Modern large-scale systems need to be able to respond nimbly to multiple streams of data. Structuring your digital business around a centralized “event firehose” that collects, stores, and processes continuous event streams is the best way to reach that goal. This approach—the Unified Log Paradigm (ULP)—along with tools like Apache Kafka and Amazon Kinesis will help get you there. And this book will get you started!
About the book
Exploring Streaming Data Analysis is a timely primer that gives you a taste of performing analytics on event streams using a Lambda function on AWS (Amazon Web Services) and deploying and testing an AWS Lambda function. You’ll learn the algorithmic side of stream processing, focusing on the what and why of streaming analysis algorithms. You’ll cover common constraints, approaches for thinking about time, and techniques for summarization. Finally, you’ll take a look at how the Kafka Streams framework uses local state to extract the maximum amount of information from event streams. This mini ebook provides the well-rounded introduction you need to get up to speed in the basics of streaming data analysis!
4.3 Using state stores for lookups and previously seen data
4.4 Joining streams for added insight
4.5 Timestamps in Kafka Streams
“Analytics-on-write” – Chapter 11 from Event Streams in Action by Alexander Dean and Valentin Crettaz
“Algorithms for data analysis” – Chapter 5 from Streaming Data by Andrew G. Psaltis
“Streams and state” – Chapter 4 from Kafka Streams in Action by William P. Bejeck Jr.
Alexander Dean is an experienced technologist with a passion for functional programming, cloud-based architectures, and big data technologies. He is the co-founder of Snowplow Analytics, an open source event processing and analytics platform.
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