{"loaderData":{"bookShellContainer":{"isRouterLoaderQuery":true,"data":{"cardMetadata":{"identifier":"9781617292286","ourn":"urn:orm:book:9781617292286","popularity":0.0001543,"name":"Streaming Data","type":"book","description":{"text/html":"

Streaming Data introduces the concepts and requirements of streaming and real-time data systems. The book is an idea-rich tutorial that teaches you to think about how to efficiently interact with fast-flowing data.

About the Technology

As humans, we're constantly filtering and deciphering the information streaming toward us. In the same way, streaming data applications can accomplish amazing tasks like reading live location data to recommend nearby services, tracking faults with machinery in real time, and sending digital receipts before your customers leave the shop. Recent advances in streaming data technology and techniques make it possible for any developer to build these applications if they have the right mindset. This book will let you join them.

About the Book

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.

What's Inside

The right way to collect real-time data
Architecting a streaming pipeline
Analyzing the data
Which technologies to use and when

About the Reader

Written for developers familiar with relational database concepts. No experience with streaming or real-time applications required.

About the Author

Andrew Psaltis is a software engineer focused on massively scalable real-time analytics.

About the Technology

About the Book

What's Inside

The right way to collect real-time data
Architecting a streaming pipeline
Analyzing the data
Which technologies to use and when

About the Reader

Written for developers familiar with relational database concepts. No experience with streaming or real-time applications required.

About the Author

Andrew Psaltis is a software engineer focused on massively scalable real-time analytics.

Quotes
The definitive book if you want to master the architecture of an enterprise-grade streaming application.
- Sergio Fernandez Gonzalez, Accenture

A thorough explanation and examination of the different systems, strategies, and tools for streaming data implementations.
- Kosmas Chatzimichalis, Mach 7x

A well-structured way to learn about streaming data and how to put it into practice in modern real-time systems.
- Giuliano Araujo Bertoti, FATEC

This book is all you need to understand what streaming is all about!
- Carlos Curotto, Globant

