What is Web Scraping: Introduction, Applications and Best Practices
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.
“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.
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,
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.
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 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.
A good web scraping project follows these practices. These
ensure that you get the data you are looking for while being non-disruptive to
the data sources.
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?
Where this information is typically published?
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.
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?
Since web scraping is mostly automated, tool selection is
crucial. The following points need to be kept in mind when finalizing tool
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
Availability of documentation
the scraping schema
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:
URL used to apply for the position
Remuneration data if it is available
Any special skills listed
and larger jobs
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.
Parse the HTML
Retrieve the desired item as per your scraping
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:
Scheduling for the created scrape
Third-party integration support (E.g., for Git,
Scrape templates for similar websites
Depending on the tool, end-users can access the data from
web scraping in several formats:
SQL Server database
Script (A script provides data from almost any
the performance and reliability of your scrape
Tools and scripts often follow a few best practices while
web scraping large amounts of data.
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.
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.
Enable the following options in your scraping tool or script – ‘Ignore cache,’ ‘Ignore certificate errors,’ and ‘Ignore to run ActiveX and flash.’
Call a terminate process after every scrape session is complete
Avoid the use of multiple web browsers for each scrape
Handle memory leaks
Things to stay away from
There are a few no-no’s when
setting up and executing a web scraping project.
Avoid sites with too many broken links
Stay away from sites that have too many missing values in their data fields
Sites that require a CAPTCHA authentication to show data
Some websites have an endless loop of pagination. Here the scraping tool would start from the beginning once the number of pages exhausts.
Web scraping iframe-based websites
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.
At Hir Infotech, we know that every dollar you spend on your business is an investment, and when you don’t get a return on that investment, it’s money down the drain. To ensure that we’re the right business with you before you spend a single dollar, and to make working with us as easy as possible, we offer free quotes for your project.