MARC details
| 000 -LEADER |
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| fixed length control field |
02046nam a22002537a 4500 |
| 952 ## - LOCATION AND ITEM INFORMATION (KOHA) |
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| Withdrawn status |
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| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
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| fixed length control field |
200608b2018 ||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
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| International Standard Book Number |
9789352137060 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
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| Classification number |
006.312 CHA-B |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Chambers, Bill |
| 245 ## - TITLE STATEMENT |
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| Title |
Spark : the definitive guide : |
| Remainder of title |
big data processing made simple / |
| Statement of responsibility, etc. |
Bill Chambers and Matei Zaharia |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc. |
India |
| Name of publisher, distributor, etc. |
Shroff Publishers |
| Date of publication, distribution, etc. |
2018 |
| 300 ## - PHYSICAL DESCRIPTION |
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| Extent |
574 p. |
| 365 ## - TRADE PRICE |
|---|
| Price type code |
INR |
| Price amount |
1600.00. |
| 500 ## - GENERAL NOTE |
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| General note |
Learn how to use, deploy and maintain Apache Spark with this comprehensive guide, written by the creators of the open-source cluster-computing framework. with an emphasis on improvements and new features in Spark 2.0, authors Bill Chambers and Matei Zaharia break down Spark topics into distinct sections, each with unique goals.<br/><br/>You'll explore the basic operations and common functions of Spark's structured APIs, as well as Structured Streaming, a new high-level API for building end-to-end streaming applications. Developers and system administrators will learn the fundamentals of monitoring, tuning and debugging Spark and explore machine learning techniques and scenarios for employing MLlib, Spark's scalable machine-learning library.<br/><br/><br/>Get a gentle overview of big data and Spark<br/><br/>Learn about DataFrames, SQL and Datasets-Spark's core APIs-through worked examples<br/><br/>Dive into Spark's low-level APIs, RDDs and execution of SQL and DataFrames<br/><br/>Understand how Spark runs on a cluster<br/><br/>Debug, monitor and tune Spark clusters and applications<br/><br/>Learn the power of Structured Streaming, Spark';s stream-processing engine<br/><br/>Learn how you can apply MLlib to a variety of problems, including classification or recommendation |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Spark (Electronic resource : Apache Software Foundation) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Big data |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Information retrieval |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Data mining |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Telecommunication |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Web servers--Computer programs |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Apache (Computer file : Apache Group) |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
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| Topical term or geographic name as entry element |
Electronic data processing |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Zaharia, Matei |
