John T. Wolohan Mastering Large Datasets (Paperback)

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Specificaties

Objectstaat
Nieuw: Een nieuw, ongelezen en ongebruikt boek in perfecte staat waarin geen bladzijden ontbreken of ...
Book Title
Mastering Large Datasets
Title
Mastering Large Datasets
ISBN-10
1617296236
Subtitle
Parallelize and Distribute Your Python Code
EAN
9781617296239
Release Date
03/30/2020
Release Year
2020
ISBN
9781617296239
Genre
Computing & Internet
Country/Region of Manufacture
US

Over dit product

Product Identifiers

Publisher
Manning Publications Co. LLC
ISBN-10
1617296236
ISBN-13
9781617296239
eBay Product ID (ePID)
11038386929

Product Key Features

Number of Pages
312 Pages
Language
English
Publication Name
Mastering Large Datasets with Python : Parallelize and Distribute Your Python Code
Subject
General, Data Processing, Databases / General, Programming Languages / Python
Publication Year
2020
Type
Textbook
Author
John T. Wolohan
Subject Area
Mathematics, Computers
Format
Trade Paperback

Dimensions

Item Height
0.7 in
Item Weight
19.9 Oz
Item Length
9.2 in
Item Width
7.4 in

Additional Product Features

Intended Audience
Scholarly & Professional
LCCN
2020-288962
Dewey Edition
23
Illustrated
Yes
Dewey Decimal
006.312
Synopsis
With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You'll get familiar with Python's functional built-ins like the functools operator and itertools modules, as well as the toolz library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you'll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level! Key features * An introduction to functional and parallel programming * Data science workflow * Profiling code for better performance * Fulfilling different quality objectives for a single unifying task * Python multiprocessing * Practical exercises including full-scale distributed applications Audience Readers should have intermediate Python programming skills. About the technology Python is a data scientist's dream-come-true, thanks to readily available libraries that support tasks like data analysis, machine learning, visualization, and numerical computing. J.T. Wolohan is a lead data scientist at Booz Allen Hamilton and a PhD researcher at Indiana University, Bloomington, affiliated with the Department of Information and Library Science and the School of Informatics and Computing. His professional work focuses on rapid prototyping and scalable AI. His research focuses on computational analysis of social uses of language online., With an emphasis on clarity, style, and performance, author J.T. Wolohan expertly guides you through implementing a functionally-influenced approach to Python coding. You'll get familiar with Python's functional built-ins like the functools operator and itertools modules, as well as the toolz library. Mastering Large Datasets teaches you to write easily readable, easily scalable Python code that can efficiently process large volumes of structured and unstructured data. By the end of this comprehensive guide, you'll have a solid grasp on the tools and methods that will take your code beyond the laptop and your data science career to the next level Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications., Summary Modern data science solutions need to be clean, easy to read, and scalable. In Mastering Large Datasets with Python , author J.T. Wolohan teaches you how to take a small project and scale it up using a functionally influenced approach to Python coding. You'll explore methods and built-in Python tools that lend themselves to clarity and scalability, like the high-performing parallelism method, as well as distributed technologies that allow for high data throughput. The abundant hands-on exercises in this practical tutorial will lock in these essential skills for any large-scale data science project. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology 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. About the book 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 firmly 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. What's inside An introduction to the map and reduce paradigm Parallelization with the multiprocessing module and pathos framework Hadoop and Spark for distributed computing Running AWS jobs to process large datasets About the reader For Python programmers who need to work faster with more data. About the author J. T. Wolohan is a lead data scientist at Booz Allen Hamilton, and a PhD researcher at Indiana University, Bloomington. Table of Contents: PART 1 1 ] Introduction 2 ] Accelerating large dataset work: Map and parallel computing 3 ] Function pipelines for mapping complex transformations 4 ] Processing large datasets with lazy workflows 5 ] Accumulation operations with reduce 6 ] Speeding up map and reduce with advanced parallelization PART 2 7 ] Processing truly big datasets with Hadoop and Spark 8 ] Best practices for large data with Apache Streaming and mrjob 9 ] PageRank with map and reduce in PySpark 10 ] Faster decision-making with machine learning and PySpark PART 3 11 ] Large datasets in the cloud with Amazon Web Services and S3 12 ] MapReduce in the cloud with Amazon's Elastic MapReduce, Summary Modern data science solutions need to be clean, easy to read, and scalable. In Mastering Large Datasets with Python , author J.T. Wolohan teaches you how to take a small project and scale it up using a functionally influenced approach to Python coding. You'll explore methods and built-in Python tools that lend themselves to clarity and scalability, like the high-performing parallelism method, as well as distributed technologies that allow for high data throughput. The abundant hands-on exercises in this practical tutorial will lock in these essential skills for any large-scale data science project. Purchase of the print book includes a free eBook in PDF, Kindle, and ePub formats from Manning Publications. About the technology 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. About the book 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 firmly 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. What's inside An introduction to the map and reduce paradigm Parallelization with the multiprocessing module and pathos framework Hadoop and Spark for distributed computing Running AWS jobs to process large datasets About the reader For Python programmers who need to work faster with more data. About the author J. T. Wolohan is a lead data scientist at Booz Allen Hamilton, and a PhD researcher at Indiana University, Bloomington. Table of Contents: PART 1 1 ¦ Introduction 2 ¦ Accelerating large dataset work: Map and parallel computing 3 ¦ Function pipelines for mapping complex transformations 4 ¦ Processing large datasets with lazy workflows 5 ¦ Accumulation operations with reduce 6 ¦ Speeding up map and reduce with advanced parallelization PART 2 7 ¦ Processing truly big datasets with Hadoop and Spark 8 ¦ Best practices for large data with Apache Streaming and mrjob 9 ¦ PageRank with map and reduce in PySpark 10 ¦ Faster decision-making with machine learning and PySpark PART 3 11 ¦ Large datasets in the cloud with Amazon Web Services and S3 12 ¦ MapReduce in the cloud with Amazon's Elastic MapReduce
LC Classification Number
QA76.73.P98W65 2019

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