| 000 | 05347nam a22005895i 4500 | ||
|---|---|---|---|
| 001 | 978-1-4842-1910-2 | ||
| 003 | DE-He213 | ||
| 005 | 20210118130714.0 | ||
| 007 | cr nn 008mamaa | ||
| 008 | 161229s2017 xxu| s |||| 0|eng d | ||
| 020 |
_a9781484219102 _9978-1-4842-1910-2 |
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| 024 | 7 |
_a10.1007/978-1-4842-1910-2 _2doi |
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| 050 | 4 | _aQA76.9.B45 | |
| 072 | 7 |
_aUN _2bicssc |
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| 072 | 7 |
_aCOM021000 _2bisacsh |
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| 072 | 7 |
_aUN _2thema |
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| 082 | 0 | 4 |
_a005.7 _223 |
| 100 | 1 |
_aKoitzsch, Kerry. _eauthor. _4aut _4http://id.loc.gov/vocabulary/relators/aut |
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| 245 | 1 | 0 |
_aPro Hadoop Data Analytics _h[electronic resource] : _bDesigning and Building Big Data Systems using the Hadoop Ecosystem / _cby Kerry Koitzsch. |
| 250 | _a1st ed. 2017. | ||
| 264 | 1 |
_aBerkeley, CA : _bApress : _bImprint: Apress, _c2017. |
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| 300 |
_aXXI, 298 p. 161 illus., 152 illus. in color. _bonline resource. |
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| 336 |
_atext _btxt _2rdacontent |
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| 337 |
_acomputer _bc _2rdamedia |
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| 338 |
_aonline resource _bcr _2rdacarrier |
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| 347 |
_atext file _bPDF _2rda |
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| 505 | 0 | _aChapter 1: Overview: Building Data Analytic Systems with Hadoop -- Chapter 2: A Scala and Python Refresher -- Chapter 3: Standard Toolkits for Hadoop and Analytics -- Chapter 4: Relational, noSQL, and Graph Databases -- Chapter 5: Data Pipelines and How to Construct Them -- Chapter 6: Advanced Search Techniques with Hadoop, Lucene, and Solr -- Chapter 7: An Overview of Analytical Techniques and Algorithms -- Chapter 8: Rule Engines, System Control, and System Orchestration -- Chapter 9: Putting it All Together: Designing a Complete Analytical System -- Chapter 10: Data Visualizers: Seeing and Interacting with the Analysis -- Chapter 11: A Case Study in Bioinformatics: Analyzing Microscope Slide Data -- Chapter 12: A Bayesian Analysis Software Component: Identifying Credit Card Fraud -- Chapter 13: Searching for Oil: Geological Data Analysis with Mahout -- Chapter 14: ‘Image as Big Data’ Systems: Some Case Studies -- Chapter 15: A Generic Data Pipeline Analytical System -- Chapter 16: Conclusions and The Future of Big Data Analysis. | |
| 520 | _aLearn advanced analytical techniques and leverage existing toolkits to make your analytic applications more powerful, precise, and efficient. This book provides the right combination of architecture, design, and implementation information to create analytical systems which go beyond the basics of classification, clustering, and recommendation. In Pro Hadoop Data Analytics best practices are emphasized to ensure coherent, efficient development. A complete example system will be developed using standard third-party components which will consist of the toolkits, libraries, visualization and reporting code, as well as support glue to provide a working and extensible end-to-end system. The book emphasizes four important topics: The importance of end-to-end, flexible, configurable, high-performance data pipeline systems with analytical components as well as appropriate visualization results. Deep-dive topics will include Spark, H20, Vopal Wabbit (NLP), Stanford NLP, and other appropriate toolkits and plugins. Best practices and structured design principles. This will include strategic topics as well as the how to example portions. The importance of mix-and-match or hybrid systems, using different analytical components in one application to accomplish application goals. The hybrid approach will be prominent in the examples. Use of existing third-party libraries is key to effective development. Deep dive examples of the functionality of some of these toolkits will be showcased as you develop the example system. . | ||
| 650 | 0 | _aBig data. | |
| 650 | 0 | _aComputer programming. | |
| 650 | 0 | _aProgramming languages (Electronic computers). | |
| 650 | 0 | _aData mining. | |
| 650 | 1 | 4 |
_aBig Data. _0http://scigraph.springernature.com/things/product-market-codes/I29120 |
| 650 | 2 | 4 |
_aProgramming Techniques. _0http://scigraph.springernature.com/things/product-market-codes/I14010 |
| 650 | 2 | 4 |
_aProgramming Languages, Compilers, Interpreters. _0http://scigraph.springernature.com/things/product-market-codes/I14037 |
| 650 | 2 | 4 |
_aData Mining and Knowledge Discovery. _0http://scigraph.springernature.com/things/product-market-codes/I18030 |
| 710 | 2 | _aSpringerLink (Online service) | |
| 773 | 0 | _tSpringer eBooks | |
| 776 | 0 | 8 |
_iPrinted edition: _z9781484219096 |
| 776 | 0 | 8 |
_iPrinted edition: _z9781484219119 |
| 776 | 0 | 8 |
_iPrinted edition: _z9781484240564 |
| 856 | 4 | 0 | _uhttps://doi.org/10.1007/978-1-4842-1910-2 |
| 912 | _aZDB-2-CWD | ||
| 999 |
_c447511 _d447511 |
||
| 942 | _cEB | ||
| 506 | _aAvailable to subscribing member institutions only. Доступно лише організаціям членам підписки. | ||
| 506 | _fOnline access from local network of NaUOA. | ||
| 506 | _fOnline access with authorization at https://link.springer.com/ | ||
| 506 | _fОнлайн-доступ з локальної мережі НаУОА. | ||
| 506 | _fОнлайн доступ з авторизацією на https://link.springer.com/ | ||