The Ultimate Guide to Web Data Extraction


*All you need to know to get started with web data extraction. *


1 Introduction 3

*2 Applications of web data extraction 5 *

*3 Different approaches to data extraction 12 *

*4 How web data extraction works 21 *

*5 Best practices in web data extraction 25 *

6 Finding reliable sources 28

*7 Legal aspects of web crawling 30 *

8 Conclusion 31

Ultimate Guide to Web Data Extraction 2


Web data extraction (also known as web scraping,

web harvesting, screen scraping, etc.) is a

technique for extracting huge amounts of data

from websites on the internet. The data available

on websites is generally not available to download

easily and can only be accessed by using a web

browser. However, web is the largest repository of

open data and this data has been growing at

exponential rates since the inception of internet.

Web data is of great use to Ecommerce portals,

media companies, research firms, data scientists,

government and can even help the healthcare

industry with ongoing research and making

predictions on the spread of diseases.

Ultimate Guide to Web Data Extraction 3


Consider the data available on classifieds sites, real estate portals, social networks, retail sites, and

online shopping websites etc. being easily

available in a structured format, ready to be

analyzed. Most of these sites don’t provide the

functionality to save their data to a local or cloud

storage. Some sites provide APIs, but they typically

come with restrictions and aren’t reliable enough.

Although it’s technically possible to copy and paste

data from a website to your local storage, this is

inconvenient and out of question when it comes to

practical use cases for businesses.

Web scraping helps you do this in an automated

fashion and does it far more efficiently and

accurately. A web scraping setup interacts with

websites in a way similar to a web browser, but

instead of displaying it on a screen, it saves the

data to a storage system.

Ultimate Guide to Web Data Extraction 4

*Applications of web data *


1. Pricing intelligence

Pricing intelligence is an application that’s gaining

popularity by each passing day given the tightening

of competition in the online space. E-commerce

portals are always watching out for their competitors

using web crawling to have real time pricing data

from them and to fine tune their own catalogs with

competitive pricing. This is done by deploying web

crawlers that are programmed to pull product details

like product name, price, variant and so on. This data is plugged into an automated system that assigns

ideal prices for every product after analyzing the

competitors’ prices.

Ultimate Guide to Web Data Extraction 5

*Applications of web data *


Pricing intelligence is also used in cases where

there is a need for consistency in pricing across

different versions of the same portal. The

capability of web crawling techniques to extract

prices in real time makes such applications a


2. Cataloging

Ecommerce portals typically have a huge number

of product listings. It’s not easy to update and

maintain such a big catalog. This is why many

companies depend on web date extractions

services for gathering data required to update

their catalogs. This helps them discover new

categories they haven’t been aware of or update

existing catalogs with new product descriptions,

images or videos.

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*Applications of web data *


3. Market research

Market research is incomplete unless the amount

of data at your disposal is huge. Given the







acquisition and considering the volume of relevant

data available on the web, web data extraction is

by far the easiest way to gather data required for

market research. The shift of businesses from brick

and mortar stores to online spaces has also made

web data a better resource for market research.

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*Applications of web data *


4. Sentiment analysis

Sentiment analysis requires data extracted from

websites where people share their reviews,

opinions or complaints about services, products,

movies, music or any other consumer focused

offering. Extracting this user generated content

would be the first step in any sentiment analysis

project and web scraping serves the purpose


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*Applications of web data *


5. Competitor analysis

The possibility of monitoring competition was







technologies came along. By deploying web

spiders, it’s now easy to closely monitor the

activities of your competitors like the promotions

they’re running, social media activity, marketing

strategies, press releases, catalogs etc. in order to

have the upper hand in competition. Near real

time crawls take it a level further and provide

businesses with real time competitor data.

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*Applications of web data *


6. Content aggregation

Media websites need instant access to breaking

news and other trending information on the web

on a continuous basis. Not being quick at reporting

news is a deal breaker for these companies. Web

crawling makes it possible to monitor or extract

data from popular news portals, forums or similar

sites for trending topics or keywords that you want

to monitor. Low latency web crawling is used for

this use case as the update speed should be very


Ultimate Guide to Web Data Extraction 10

*Applications of web data *


7. Brand monitoring

Every brand now understands the importance of

customer focus for business growth. It would be in

their best interests to have a clean reputation for

their brand if they want to survive in this

competitive market. Most companies are now

using web crawling solutions to monitor popular

forums, reviews on ecommerce sites and social

media platforms for mentions of their brand and

product names. This in turn can help them stay

updated to the voice of the customer and fix

issues that could ruin brand reputation at the

earliest. There’s no doubt about a customer-

focused business going up in the growth graph.

