| 000 -LEADER |
|---|
| fixed length control field |
a |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
230220b xxu||||| |||| 00| 0 eng d |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9789355420787 |
| 082 ## - DEWEY DECIMAL CLASSIFICATION NUMBER |
|---|
| Classification number |
005.1 |
| Item number |
PAR |
| 100 ## - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Parsian, Mahmoud |
| 245 ## - TITLE STATEMENT |
|---|
| Title |
Data algorithms with Spark : recipes and design patterns for scaling up using PySpark |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Name of publisher, distributor, etc |
Shroff Publishers, |
| Date of publication, distribution, etc |
2022 |
| Place of publication, distribution, etc |
Mumbai : |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xx, 412 p.; |
| Other physical details |
ill. |
| Dimensions |
24 cm |
| 365 ## - TRADE PRICE |
|---|
| Price amount |
1600.00 |
| Price type code |
INR |
| Unit of pricing |
1.00 |
| 504 ## - BIBLIOGRAPHY, ETC. NOTE |
|---|
| Bibliography, etc |
Includes bibliographical references and index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Apache Spark's speed, ease of use, sophisticated analytics, and multilanguage support makes practical knowledge of this cluster-computing framework a required skill for data engineers and data scientists. With this hands-on guide, anyone looking for an introduction to Spark will learn practical algorithms and examples using PySpark. In each chapter, author Mahmoud Parsian shows you how to solve a data problem with a set of Spark transformations and algorithms. You'll learn how to tackle problems involving ETL, design patterns, machine learning algorithms, data partitioning, and genomics analysis. Each detailed recipe includes PySpark algorithms using the PySpark driver and shell script. With this book, you will: Learn how to select Spark transformations for optimized solutions Explore powerful transformations and reductions including reduceByKey(), combineByKey(), and mapPartitions() Understand data partitioning for optimized queries Build and apply a model using PySpark design patterns Apply motif-finding algorithms to graph data Analyze graph data by using the GraphFrames API Apply PySpark algorithms to clinical and genomics data Learn how to use and apply feature engineering in ML algorithms Understand and use practical and pragmatic data design patterns. |
| 650 ## - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Computer programming |
|
|---|
| Topical term or geographic name as entry element |
Data Mining |
|
|---|
| Topical term or geographic name as entry element |
Programmation |
|
|---|
| Topical term or geographic name as entry element |
Spark |
|
|---|
| Topical term or geographic name as entry element |
Open source tools |
|
|---|
| Topical term or geographic name as entry element |
Associative law |
|
|---|
| Topical term or geographic name as entry element |
Binning |
|
|---|
| Topical term or geographic name as entry element |
FlatMap |
|
|---|
| Topical term or geographic name as entry element |
Input-Filter-Output design pattern |
|
|---|
| Topical term or geographic name as entry element |
Join design patterns |
|
|---|
| Topical term or geographic name as entry element |
Monoids |
|
|---|
| Topical term or geographic name as entry element |
Motif finding feature |
|
|---|
| Topical term or geographic name as entry element |
One-hot encoding |
|
|---|
| Topical term or geographic name as entry element |
PageRank algorithm |
|
|---|
| Topical term or geographic name as entry element |
Pipelines |
|
|---|
| Topical term or geographic name as entry element |
Transformation algorithms |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
|
| Item type |
Books |