Getting Structured Data from the Internet: Running Web Crawlers/Scrapers on a Big Data Production Scale
Patel, Jay M.
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 petabytes of data publicly available and a web crawl data set available on AWS's registry of open data.
What You Will Learn
- Understand web scraping, its applications/uses, and how to avoid web scraping by hitting publicly available rest API endpoints to directly get data
- 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
Who This Book Is ForPrimary audience: data analysts and scientists with little to no exposure to real-world data processing challenges, secondary: experienced software developers doing web-heavy data processing who need a primer, tertiary: business owners and startup founders who need to know more about implementation to better direct their technical team
Jay graduated with a bachelor's degree in engineering from the Institute of Chemical Technology, University of Mumbai, India and a master of science degree from the University of Georgia, USA. Jay serves as an editor of a publication titled Web Data Extraction and also blogs about personal projects, open source packages, and experiences as a startup founder on his personal site, jaympatel.com.