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*Different approaches to web data *


There are businesses that function solely based on

data, others use it for business intelligence,

competitor analysis and market research among

other countless use cases. However, extracting

massive amounts of data from the web is still a major

roadblock for many companies, more so because

they are not going through the optimal route. Here is

a detailed overview of different ways by which you

can extract data from the web.

1. Data as a Service

Outsourcing your web data extraction project to a

DaaS provider is by far the best way to extract data

from the web. When depending on a data provider,

you are completely relieved from the responsibility of crawler setup, maintenance and quality inspection of

the data being extracted.

Ultimate Guide to Web Data Extraction 12

*Different approaches to web data *


Since DaaS companies would have the necessary

expertise and infrastructure required for a smooth

and seamless data extraction, you can avail their

services at a much lower cost than what you’d incur

by doing it yourself.

Providing the DaaS provider with your exact

requirements is all you need to do and rest is

assured. You would have to send across details like

the data points, source websites, frequency of crawl,

data format and delivery methods. With DaaS, you

get the data exactly the way you want, and you can

rather focus on utilizing the data to improve your

business bottom lines, which should ideally be your

priority. Since they are experienced in scraping and

possess domain knowledge to get the data efficiently

and at scale, going with a DaaS provider is the right

option if your requirement is large and recurring.

One of the biggest benefits of outsourcing is the data quality assurance.

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*Different approaches to web data *


Since the web is highly dynamic in nature, data






maintenance to work smoothly. Web data extraction

services tackle all these challenges and deliver noise-free data of high quality.

Another benefit of going with a data extraction

service is the customization and flexibility. Since

these services are meant for enterprises, the offering is completely customizable according to your specific



• Completely customizable for your requirement

• Takes complete ownership of the process

• Quality checks to ensure high quality data

• Can handle dynamic and complicated websites

• More time to focus on your core business


• Might have to enter into a long-term contract

• Slightly costlier than DIY tools

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*Different approaches to web data *


2. In house data extraction

You can go with in house data extraction if your

company is technically rich. Web scraping is a

technically niche process and demands a team of

skilled programmers to code the crawler, deploy

them on servers, debug, monitor and do the post

processing of extracted data. Apart from a team, you

would also need high end infrastructure to run the

crawling jobs.

Maintaining the in-house crawling setup can be a

bigger challenge than building it. Web crawlers tend

to be very fragile. They break even with small

changes or updates in the target websites. You would

have to setup a monitoring system to know when

something goes wrong with the crawling task, so that

it can be fixed to avoid data loss. You will have to

dedicate time and labor into the maintenance of the

in-house crawling setup.

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*Different approaches to web data *


Apart from this, the complexity associated with

building an in-house crawling setup would go up

significantly if the number of websites you need to

scrape is high or the target sites are using dynamic

coding practices. An in-house crawling setup would

also take a toll on the focus and dilute your results as web scraping itself is something that needs

specialization. If you aren’t cautious, it could easily hog your resources and cause friction in your

operational workflow.


• Total ownership and control over the process

• Ideal for simpler requirements


• Maintenance of crawlers is a headache

• Increased cost

• Hiring, training and managing a team might be hectic

• Might hog on the company resources

• Could affect the core focus of the organization

• Infrastructure is costly

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*Different approaches to web data *


3. Vertical specific solutions

There are data providers that cater to only a specific industry vertical. Vertical specific data extraction

solutions are great if you could find one that’s

catering to the domain you are targeting and covers

all your necessary data points. The benefit of going








comprehensiveness of data that you would get. Since

these solutions cater to only one specific domain,

their expertise in that domain would be very high.

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*Different approaches to web data *


The schema of data sets you would get from vertical

specific data extraction solutions are typically fixed and won’t be customizable. Your data project will be

limited to the data points provided by such solutions, but this may or may not be a deal breaker depending

on your requirements. These solutions typically give

you datasets that are already extracted and is ready

to use.

A good example for a vertical specific data extraction

solution is JobsPikr, which is a job listings

data solution that extracts data directly from career

pages of company websites from across the world.


