東華大學圖書館 |

語系: 繁體中文

說明(常見問題)

回圖書館首頁

手機版館藏查詢

登入

回首頁

切換: 標籤 | MARC模式 | ISBD

Getting structured data from the Int...

Patel, Jay M.

FindBook

Google Book

Amazon

博客來

Getting structured data from the Internet = running web crawlers/Scrapers on a Big Data Production Scale /

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Getting structured data from the Internet/ by Jay M. Patel.
其他題名:	running web crawlers/Scrapers on a Big Data Production Scale /
作者:	Patel, Jay M.
出版者:	Berkeley, CA :Apress : : 2020.,
面頁冊數:	xix, 397 p. :ill., digital ;24 cm.
內容註:	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.
Contained By:	Springer Nature eBook
標題:	Big data. -
電子資源:	https://doi.org/10.1007/978-1-4842-6576-5
ISBN:	9781484265765

Getting structured data from the Internet = running web crawlers/Scrapers on a Big Data Production Scale /
Patel, Jay M.

Getting structured data from the Internetrunning web crawlers/Scrapers on a Big Data Production Scale /[electronic resource] :by Jay M. Patel. - Berkeley, CA :Apress :2020. - xix, 397 p. :ill., digital ;24 cm.

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.

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.

ISBN: 9781484265765

Standard No.: 10.1007/978-1-4842-6576-5doiSubjects--Topical Terms:

2045508
Big data.

LC Class. No.: QA76.9.B45 / P38 2020

Dewey Class. No.: 005.7

Getting structured data from the Internet = running web crawlers/Scrapers on a Big Data Production Scale /
LDR:04404nmm a2200325 a 4500 001 2256815
003 DE-He213
005 20210225134404.0
006 m d
007 cr nn 008maaau
008 220420s2020 cau s 0 eng d
020 $a 9781484265765 $q (electronic bk.)
020 $a 9781484265758 $q (paper)
024 7 $a 10.1007/978-1-4842-6576-5 $2 doi
035 $a 978-1-4842-6576-5
040 $a GP $c GP
041 0 $a eng
050 4 $a QA76.9.B45 $b P38 2020
072 7 $a UN $2 bicssc
072 7 $a COM021000 $2 bisacsh
072 7 $a UN $2 thema
082 0 4 $a 005.7 $2 23
090 $a QA76.9.B45 $b P295 2020
100 1 $a Patel, Jay M. $3 3527432
245 1 0 $a Getting structured data from the Internet $h [electronic resource] : $b running web crawlers/Scrapers on a Big Data Production Scale / $c by Jay M. Patel.
260 $a Berkeley, CA : $b Apress : $b Imprint: Apress, $c 2020.
300 $a xix, 397 p. : $b ill., digital ; $c 24 cm.
505 0 $a 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 $a 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.
650 0 $a Big data. $3 2045508
650 0 $a Programming languages (Electronic computers) $3 606806
650 0 $a Data mining. $3 562972
650 0 $a Automatic data collection systems. $3 684484
650 1 4 $a Big Data. $3 3134868
650 2 4 $a Programming Languages, Compilers, Interpreters. $3 891123
710 2 $a SpringerLink (Online service) $3 836513
773 0 $t Springer Nature eBook
856 4 0 $u https://doi.org/10.1007/978-1-4842-6576-5
950 $a Business and Management (SpringerNature-41169)