MARC details
| 000 -LEADER |
|---|
| fixed length control field |
02073cam a2200325 i 4500 |
| 001 - CONTROL NUMBER |
|---|
| control field |
21892389 |
| 003 - CONTROL NUMBER IDENTIFIER |
|---|
| control field |
KE-NaKCAU |
| 005 - DATE AND TIME OF LATEST TRANSACTION |
|---|
| control field |
20240305153020.0 |
| 008 - FIXED-LENGTH DATA ELEMENTS--GENERAL INFORMATION |
|---|
| fixed length control field |
210204t20192019nyua 001 0 eng d |
| 010 ## - LIBRARY OF CONGRESS CONTROL NUMBER |
|---|
| LC control number |
2020288962 |
| 020 ## - INTERNATIONAL STANDARD BOOK NUMBER |
|---|
| International Standard Book Number |
9781617296239 |
| 035 ## - SYSTEM CONTROL NUMBER |
|---|
| System control number |
(OCoLC)on1104044860 |
| 040 ## - CATALOGING SOURCE |
|---|
| Original cataloging agency |
YDX |
| Language of cataloging |
eng |
| Transcribing agency |
KE-NaKCAU |
| Description conventions |
rda |
| Modifying agency |
JRZ |
| -- |
MHD |
| -- |
OCLCF |
| 042 ## - AUTHENTICATION CODE |
|---|
| Authentication code |
lccopycat |
| 050 00 - LIBRARY OF CONGRESS CALL NUMBER |
|---|
| Classification number |
QA76.73.P98 |
| Item number |
W65 2019 |
| 100 1# - MAIN ENTRY--PERSONAL NAME |
|---|
| Personal name |
Wolohan, J. T., |
| Relator term |
author. |
| 245 10 - TITLE STATEMENT |
|---|
| Title |
Mastering large datasets with Python : |
| Remainder of title |
parallelize and distribute your Python code / |
| Statement of responsibility, etc |
J.T. Wolohan. |
| 246 30 - VARYING FORM OF TITLE |
|---|
| Title proper/short title |
Large datasets with Python |
| 260 ## - PUBLICATION, DISTRIBUTION, ETC. (IMPRINT) |
|---|
| Place of publication, distribution, etc |
Shelter Island, New York : |
| Name of publisher, distributor, etc |
Manning, |
| Date of publication, distribution, etc |
2019. |
| 300 ## - PHYSICAL DESCRIPTION |
|---|
| Extent |
xx, 289 pages : |
| Other physical details |
illustrations ; |
| Dimensions |
24 cm |
| 500 ## - GENERAL NOTE |
|---|
| General note |
Includes index. |
| 520 ## - SUMMARY, ETC. |
|---|
| Summary, etc |
Programming techniques that work well on laptop-sized data can slow to a crawl-- or fail altogether-- when applied to massive files or distributed datasets. By mastering the powerful map and reduce paradigm, along with the Python-based tools that support it, you can write data-centric applications that scale efficiently without requiring codebase rewrites as your requirements change. "Mastering large datasets with Python" teaches you to write code that can handle datasets of any size. You'll start with laptop-sized datasets that teach you to parallelize data analysis by breaking large tasks into smaller ones that can run simultaneously. You'll then scale those same programs to industrial-sized datasets on a cluster of cloud servers. With the map and reduce paradigm firly in place, you'll explore tools like Hadoop and PySpark to efficiently process massive distributed datasets, speed up decision-making with machine learning, and simplify your data storage with AWS S3. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Big data. |
| 650 #0 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Big data. |
| Source of heading or term |
fast |
| 650 #7 - SUBJECT ADDED ENTRY--TOPICAL TERM |
|---|
| Topical term or geographic name as entry element |
Python (Computer program language) |
| Source of heading or term |
fast |
| 906 ## - LOCAL DATA ELEMENT F, LDF (RLIN) |
|---|
| a |
7 |
| b |
cbc |
| c |
copycat |
| d |
2 |
| e |
ncip |
| f |
20 |
| g |
y-gencatlg |
| 942 ## - ADDED ENTRY ELEMENTS (KOHA) |
|---|
| Source of classification or shelving scheme |
Library of Congress Classification |
| Koha item type |
Books |