• Comprehensive data from the industry

• Faster access to data

• No need to handle the complicated aspects of



• Lack of customization options

• Data is not exclusive

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*Different approaches to web data *


4. DIY data extraction tools

If you don’t have the budget for building an in-house

crawling setup or outsourcing your data extraction

process to a vendor, you are left with DIY tools. These tools are easy to learn and often provide a point and

click interface to make data extraction simpler than

you could ever imagine. These tools are an ideal

choice if you are just starting out with no budgets for data acquisition. DIY web scraping tools are usually

priced very low and some are even free to use.

However, there are serious downsides to using a DIY

tool to extract data from the web. Since these tools

wouldn’t be able to handle complex websites, they

are very limited in terms of functionality, scale, and the efficiency of data extraction. Maintenance will

also be a challenge with DIY tools as they are made in a rigid and less flexible manner.

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*Different approaches to web data *


You will have to make sure that the tool is working

and even make changes from time to time.

The only good side is that it doesn’t take much

technical expertise to configure and use such tools,

which might be right for you if you aren’t a technical person. Since the solution is readymade, you will also save the costs associated with building your own

infrastructure for scraping. With the downsides

apart, DIY tools can cater to simple and small scale

data requirements.


• Prebuilt solution

• You can avail support for the tools

• Easier to configure and use


• They get outdated often

• More noise in the data

• Less customization options

• Interruption in data flow in case of structural changes Ultimate Guide to Web Data Extraction 20

How web data extraction works

There are several different methods and technologies

that can be used to build a crawler and extract data

from the web.

1. The seed

A seed URL is where it all starts. A crawler would

start its journey from the seed URL and start looking

for the next URL in the data that’s fetched from the

seed. If the crawler is programmed to traverse

through the entire website, the seed URL would be

same as the root of the domain. The seed URL is

programmed into the crawler at the time of setup

and would remain the same throughout the

extraction process.

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How web data extraction works

2. Setting directions

Once the crawler fetches the seed URL, it would have

different options to proceed further. These options

would be hyperlinks on the page that it just loaded

by querying the seed URL. The second step is to

program the crawler to identify and take different

routes by itself from this point. At this point, the bot knows where to start and where to go from there.

3. Queueing

Now that the crawler knows how to get into the

depths of a website and reach pages where the data

to be extracted is, the next step is to compile all

these destination pages to a repository that it can

pick the URLs to crawl. Once this is complete, the

crawler starts fetching the URLs from the repository.

It saves these pages as HTML files on either a local or cloud based storage space.

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How web data extraction works

The final scraping happens at this repository of HTML


4. Data extraction

Now that the crawler has saved all the pages that

needs to be scraped, it’s time to extract only the

required data points from these pages. The schema

used will be in accordance with your requirement.

Now is the time to instruct the crawler to pick only

the relevant data points from these HTML files and

ignore the rest. The crawler can be taught to identify data points based on the HTML tags or class names

associated with the data points.

6. Deduplication and cleansing

Deduplication is a process done on the extracted

records to eliminate the chances of duplicates in the

extracted data.

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How web data extraction works

This will require a separate system that can look for

duplicate records and remove them to make the data

concise. The data could also have noise in it, which

needs to be cleaned too. Noise here refers to

unwanted HTML tags or text that got scraped along

with the relevant data.

6. Structuring

Structuring is what makes the data compatible with

databases and analytics systems by giving it a proper, machine readable syntax. This is the final process in

data extraction and post this, the data is ready for

delivery. With structuring done, the data is ready to

be consumed either by importing it to a database or

plugging it to an analytics system.

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*Best practices in web data *

*extraction *

As a great tool for deriving powerful insights, web







businesses in this competitive market. As is the case

with most powerful things, web scraping must be

used responsibly. Here is a compilation of the best

practices that you must follow while scraping


1. Respect the robots.txt

You should always check the Robots.txt file of a

website you are planning to extract data from.

Websites set rules on how bots should interact with

the site in their robots.txt file. Some sites even block crawler access completely in their robots file.

Extracting data from sites that disallow crawling is

can lead to legal ramifications and should be avoided.

Apart from outright blocking, every site would have

set rules on good behavior on their site in the

robots.txt. You are bound to follow these rules while

extracting data from the target site.

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*Best practices in web data *

*extraction *

2. Do not hit the servers too frequently

Web servers are susceptible to downtimes if the load

is very high. Just like human users, bots can also add load to the website’s server. If the load exceeds a

certain limit, the server might slow down or crash,

rendering the website unresponsive for the users.

