Getting Structured Data from the Internet: Running Web Crawlers/Scrapers on a Big Data Production Scale

Getting Structured Data from the Internet: Running Web Crawlers/Scrapers on a Big Data Production Scale
By 作者:Jay M. Patel
Edition:1st ed.
Release Finelybook 出版日期:November 2020
Publisher Finelybook 出版社:Apress
pages 页数: Paperback
ISBN-13 书号:9781484265758
ISBN-10 书号:1484265750
The Book Description robot was collected from Amazon and arranged by Finelybook
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 book covers developing a robust data processing and ingestion pipeline on the Common Crawl corpus, containing petaBy 作者:tes 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 作者:-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.
What You Will Learn

Understand web scraping, its applications/uses, and how to avoid web scraping By 作者: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 作者:building a similar website tool and a technology profiler similar to
Write scripts to create a backlinks database on a web scale similar to,,, 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

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