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LEADER 00000cam a2200709 i 4500
003 OCoLC
005 20240129213017.0
006 m o d
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008 201112s2020 caua o 001 0 eng d
019 1220827695|a1223089267|a1225934204|a1226591554|a1228545474
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020 9781484265765|q(electronic bk.)
020 1484265769|q(electronic bk.)
020 1484265750
020 9781484265758
024 7 10.1007/978-1-4842-6576-5|2doi
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049 INap
082 04 005.7
082 04 005.7|223
099 eBook O'Reilly for Public Librairies
100 1 Patel, Jay M.,|eauthor.
245 10 Getting structured data from the Internet :|brunning web
crawlers/scrapers on a big data production scale /|cJay M.
Patel.|h[O'Reilly electronic resource]
264 1 [Berkeley, CA] :|bApress,|c[2020]
300 1 online resource (xix, 397 pages) :|billustrations
336 text|btxt|2rdacontent
337 computer|bc|2rdamedia
338 online resource|bcr|2rdacarrier
347 text file|2rdaft|0http://rdaregistry.info/termList/
fileType/1002
347 |bPDF
500 Includes index.
505 0 Chapter 1: Introduction to Web Scraping -- Chapter 2: Web
Scraping in Python Using Beautiful Soup Library -- Chapter
3: Introduction to Cloud Computing and Amazon Web Services
(AWS) -- Chapter 4: Natural Language Processing (NLP) and
Text Analytics -- Chapter 5: Relational Databases and SQL
Language -- Chapter 6: Introduction to Common Crawl
Datasets -- Chapter 7: Web Crawl Processing on Big Data
Scale -- Chapter 8: Advanced Web Crawlers --
520 Utilize web scraping at scale to quickly get unlimited
amounts of free data available on the web into a
structured format. This book teaches you to use Python
scripts to crawl through websites at scale and scrape data
from HTML and JavaScript-enabled pages and convert it into
structured data formats such as CSV, Excel, JSON, or load
it into a SQL database of your choice. This book goes
beyond the basics of web scraping and covers advanced
topics such as natural language processing (NLP) and text
analytics to extract names of people, places, email
addresses, contact details, etc., from a page at
production scale using distributed big data techniques on
an Amazon Web Services (AWS)-based cloud infrastructure.
It covers developing a robust data processing and
ingestion pipeline on the Common Crawl corpus, containing
petabytes of data publicly available and a web crawl data
set available on AWS's registry of open data. Getting
Structured Data from the Internet also includes a step-by-
step tutorial on deploying your own crawlers using a
production web scraping framework (such as Scrapy) and
dealing with real-world issues (such as breaking Captcha,
proxy IP rotation, and more). Code used in the book is
provided to help you understand the concepts in practice
and write your own web crawler to power your business
ideas. You will: Understand web scraping, its applications
/uses, and how to avoid web scraping by hitting publicly
available rest API endpoints to directly get data Develop
a web scraper and crawler from scratch using lxml and
BeautifulSoup library, and learn about scraping from
JavaScript-enabled pages using Selenium Use AWS-based
cloud computing with EC2, S3, Athena, SQS, and SNS to
analyze, extract, and store useful insights from crawled
pages Use SQL language on PostgreSQL running on Amazon
Relational Database Service (RDS) and SQLite using
SQLalchemy Review sci-kit learn, Gensim, and spaCy to
perform NLP tasks on scraped web pages such as name entity
recognition, topic clustering (Kmeans, Agglomerative
Clustering), topic modeling (LDA, NMF, LSI), topic
classification (naive Bayes, Gradient Boosting Classifier)
and text similarity (cosine distance-based nearest
neighbors) Handle web archival file formats and explore
Common Crawl open data on AWS Illustrate practical
applications for web crawl data by building a similar
website tool and a technology profiler similar to
builtwith.com Write scripts to create a backlinks database
on a web scale similar to Ahrefs.com, Moz.com,
Majestic.com, etc., for search engine optimization (SEO),
competitor research, and determining website domain
authority and ranking Use web crawl data to build a news
sentiment analysis system or alternative financial
analysis covering stock market trading signals Write a
production-ready crawler in Python using Scrapy framework
and deal with practical workarounds for Captchas, IP
rotation, and more.
590 O'Reilly|bO'Reilly Online Learning: Academic/Public
Library Edition
650 0 Big data.
650 0 Programming languages (Electronic computers)
650 0 Data mining.
650 0 Automatic data collection systems.
650 2 Data Mining
650 6 Données volumineuses.
650 6 Exploration de données (Informatique)
650 6 Collecte automatique des données.
650 7 Data mining|2fast
650 7 Automatic data collection systems|2fast
650 7 Big data|2fast
650 7 Programming languages (Electronic computers)|2fast
776 08 |iPrint version:|z9781484265758
776 08 |iPrint version:|z9781484265772
856 40 |uhttps://ezproxy.naperville-lib.org/login?url=https://
learning.oreilly.com/library/view/~/9781484265765/?ar
|zAvailable on O'Reilly for Public Libraries
938 Askews and Holts Library Services|bASKH|nAH37890098
938 ProQuest Ebook Central|bEBLB|nEBL6395781
938 YBP Library Services|bYANK|n301736281
938 EBSCOhost|bEBSC|n2678338
994 92|bJFN