MARC details
| 000 -LEADER |
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| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
221014b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9780134846019 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.312 |
| Item number |
AVE |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Aven, Jeffrey |
| 245 ## - TITLE STATEMENT |
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| Title |
Data analytics with spark using python |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Addison-Wesley, |
| Date of publication, distribution, etc |
2018 |
| Place of publication, distribution, etc |
Boston : |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
x, 306 p. ; |
| Other physical details |
ill. |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
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| Price amount |
475.00 |
| Price type code |
INR |
| Unit of pricing |
01 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
Spark for Data Professionals introduces and solidifies the concepts behind Spark 2.x, teaching working developers, architects, and data professionals exactly how to build practical Spark solutions. Jeffrey Aven covers all aspects of Spark development, including basic programming to SparkSQL, SparkR, Spark Streaming, Messaging, NoSQL and Hadoop integration. Each chapter presents practical exercises deploying Spark to your local or cloud environment, plus programming exercises for building real applications. Unlike other Spark guides, Spark for Data Professionals explains crucial concepts step-by-step, assuming no extensive background as an open source developer. It provides a complete foundation for quickly progressing to more advanced data science and machine learning topics. This guide will help you: Understand Spark basics that will make you a better programmer and cluster “citizen” Master Spark programming techniques that maximize your productivity Choose the right approach for each problem Make the most of built-in platform constructs, including broadcast variables, accumulators, effective partitioning, caching, and checkpointing Leverage powerful tools for managing streaming, structured, semi-structured, and unstructured data. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Python |
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Anonymous function |
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Apache |
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Big data |
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Broadcast method |
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Checkpointing |
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| Topical term or geographic name as entry element |
Create Direct Stream method |
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Data frames |
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Dsteams |
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Environment variables |
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FlatMap |
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Hadoop |
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| Topical term or geographic name as entry element |
Higher-order function |
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JSON |
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| Topical term or geographic name as entry element |
JDBC |
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Lamda syntax |
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MapReduce |
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NOSQL systems |
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PySpark |
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Spark cluster architecture |
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Tuple function |
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Unpersist method |
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| Topical term or geographic name as entry element |
YARN |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
Dewey Decimal Classification |
| Item type |
Books |
