Today, we are surrounded by data everywhere. Data has become easily accessible. So, the challenge that arises out of it is how to make the most of the available data! The first step towards using such vast amounts of data is finding the right data integration tool that could help you to study, analyse and manage dynamically different data from numerous sources. However, the bigger challenge before using the data is data extraction.

Therefore, now we are going to see in details what exactly this extraction of data is, what tools are available for the same and what role it plays in integrating data.

What is data extraction?

In simple words, it is the collection of different types of data from multiple sources, most of which are unorganised or purely unstructured.

Data extraction is mainly about consolidating, processing and refining the unstructured data and storing it on a centralised location for further transformation. You may store it on-site, or on cloud based platforms or a hybrid of both.

Data Extraction and ETL: How does the process work?

Let us have a brief look at the ETL process for a better understanding. With the help of ETL, the companies can collect data from different sources and store them on a centralised location and assimilate various and differing data into a common and understandable format. Basically, the ETL process involves:

  1. Extraction: This process mainly deals with getting the data from various different sources. The extraction finds and locates relevant data and makes it suitable for further processing.
  2. Transformation: After extraction is complete, it is now time for refining the data. During this process, the data is organised and cleansed. The main elements in this process include erasing the duplicate entries, removing the missing values etc. At the end of the transformation phase, what we are left with is reliable, and usable data.
  3. Loading: Once the transformation of data is complete, the processed and high-quality data is loaded onto a centralised storage location for further use and analysis.

Many companies use data extraction for a number of reasons. It could be to streamline processes or support compliant efforts or so on.

Because now we are clear about what the process of data extraction is, let us have a look at what are the tools or methodologies available to extract the data.

Types of Data Extraction Tools

When it comes to extracting data, the two key decisions that data engineers have to take while designing the process are

What method to choose for extraction?

When it comes to selecting the extraction method, there are two options with the data engineers. They can go for either logical or physical modes of extraction. Under the logical extraction, there are further two ways – full extraction and incremental extraction.

Now, let us look at these extraction methods in brief.

Physical extraction

Sometimes, there could be certain limitation with the source systems. Say for example, if you are trying to extract data from an outdated data storage unit, you will not be able to do it using logical extraction and you are left with only the physical way to do it. There are two types of physical extraction

Online extraction – where data is directly transferred from the source to the data warehouse by directly connecting the extraction tools to the source system or the transitional system.

Offline extraction – where there is no direct extraction and the process has to be carried out outside the source unit. In this process, the data in question is already organised.

Logical Extraction

There are two kinds of logical extraction:

Full extraction: Under this process, all the data is extracted from the source system at one go directly. Any need for extra information, be it logical or technological, does not arise. For example, if you are trying to export a file on price change, the system will extract the entire financial records of the organisation.

Incremental extraction: This process deals with the incremental or delta changes in the data. The extraction tool recognises new or altered information based on date and time. If you are using this method, you need to add complex extraction logic to the source systems first.

What are the two libraries you would need to scrape website data on Python?

To extract data from web pages, some of the Popular Python Libraries to Perform Web Scraping include

lxml Library

It is another versatile Python library that deals with HTML and XML files. It is relatively fast and easy to use.

How to install it?

We can use the pip command to install lxml.

(base) D:\ProgramData>pip install lxml
Collecting lxml
   100% |¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦¦| 3.6MB 64kB/s
Installing collected packages: lxml
Successfully installed lxml-4.2.5

Beautiful Soup Library for Web Scraping

Let’s consider the case where you are looking to collect al the hyperlinks from any web page. In such cases, we can use Beautiful Soup Python library. It is mainly used to pull data out of HTML and XML files. You can use it with requests because it can’t fetch a web page on its own and needs an input to process.

How to install it?

We use the pip command to install beautiulsoup.

(base) D:\ProgramData>pip install bs4
Collecting bs4
Requirement already satisfied: beautifulsoup4 in d:\programdata\lib\sitepackages
(from bs4) (4.6.0)
Building wheels for collected packages: bs4
   Running bdist_wheel for bs4 ... done
   Stored in directory:
Successfully built bs4
Installing collected packages: bs4
Successfully installed bs4-0.0.1

Extracting Data with EOV

EmbarkingOnVoyage has been a successfully leading the data extraction field, with an adept knowledge in multilingual text analytics. So, if you would like to know how we can help you in extraction of required data, please feel free to get in touch with us at today!