Apache ZooKeeper and Drillprojects. He developed the t-digest algorithm used to estimateextreme quantiles. T-digest has been adopted by several open sourceprojects. He also developed the open source log-synth projectdescribed in the book Sharing Big Data Safely (O Reilly).Ted was the chief architect behind the MusicMatch (now YahooMusic) and Veoh recommendation systems, built fraud-detectionsystems for ID Analytics (LifeLock), and has issued 24 patents todate. Ted has a PhD in computing science from University of Sheffield.When he s not doing data science, he plays guitar and mandolin.Ted is on Twitter as @ted_dunning.

Ellen Friedman is a solutions consultant and well-known speaker and author, currently writing mainly about big data topics. She is acommitter for the Apache Drill and Apache Mahout projects. With aPhD in Biochemistry, she has years of experience as a research scientistand has written about a variety of technical topics, includingmolecular biology, nontraditional inheritance, and oceanography.Ellen is also coauthor of a book of magic-themed cartoons, A Rabbit Under the Hat (The Edition House). Ellen is on Twitter as@Ellen_Friedman.

Chapter 1Why Stream?
Planes, Trains, and Automobiles: Connected Vehicles and the IoT
Streaming Data: Life As It Happens
Beyond Real Time: More Benefits of Streaming Architecture
Emerging Best Practices for Streaming Architectures
Healthcare Example with Data Streams
Streaming Data as a Central Aspect of Architectural Design
Chapter 2Stream-based Architecture
A Limited View: Single Real-Time Application
Key Aspects of a Universal Stream-based Architecture
Importance of the Messaging Technology
Choices for Real-Time Analytics
Comparison of Capabilities for Streaming Analytics
Summary
Chapter 3Streaming Architecture: Ideal Platform for Microservices
Why Microservices Matter
What Is Needed to Support Microservices
Microservices in More Detail
Designing a Streaming Architecture: Online Video Service Example
Importance of a Universal Microarchitecture
What’s in a Name?
Why Use Distributed Files and NoSQL Databases?
New Design for the Video Service
Summary: The Converged Platform View
Chapter 4Kafka as Streaming Transport
Motivations for Kafka
Kafka Innovations
Kafka Basic Concepts
The Kafka APIs
Kafka Utility Programs
Kafka Gotchas
Summary
Chapter 5MapR Streams
Innovations in MapR Streams
History and Context of MapR’s Streaming System
How MapR Streams Works
How to Configure MapR Streams
Geo-Distributed Replication
MapR Streams Gotchas
Chapter 6Fraud Detection with Streaming Data
Card Velocity
Fast Response Decision to the Question: “Is It Fraud?”
Multiuse Streaming Data
Scaling Up the Fraud Detector
Summary
Chapter 7Geo-Distributed Data Streams
Stakeholders
Design Goals
Design Choices
Advantages of Streams-based Geo-Replication
Chapter 8Putting It All Together
Benefits of Stream-based Architectures
Making the Transition to Streaming Architecture
Conclusion
Appendix Additional Resources
Streaming Data Topics
Selected O’Reilly Publications by the Authors