\n","text/plain":"Streaming Data introduces the concepts and requirements of streaming and real-time data systems. The book is an idea-rich tutorial that teaches you to think about how to efficiently interact with fast-flowing data. About the Technology As humans, we're constantly filtering and deciphering the information streaming toward us. In the same way, streaming data applications can accomplish amazing tasks like reading live location data to recommend nearby services, tracking faults with machinery in real time, and sending digital receipts before your customers leave the shop. Recent advances in streaming data technology and techniques make it possible for any developer to build these applications if they have the right mindset. This book will let you join them. About the Book 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. What's Inside The right way to collect real-time data Architecting a streaming pipeline Analyzing the data Which technologies to use and when About the Reader Written for developers familiar with relational database concepts. No experience with streaming or real-time applications required. About the Author Andrew Psaltis is a software engineer focused on massively scalable real-time analytics. Quotes The definitive book if you want to master the architecture of an enterprise-grade streaming application. - Sergio Fernandez Gonzalez, Accenture A thorough explanation and examination of the different systems, strategies, and tools for streaming data implementations. - Kosmas Chatzimichalis, Mach 7x A well-structured way to learn about streaming data and how to put it into practice in modern real-time systems. - Giuliano Araujo Bertoti, FATEC This book is all you need to understand what streaming is all about! - Carlos Curotto, Globant"},"spine":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epubs/urn:orm:book:9781617292286/spine/","files":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epubs/urn:orm:book:9781617292286/files/","table_of_contents":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epubs/urn:orm:book:9781617292286/table-of-contents/","chapters":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/?epub_identifier=urn:orm:book:9781617292286","tags":[],"language":"en","resources":[{"url":"https://livebook.manning.com/book/streaming-data","type":"Unspecified, see website description","description":"Publisher Support"},{"url":"https://www.manning.com","type":"Publisher’s website for a specified work","description":"Publisher Website"},{"url":"https://www.manning.com/downloads/1571","type":"Unspecified, see website description","description":"Supplemental Content"}],"roughcut":false},"tableOfContents":[{"depth":1,"reference_id":"9781617292286-/kindle_split_001.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_001.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_001.html/","fragment":"","title":"Copyright","duration":138,"children":[]},{"depth":1,"reference_id":"9781617292286-/kindle_split_002.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_002.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_002.html/","fragment":"","title":"Brief Table of Contents","duration":69,"children":[]},{"depth":1,"reference_id":"9781617292286-/kindle_split_003.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_003.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_003.html/","fragment":"","title":"Table of Contents","duration":345,"children":[]},{"depth":1,"reference_id":"9781617292286-/kindle_split_004.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_004.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_004.html/","fragment":"","title":"Preface","duration":207,"children":[]},{"depth":1,"reference_id":"9781617292286-/kindle_split_005.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_005.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_005.html/","fragment":"","title":"Acknowledgments","duration":138,"children":[]},{"depth":1,"reference_id":"9781617292286-/kindle_split_006.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_006.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_006.html/","fragment":"","title":"About this Book","duration":483,"children":[]},{"depth":1,"reference_id":"9781617292286-/kindle_split_007.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_007.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_007.html/","fragment":"","title":"Part 1. A new holistic approach","duration":138,"children":[{"depth":2,"reference_id":"9781617292286-/kindle_split_008.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_008.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_008.html/","fragment":"","title":"Chapter 1. Introducing streaming data","duration":1311,"children":[]},{"depth":2,"reference_id":"9781617292286-/kindle_split_009.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_009.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_009.html/","fragment":"","title":"Chapter 2. Getting data from clients: data ingestion","duration":2967,"children":[]},{"depth":2,"reference_id":"9781617292286-/kindle_split_010.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_010.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_010.html/","fragment":"","title":"Chapter 3. Transporting the data from collection tier: decoupling the data pipeline","duration":2415,"children":[]},{"depth":2,"reference_id":"9781617292286-/kindle_split_011.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_011.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_011.html/","fragment":"","title":"Chapter 4. Analyzing streaming data","duration":2484,"children":[]},{"depth":2,"reference_id":"9781617292286-/kindle_split_012.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_012.