000 | 03197cam a22004097i 4500 | ||
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001 | 21172 | ||
003 | OSt | ||
005 | 20241212105428.0 | ||
008 | 240401t20232023caua 001 0 eng d | ||
020 |
_a1098138791 _q(paperback) |
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020 |
_a9781098138790 _q(paperback) |
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040 |
_aYDX _beng _erda _cYDX _dSFR _dOCLCO _dCDX _dJRZ _dOCLCF _dDLC _dIQ-MoCLU |
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082 | 0 | 4 |
_a004/.33 _223 _bN375 |
100 | 1 |
_aNeedham, Mark _c(Co-author of Graph algorithms), _eauthor. |
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245 | 1 | 0 |
_aBuilding real-time analytics systems : _bfrom events to insights with Apache Kafka and Apache Pinot / _cMark Needham ; foreword by Gunnar Morling. |
250 | _aFirst edition. | ||
264 | 1 |
_aSebastopol, CA : _bO'Reilly Media, Inc., _c2023. |
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264 | 4 | _c©2023 | |
300 |
_axiv, 203 pages : _billustrations ; _c24 cm |
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336 |
_atext _btxt _2rdacontent |
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337 |
_aunmediated _bn _2rdamedia |
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338 |
_avolume _bnc _2rdacarrier |
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500 | _aIncludes index. | ||
505 | 0 | _aIntroduction to real-time analytics -- The real-time analytics ecosystem -- Introducing all about that dough: real-time analytics on pizza -- Querying Kafka with Kafka streams -- The serving layer: Apache Pinot -- Building a real-time analytics dashboard -- Product changes captured with change data capture -- Joining streams with Kafka streams -- Upserts in the serving layer -- Geospatial querying -- Production considerations -- Real-time analytics in the real world -- The future of real-time analytics. | |
520 | _a"Gain deep insight into real-time analytics, including the features of these systems and the problems they solve. With this practical book, data engineers at organizations that use event-processing systems such as Kafka, Google Pub/Sub, and AWS Kinesis will learn how to analyze data streams in real time. The faster you derive insights, the quicker you can spot changes in your business and act accordingly. Author Mark Needham from StarTree provides an overview of the real-time analytics space and an understanding of what goes into building real-time applications. The book's second part offers a series of hands-on tutorials that show you how to combine multiple software products to build real-time analytics applications for an imaginary pizza delivery service. You will: Learn common architectures for real-time analytics Discover how event processing differs from real-time analytics Ingest event data from Apache Kafka into Apache Pinot Combine event streams with OLTP data using Debezium and Kafka Streams Write real-time queries against event data stored in Apache Pinot Build a real-time dashboard and order tracking app Learn how Uber, Stripe, and Just Eat use real-time analytics"-- | ||
630 | 0 | 0 | _aApache Kafka (Electronic resource) |
630 | 0 | 0 | _aApache Pinot (Electronic resource) |
630 | 0 | 0 | _aApache (Computer file : Apache Group) |
630 | 0 | 7 |
_aApache (Computer file : Apache Group) _2fast |
650 | 0 | _aReal-time data processing. | |
650 | 6 | _aTemps réel (Informatique) | |
650 | 7 |
_aReal-time data processing. _2fast |
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700 | 1 |
_aMorling, Gunnar, _ewriter of foreword. |
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910 | _aSAJA | ||
942 |
_2ddc _cBK |
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999 |
_c21172 _d21172 |