What is web scraping?

What is Web Scraping ?
Introduction, Applications and Best Practices

Table of contents
Introduction
Basics of Web Scraping
Typical applications of web scraping
Identify the goal
Tool analysis
Designing the scraping schema
Test runs and larger jobs
Output formats
Improving the performance and reliability of your scrape
Things to stay away from
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Introduction
Web scraping typically extracts large amounts of data from websites for a variety of uses
such as price monitoring, enriching machine learning models, financial data aggregation,
monitoring consumer sentiment, news tracking, etc. Browsers show data from a website.
However, manually copy data from multiple sources for retrieval in a central place can be
very tedious and time-consuming. Web scraping tools essentially automate this manual
process.

Basics of Web Scraping
“Web scraping,” also called crawling or spidering, is the automated gathering of data from an
online source usually from a website. While scraping is a great way to get massive amounts
of data in relatively short timeframes, it does add stress to the server where the source
hosted.
Primarily why many websites disallow or ban scraping all together. However, as long as it
does not disrupt the primary function of the online source, it is relatively acceptable.
Despite its legal challenges, web scraping remains popular even in 2019. The prominence
and need for analytics have risen multifold. This, in turn, means various learning models and
analytics engine need more raw data. Web scraping remains a popular way to collect
information. With the rise of programming languages such a Python, web scraping has
made significant leaps.

Social media sentiment analysis
The shelf life of social media posts is very little. However, when looked at collectively, they
show valuable trends. While most social media platforms have APIs that let 3rd party tools
access their data, this may not always be sufficient. In such cases scraping these websites
gives access to real-time information such as trending sentiments, phrases, topics, etc.
E-Commerce pricing
Many E-Commerce sellers often have their products listed on multiple marketplaces. With
scraping, they can monitor the pricing on various platforms and make a sale on the market
where the profit is higher.
Investment opportunities
Real estate investors often want to know about promising neighborhoods they can invest in
that. While there are multiple ways to get this data, web scraping travel marketplaces and
hospitality brokerage websites offer valuable information. It includes information such as the
highest-rated areas, amenities that typical buyers look for, locations that may be upcoming
as attractive renting options, etc.

Machine learning
Machine learning models need raw data to evolve and improve. Web scraping tools can
scrape a large number of data points, text and images in a relatively short time. Machine
learning is fueling today’s technological marvels such as driverless cars, space flight, image
and speech recognition. However, these models need data to improve their accuracy and
reliability.

Identify the goal
Any web scraping project begins with a need. A goal detailing the expected outcomes is
necessary and is the most basic need for a scraping task. The following set of questions
need to ask while identifying the need for a web scraping project:
 What kind of information do we expect to seek?
 What will be the outcome of this scraping activity?
 Who are the end-users who will consume this data?
 Where will the extracted data be stored? E.g., on Cloud or on premise storage, on an
external database, etc.
 How should this data be presented to its end-users? E.g., as a CSV/Excel/JSON file or as an
SQL database, etc. What kind of information do we expect to seek?
 How often are the source websites refreshed with new data? In other words, what is the
typical shelf-life of the data? That collected and how often does it have to be updated?
 Post the scraping activity, what are the types of reports you would want to generate?

Tool analysis
Since web scraping is mostly automated, tool selection is crucial. The following points
need to be kept in mind when finalizing tool selection:
 Fitment with the needs of the project
 Supported operating systems and platforms
 Free/open-source or paid tool
 Support for scripting languages
 Support for built-in data storage.
 Available selectors
 Availability of documentation

Let’s assume that our scraping job collects data from job sites about open positions listed
by various organizations. The data source would also dictate the schema attributes. The
schema for this job would look something like this:
 Job ID
 Title
 Job description
 URL used to apply for the position
 Job location
 Remuneration data if it is available
 Job type
 Experience level
 Any special skills listed

It is a no-brainer and a test run will help you identify any roadblocks or potential issues before
running a more significant role. While there is no guarantee that there will be no surprises
later on, results from the test run are a good indicator of what to expect going forward.
1) Parse the HTML
2) Retrieve the desired item as per your scraping schema
3) Identify URLs pointing to subsequent pages
Once we are happy with the test run, we can now generalize the scope and move ahead with
a more massive scrape. Here we need to understand how a human would retrieve data from
each page. Using regular expressions, we can accurately match and retrieve the correct data.
Subsequently, we also need to catch the correct XPath’s and replace them with hardcoded
values if necessary. You may also need support from an external library.
Often you may need external libraries that act as inputs on the source. E.g., you may need to
enter the Country, State and Zipcode to identify the correct values that you need.

Here are a few additional points to check
1) Command-line interface
2) Scheduling for the created scrape
3) Third-party integration support (E.g., for Git, TFS, Bitbucket)
4) Scrape templates for similar websites

Output formats
Depending on the tool, end-users can access the data from web scraping in several
formats:
1) CSV (Comma Separated)
2) JSON, XML
3) Excel, Google Sheet Share
4) Word, PDF
5) SQL Server Database
6) Cloud Upload
7) Direct Import in CRM, ERP
8) Script (A script provides data from almost any data source)

Improving the performance and reliability of your scrape
Tools and scripts often follow a few best practices while web scraping large amounts of
data.
1) If possible, avoid the use of images while web scraping. If you need images, you must
store these in a local drive and update the database with the appropriate path.
2) Certain Java script features can cause instability. Certain dynamic features may cause
memory leaks, websites hang or even crashes. In such scenarios, a few tools use web
crawler agents to facilitate the scrape. Very often, using a web crawler agent can be up to
100 times faster than using a web browser agent.
3) Enable the following options in your scraping tool or script – ‘Ignore cache,’ ‘Ignore
certificate errors,’ and ‘Ignore to run ActiveX and flash.’
4) Call a terminate process after every scrape session is complete
5) Avoid the use of multiple web browsers for each scrape.
In many cases, the scraping job may have to collect vast amounts of data. It may take too
much time and encounter timeouts and endless loops. Hence tool identification and
understanding its capabilities are essential. Here are a few best practices to help you better
tune your scraping models for performance and reliability.

Things to stay away from
There are a few no-no’s when setting up and executing a web scraping project.
1) Avoid sites with too many broken links
2) Stay away from sites that have too many missing values in their data fields
3) Sites that require a CAPTCHA authentication to show data
4) Some websites have an endless loop of pagination. Here the scraping tool would start
from the beginning once the number of pages exhausts.
5) Web scraping iframe-based websites.
6) Once a certain connection threshold reaches, some websites may prevent users from
scraping it further. While you can use proxies and different user headers to complete the
scraping, it is vital to understand the reason why these measures are in place. If a website
has taken steps to prevent web scraping, these should be respected and left alone.
Forcibly web scraping such sites is illegal.
Web scraping has been around since the early days of the internet. While it can provide you
the data you need, certain care, caution and restraint should exercise. A properly planned and
executed web scraping project can yield valuable data – one that will be useful for the end-
user.

About HIR INFOTECH
Hir Infotech is a leading global outsourcing company with its core focus on offering web
scraping, data extraction, lead generation, data scraping, Data Processing, Digital marketing,
Web Design & Development, Web Research services and developing web crawler, web
scraper, web spiders, harvester, bot crawlers, and aggregators’ software's. Our team of
dedicated and committed professionals is a unique combination of strategy, creativity, and
technology
Contact Information
Phone : +91 99099 90610
Email Id : inquiry@hirinfotech.com
Website : https://hirinfotech.com
Office Address : B109, Ganesh Glory, Jagatpur Road, SG Highway, Gota, Ahmedabad 382481,
Gujarat, India

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