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220922 ||| eng |
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|a 9781617297304
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|a 9781638356493
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|a 1617297305
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4 |
|a TK5105.386
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| 100 |
1 |
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|a Fischer, Joshua
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| 245 |
0 |
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|a Grokking streaming systems
|b real-time event processing
|c Josh Fischer, Ning Wang
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| 260 |
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|a Shelter Island
|b Manning Publications
|c 2022
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| 300 |
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|a 1 volume.
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| 505 |
0 |
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|a Comparing grouping behaviors -- Summary -- Exercises -- 4 Stream graph -- A credit card fraud detection system -- More about the credit card fraud detection system -- The fraud detection business -- Streaming isn't always a straight line -- Zoom into the system -- The fraud detection job in detail -- New concepts -- Upstream and downstream components -- Stream fan-out and fan-in -- Graph, directed graph, and DAG -- DAG in stream processing systems -- All new concepts in one page -- Stream fan-out to the analyzers -- Look inside the engine -- There is a problem: Efficiency
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|a The Streamwork framework overview -- Zooming in on the Streamwork engine -- Core streaming concepts -- More details of the concepts -- The streaming job execution flow -- Your first streaming job -- Executing the job -- Inspecting the job execution -- Look inside the engine -- Keep events moving -- The life of a data element -- Reviewing streaming concepts -- Summary -- Exercises -- 3 Parallelization and data grouping -- The sensor is emitting more events -- Even in streaming, real time is hard -- New concepts: Parallelism is important -- New concepts: Data parallelism
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|a The advantages of multi-stage architecture -- The multi-stage architecture in batch and stream processing systems -- Compare the systems -- A model stream processing system -- Summary -- Exercise -- 2 Hello, streaming systems! -- The chief needs a fancy tollbooth -- It started as HTTP requests, and it failed -- AJ and Miranda take time to reflect -- AJ ponders about streaming systems -- Comparing backend service and streaming -- How a streaming system could fit -- Queues: A foundational concept -- Data transfer via queues -- Our streaming framework (the start of it)
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|a New concepts: Data execution independence -- New concepts: Task parallelism -- Data parallelism vs. task parallelism -- Parallelism and concurrency -- Parallelizing the job -- Parallelizing components -- Parallelizing sources -- Viewing job output -- Parallelizing operators -- Viewing job output -- Events and instances -- Event ordering -- Event grouping -- Shuffle grouping -- Shuffle grouping: Under the hood -- Fields grouping -- Fields grouping: Under the hood -- Event grouping execution -- Look inside the engine: Event dispatcher -- Applying fields grouping in your job -- Event ordering
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|a Intro -- inside front cover -- Grokking Streaming Systems -- Copyright -- brief contents -- contents -- front matter -- preface -- acknowledgments -- about this book -- about the authors -- Part 1. Getting started with streaming -- 1 Welcome to Grokking Streaming Systems -- What is stream processing? -- Streaming system examples -- Streaming systems and real time -- How a streaming system works -- Applications -- Backend services -- Inside a backend service -- Batch processing systems -- Inside a batch processing system -- Stream processing systems -- Inside a stream processing system
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|a Streaming technology (Telecommunications) / fast
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|a Web / bisacsh
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|a Streaming technology (Telecommunications) / http://id.loc.gov/authorities/subjects/sh99000996
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|a En continu (Télécommunications)
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| 653 |
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|a COMPUTERS. / bisacsh
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| 653 |
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|a Network Protocols / bisacsh
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| 653 |
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|a Webcasts as Topic
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| 653 |
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|a Networking / bisacsh
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| 653 |
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|a Web Services & APIs / bisacsh
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| 700 |
1 |
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|a Wang, Ning
|e author
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| 041 |
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7 |
|a eng
|2 ISO 639-2
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| 989 |
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|b OREILLY
|a O'Reilly
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| 776 |
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|z 9781638356493
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| 776 |
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|z 1617297305
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| 776 |
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|z 9781617297304
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| 776 |
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|z 1617297305
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| 776 |
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|z 1638356491
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| 856 |
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|u https://learning.oreilly.com/library/view/~/9781617297304/?ar
|x Verlag
|3 Volltext
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| 082 |
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|a 331
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|a 384
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|a 006.7/876
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| 520 |
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|a Grokking Streaming Systems introduces real-time event streaming applications in clear, reader-friendly language. This engaging book illuminates core concepts like data parallelization, event windows, and backpressure without getting bogged down in framework-specific details. As you go, you'll build your own simple streaming tool from the ground up to make sure all the ideas and techniques stick. The helpful and entertaining illustrations make streaming systems come alive as you tackle relevant examples like real-time credit card fraud detection and monitoring IoT services
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