Chapter 1, Getting Started with Scraping, introduces several concepts and tools for web scraping. We will examine how to install and do basic tasks with tools such as requests, urllib, BeautifulSoup, Scrapy, PhantomJS and Selenium.
Chapter 2, Data Acquisition and Extraction, is based on an understanding of the structure of HTML and how to find and extract embedded data. We will cover many of the concepts in the DOM and how to find and extract data using BeautifulSoup, XPath, LXML, and CSS selectors. We also briefly examine working with Unicode / UTF8.
Chapter 3, Processing Data, teaches you to load and manipulate data in many formats, and then how to store that data in various data stores (S3, MySQL, PostgreSQL, and ElasticSearch). Data in web pages is represented in various formats, the most common being HTML, JSON, CSV, and XML We will also examine the use of message queue systems, primarily AWS SQS, to help build robust data processing pipelines.
Chapter 4, Working with Images, Audio and other Assets, examines the means of retrieving multimedia items, storing them locally, and also performing several tasks such as OCR, generating thumbnails, making web page screenshots, audio extraction from videos, and finding all video URLs in a YouTube playlist.
Chapter 5, Scraping – Code of Conduct, covers several concepts involved in the legality of scraping, and practices for performing polite scraping. We will examine tools for processing robots.txt and sitemaps to respect the web host's desire for acceptable behavior. We will also examine the control of several facets of crawling, such as using delays, containing the depth and length of crawls, using user agents, and implementing caching to prevent repeated requests.
Chapter 6, Scraping Challenges and Solutions, covers many of the challenges that writing a robust scraper is rife with, and how to handle many scenarios. These scenarios are pagination, redirects, login forms, keeping the crawler within the same domain, retrying requests upon failure, and handling captchas.
Chapter 7, Text Wrangling and Analysis, examines various tools such as using NLTK for natural language processing and how to remove common noise words and punctuation. We often need to process the textual content of a web page to find information on the page that is part of the text and neither structured/embedded data nor multimedia. This requires knowledge of using various concepts and tools to clean and understand text.
Chapter 8, Searching, Mining, and Visualizing Data, covers several means of searching for data on the Web, storing and organizing data, and deriving results from the identified relationships. We will see how to understand the geographic locations of contributors to Wikipedia, finding relationships between actors on IMDB, and finding jobs on Stack Overflow that match specific technologies.
Chapter 9, Creating a Simple Data API, teaches us how to create a scraper as a service. We will create a REST API for a scraper using Flask. We will run the scraper as a service behind this API and be able to submit requests to scrape specific pages, in order to dynamically query data from a scrape as well as a local ElasticSearch instance.
Chapter 10, Creating Scraper Microservices with Docker, continues the growth of our scraper as a service by packaging the service and API in a Docker swarm and distributing requests across scrapers via a message queuing system (AWS SQS). We will also cover scaling of scraper instances up and down using Docker swarm tools.
Chapter 11, Making the Scraper as a Service Real, concludes by fleshing out the services crated in the previous chapter to add a scraper that pulls together various concepts covered earlier. This scraper can assist in analyzing job posts on StackOverflow to find and compare employers using specified technologies. The service will collect posts and allow a query to find and compare those companies.