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_012.html/","fragment":"","title":"Chapter 5. Algorithms for data analysis","duration":2553,"children":[]},{"depth":2,"reference_id":"9781617292286-/kindle_split_013.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_013.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_013.html/","fragment":"","title":"Chapter 6. Storing the analyzed or collected data","duration":2346,"children":[]},{"depth":2,"reference_id":"9781617292286-/kindle_split_014.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_014.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_014.html/","fragment":"","title":"Chapter 7. Making the data available","duration":2898,"children":[]},{"depth":2,"reference_id":"9781617292286-/kindle_split_015.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_015.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_015.html/","fragment":"","title":"Chapter 8. Consumer device capabilities and limitations accessing the data","duration":2622,"children":[]}]},{"depth":1,"reference_id":"9781617292286-/kindle_split_016.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_016.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_016.html/","fragment":"","title":"Part 2. Taking it real world","duration":69,"children":[{"depth":2,"reference_id":"9781617292286-/kindle_split_017.html","ourn":"urn:orm:book:9781617292286:chapter:kindle_split_017.html","url":"https://internal-ingress.platform.gcp.oreilly.com/api/v2/epub-chapters/urn:orm:book:9781617292286:chapter:kindle_split_017.html/","fragment":"","title":"Chapter 9. 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Data meshes can help your organization decentralize data, giving ownership back to the engineers who produced it. This book provides a concise yet comprehensive overview of data mesh patterns for streaming and real-time data services. Authors Hubert Dulay and Stephen Mooney examine the vast differences between streaming and batch data meshes. Data engineers, architects, data product owners, and those in DevOps and MLOps roles will learn steps for implementing a streaming data mesh, from defining a data domain to building a good data product. Through the course of the book, you'll create a complete self-service data platform and devise a data governance system that enables your mesh to work seamlessly. With this book, you will: Design a streaming data mesh using Kafka Learn how to identify a domain Build your first data product using self-service tools Apply data governance to the data products you create Learn the differences between synchronous and asynchronous data services Implement self-services that support decentralized data","identifier":"9781098130718","issued":"2023-05-11","ourn":"urn:orm:book:9781098130718","model":"recommendations_usage_based","score":0.841268659,"title":"Streaming Data Mesh","uiFormat":"book","uri":"/library/view/-/9781098130718/"},{"apiUrl":"/api/v1/book/9781492031840/","authors":["Anthony Aragues"],"contentFormat":"book","continueUri":"/api/v1/continue/9781492031840/","coverUri":"/library/cover/9781492031840/","description":"While tools for analyzing streaming and real-time data are gaining adoption, the ability to visualize these data types has yet to catch up. Dashboards are good at conveying daily or weekly data trends at a glance, though capturing snapshots when data is transforming from moment to moment is more difficult—but not impossible. With this practical guide, application designers, data scientists, and system administrators will explore ways to create visualizations that bring context and a sense of time to streaming text data. Author Anthony Aragues guides you through the concepts and tools you need to build visualizations for analyzing data as it arrives. Determine your company’s goals for visualizing streaming data Identify key data sources and learn how to stream them Learn practical methods for processing streaming data Build a client application for interacting with events, logs, and records Explore common components for visualizing streaming data Consider analysis concepts for developing your visualization Define the dashboard’s layout, flow direction, and component movement Improve visualization quality and productivity through collaboration Explore use cases including security, IoT devices, and application data","identifier":"9781492031840","issued":"2018-06-01","ourn":"urn:orm:book:9781492031840","model":"recommendations_usage_based","score":0.662877083,"title":"Visualizing Streaming Data","uiFormat":"book","uri":"/library/view/-/9781492031840/"},{"apiUrl":"/api/v1/book/9781492046820/","authors":["Dean Wampler"],"contentFormat":"book","continueUri":"/api/v1/continue/9781492046820/","coverUri":"/library/cover/9781492046820/","description":"Why have stream-oriented data systems become so popular, when batch-oriented systems have served big data needs for many years? In the updated edition of this report, Dean Wampler examines the rise of streaming systems for handling time-sensitive problems—such as detecting fraudulent financial activity as it happens. You’ll explore the characteristics of fast data architectures, along with several open source tools for implementing them. Batch processing isn’t going away, but exclusive use of these systems is now a competitive disadvantage. You’ll learn that, while fast data architectures using tools such as Kafka, Akka, Spark, and Flink are much harder to build, they represent the state of the art for dealing with mountains of data that require immediate attention. Learn how a basic fast data architecture works, step-by-step Examine how Kafka’s data backplane combines the best abstractions of log-oriented and message queue systems for integrating components Evaluate four streaming engines, including Kafka Streams, Akka Streams, Spark, and Flink Learn which streaming engines work best for different use cases Get recommendations for