| 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 |
230220b xxu||||| |||| 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 |
006.31 |
| Item number |
CHA |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
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| Personal name |
Chambers, Bill |
| 245 ## - TITLE STATEMENT |
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| Title |
Spark : the definintive guide : big data processing made simple |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
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| Name of publisher, distributor, etc |
Shroff Publishers, |
| Date of publication, distribution, etc |
2018 |
| Place of publication, distribution, etc |
Mumbai : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xxvi, 574 p.; |
| Other physical details |
ill. |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
1800.00 |
| Price type code |
INR |
| Unit of pricing |
1.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
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| Bibliography, etc |
Includes index. |
| 520 ## - SUMMARY, ETC. |
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| Summary, etc |
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. 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. Get a gentle overview of big data and Spark Learn about DataFrames, SQL, and Datasets—Spark’s core APIs—through worked examples Dive into Spark’s low-level APIs, RDDs, and execution of SQL and DataFrames Understand how Spark runs on a cluster Debug, monitor, and tune Spark clusters and applications Learn the power of Structured Streaming, Spark’s stream-processing engine 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 |
Big data |
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Computer Science |
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Data Processing |
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Hardware General |
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Information Technology |
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Data mining |
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Information retrieval |
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Spark |
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Advanced analytics |
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Aggregations |
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Apache Hive |
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Cluster manager |
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Configuration options |
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Dataframe |
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Datasets |
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| Topical term or geographic name as entry element |
Decision trees |
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Graphframes |
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Hadoop distributed file system |
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Hyperparameters |
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JSON data |
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Linear regression |
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Logistic regression |
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| Topical term or geographic name as entry element |
Machine learning |
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MLib |
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NullIf fiunction |
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| Topical term or geographic name as entry element |
Python |
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Random forests |
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RDD method |
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| Topical term or geographic name as entry element |
Stream processing |
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Timestamp type class |
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| Topical term or geographic name as entry element |
Unsupervised learning |
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| Topical term or geographic name as entry element |
Watermarks |
| 700 ## - ADDED ENTRY--PERSONAL NAME |
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| Personal name |
Zaharia, Matei |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
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| Source of classification or shelving scheme |
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| Item type |
Books |