This creates a bad user experience for the human

visitors on the website which defies the whole

purpose of that site. It should be noted that the

human visitors are of higher priority for the website

than bots. To avoid such issues, you should set your

crawler to hit the target site with a reasonable

interval and limit the number of parallel requests.

This will give the website some breathing space,

which it should indeed have.

3. Scrape during off peak hours

To make sure that the target website doesn’t slow

down due to a high traffic from humans as well as

bots, it is better to schedule your web crawling tasks to run in the off-peak hours.

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*Best practices in web data *

*extraction *

The off-peak hours of the site can be determined by

the geo location of where the site’s majority of traffic is from. You can avoid possible overload on the

website’s servers by scraping during off-peak hours.

This will also have a positive effect on the speed of

your data extraction process as the server would

respond faster during this time.

4. Use the scraped data responsibly

Extracting data from the web has become an

important business process. However, this doesn’t

mean you own the data you extracted from a website

on the internet.

Publishing the data elsewhere without the consent of

the website you are scraping can be considered

unethical and you could be violating copyright laws.

Using the data responsibly and in line with the target website’s policies is something you should practice

while extracting data from the web.

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Finding reliable sources

1. Avoid sites with too many Broken links

Links are like the connecting tissue of the internet. A website that has too many broken links is a bad

choice for a web data extraction project. This is an

indicator of the poor maintenance of the site and

crawling such a site won’t be a good experience for

you. For one, a scraping setup can come to a halt if it encounters a broken link during the fetching process.

This would eventually tamper the data quality, which

should be a deal breaker for anyone who’s serious

about the data project. You are better off with a

different source website that has similar data and

better housekeeping.

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Finding reliable sources

2. Avoid sites with highly dynamic coding practices This might not always be an option; however, it is

better to avoid sites with complex and dynamic

practices to have a stable crawling job running. Since dynamic sites tend to be difficult to extract data from and change very frequently, maintenance could

become a huge bottleneck. It’s always better to find

less complex sites when it comes to web crawling.

3. Quality and freshness of the Data

The quality and freshness of data must be one of

your most important criteria while choosing sources

for data extraction. The data that you acquire should

be fresh and relevant to the current time-period for it to be of any use at all. Always look for sites that are updated frequently with fresh and relevant data

when selecting sources for your data extraction

project. You could check the last modified date on

the site’s source code to get an idea of how fresh the data is.

Ultimate Guide to Web Data Extraction 29

Legal aspects of web crawling

Web data extraction is sometimes seen with clouded

eyes by people who aren’t very familiar with the

concept. To clear the air, web scraping/crawling is not an unethical or illegal activity. The way a crawler bot fetches information from a website is in no different

from a human visitor consuming the content on a

webpage. Google search, for example runs of web

crawling and we don’t see anyone accusing Google of

doing something even remotely illegal. However,

there are some ground rules you should follow while

scraping websites. If you follow these rules and

operate as a good bot on the internet, you aren’t

doing anything illegal. Here are the rules to follow:

• Respect the robots.txt file of the target site

• Make sure you are staying compliant to the TOS


• Do not reproduce the data elsewhere, online or

offline without prior permission from the site

• If you follow these rules while crawling a website,

you are completely in the safe zone.

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We covered the important aspects of web data

extraction here like the different routes you can take to acquire web data, best practices, various business

applications and the legal aspects of the process. As

the business world is rapidly moving towards a data-

centric operational model, it’s high time to evaluate

your data requirements and get started with

extracting relevant data from the web to improve

your business efficiency and boost the revenues. This

guide should help you get going in case you get stuck

during the journey.

About the Author:

Jacob Koshy

Jacob is a blogger and marketer who is

passionate about newer innovations in

technology like Big Data and the Internet

of Things. You can follow him on twitter


Ultimate Guide to Web Data Extraction 31

Big Data Made Small


[email protected]

The Ultimate Guide to Web Data Extraction

This is a comprehensive eBook on acquiring data from the web to complement various business use cases ranging from pricing intelligence to market research. Enterprises, data scientists and tech enthusiasts who want to learn the nuances of web data extraction will find this resourceful and informative. This can also help you in your hunt for a suitable means of data acquisition.

  • Author: PromptCloud
  • Published: 2017-05-30 08:05:09
  • Words: 3912
The Ultimate Guide to Web Data Extraction The Ultimate Guide to Web Data Extraction