making real-world streaming systems responsive, resilient, elastic, and message driven Explore an example IoT streaming application that includes telemetry ingestion and anomaly detection","identifier":"9781492046820","issued":"2018-12-25","ourn":"urn:orm:book:9781492046820","model":"recommendations_usage_based","score":0.486629814,"title":"Fast Data Architectures for Streaming Applications, 2nd Edition","uiFormat":"book","uri":"/library/view/-/9781492046820/"},{"apiUrl":"/api/v1/book/9781617292347/","authors":["Valentin Crettaz","Alexander Dean"],"contentFormat":"book","continueUri":"/api/v1/continue/9781617292347/","coverUri":"/library/cover/9781617292347/","description":"Event Streams in Action is a foundational book introducing the ULP paradigm and presenting techniques to use it effectively in data-rich environments. About the Technology Many high-profile applications, like LinkedIn and Netflix, deliver nimble, responsive performance by reacting to user and system events as they occur. In large-scale systems, this requires efficiently monitoring, managing, and reacting to multiple event streams. Tools like Kafka, along with innovative patterns like unified log processing, help create a coherent data processing architecture for event-based applications. About the Book Event Streams in Action teaches you techniques for aggregating, storing, and processing event streams using the unified log processing pattern. In this hands-on guide, you’ll discover important application designs like the lambda architecture, stream aggregation, and event reprocessing. You’ll also explore scaling, resiliency, advanced stream patterns, and much more! By the time you’re finished, you’ll be designing large-scale data-driven applications that are easier to build, deploy, and maintain. What's Inside Validating and monitoring event streams Event analytics Methods for event modeling Examples using Apache Kafka and Amazon Kinesis About the Reader For readers with experience coding in Java, Scala, or Python. About the Authors Alexander Dean developed Snowplow, an open source event processing and analytics platform. Valentin Crettaz is an independent IT consultant with 25 years of experience. Quotes Clear, precise, detailed, and well written. A must read. - Thorsten P. Weber, Mercateo A brilliant, progressive introduction to event streaming and related technologies. - Cosimo Attanasi, ER Sistemi A fantastic book covering the why, what, and how of data streams. Excellent tips and practical advice from the trenches. - Miguel Eduardo Gil Biraud, Minddistrict With practical code examples that translate well across environments, languages, and disciplines, this is your springboard into real-world event streaming. - Bartosz \"Azatar\" Solowiej, pSilent Partners","identifier":"9781617292347","issued":"2019-05-23","ourn":"urn:orm:book:9781617292347","model":"recommendations_usage_based","score":0.427807,"title":"Event Streams in Action","uiFormat":"book","uri":"/library/view/-/9781617292347/"}],"titleOurn":"urn:orm:book:9781617292286","productServiceData":{"data":[{"approvedToAnnounceBy":"cindy","brands":[],"businessUnit":{"businessEntity":{"code":"MNG","id":15,"name":"Manning Publications","type":{"code":"Publisher","id":1}},"code":"DIS","id":21,"name":"Distribution","status":{"id":1,"name":"Active"}},"categories":[],"contributors":[{"firstName":"Marjan","id":2115,"lastName":"Bace","role":{"id":3,"name":"Executive Editor"}},{"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.","firstName":"Andrew","id":36369,"lastName":"Psaltis","role":{"id":1,"name":"Author"},"viewOrder":1}],"created":"2016-06-13T07:00:00.000Z","discountable":true,"edition":1,"editionLabel":"1st Edition","enhancedQuiz":false,"enhancedSandbox":false,"estimatedReleaseDate":"2017-06-15","groupIdentifier":"9781617292286","id":166693,"imprint":{"code":"MNG","id":15,"name":"Manning Publications","publisher":{"code":"MNG","id":15,"name":"Manning Publications","type":{"code":"Publisher","id":1}}},"isbn13":"9781617292286","lastUpdated":"2019-12-19T17:10:11.000Z","lastUpdatedBy":"ITOPSDEV-199","legacyVideo":{"productId":null,"videoToc":null,"videoPreview":null,"videoPubProfile":null,"lastUpdatedBy":"manning","videoDuration":null,"earlyReleaseDuration":null},"longDescription":"

Summary

Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications.

About the Technology

About the Book

What's Inside

The right way to collect real-time data
Architecting a streaming pipeline
Analyzing the data
Which technologies to use and when

About the Reader

Written for developers familiar with relational database concepts. No experience with streaming or real-time applications required.

About the Author

Andrew Psaltis is a software engineer focused on massively scalable real-time analytics.

Table of Contents

PART 1 - A NEW HOLISTIC APPROACH

Introducing streaming data
Getting data from clients: data ingestion
Transporting the data from collection tier: decoupling the data pipeline
Analyzing streaming data
Algorithms for data analysis
Storing the analyzed or collected data
Making the data available
Consumer device capabilities and limitations accessing the data

PART 2 - TAKING IT REAL WORLD

Analyzing Meetup RSVPs in real time

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Streaming Data introduces the concepts and requirements of streaming and real-time data systems. Readers will develop a foundation to understand the challenges and solutions of building in-the-moment data systems before committing to specific technologies. Using lots of diagrams, this book systematically builds up the blueprint for an in-the-moment system concept by concept. This book focuses on the big ideas of streaming and real time data systems rather than the implementation details.

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