What is Data Extraction? Everything You Need to Know

• February 10th, 2021

FI - Data Extraction

Data is increasingly becoming the lifeblood of the digital economy and as most companies transit into online businesses, the importance of data is increasing rapidly. For data to be useful, it has to be collected and transformed into a form suitable for analysis.

The first step of taking advantage of data for business growth through analytics and Business Intelligence applications involves data gathering. In this article, you will be introduced to the concept of “Data Extraction,” which is the first component of the Extract, Transform, Load (ETL) paradigm used in most data workflows.

Data Extraction sets up the rest of the pipeline for success as it is the most time-consuming portion of any data science or analytics project. Setting up a Data Extraction process requires various considerations such as the sources of data, the method of extraction, and the reliability of the data extracted through this routine. Simply put, a well-thought-out and implemented Data Extraction process can make the rest of the data pipeline more efficient and result-oriented.

Table of Contents

What is Data Extraction

what is Data Extraction
Image Source: https://www.alooma.com/blog/what-is-data-extraction

Data Extraction can be defined as the process of collecting data from various sources for the purpose of storing that data, transforming it, and feeding it to another system for subsequent analysis. Data Extraction is also known as data collection as it involves gathering data from different sources such as web pages, emails, flat files, Relational Database Management System (RDBMS), documents, Portable Document Format (PDFs), scanned text, etc. The sources through which this data is extracted may be structured or unstructured. 

With structured data, the data adheres to a specific form or schema, for example, a database table with clearly defined columns of a particular data type and values contained in rows. In contrast, unstructured data does not conform to any definite structure. As a result, it can be more tedious to extract data from unstructured sources such as free-form text, images, web pages, etc.  

Nowadays, data is also being retrieved from recording/measuring devices like sensors and the Internet of Things (IoT) devices. All of this means that Data Extraction is now required at a cross-section of input sources, some of them at the edge of computing. Therefore, it is essential that any Data Extraction routine be both robust and capable of delivering consistent data to the next layer of the data pipeline toolchain.

What is The Need for Data Extraction

The importance of Data Extraction cannot be ignored as it is an integral part of the data workflow that transforms raw data into competitive insights that can have a real bearing on a company’s bottom line. Any successful data project first has to get the data portion of the project right as inaccurate or faulty data can only lead to inaccurate results regardless of how well-designed the data modeling techniques may be. 

The process of Data Extraction generally shapes raw data that may be scattered and clumsy into a more useful, definite form that can be used for further processing. Data Extraction opens up analytics and Business Intelligence tools to new sources of data through which information could be gleaned. 

For example, without Data Extraction, data from web pages, social media feeds, video content, etc., will be inaccessible for further analysis. In today’s interconnected world, the data derived from online sources can be used to gain a competitive advantage through sentiment analysis, gauging user preferences, churn analysis, etc. Therefore, it means that any serious data operation has to fine-tune the Data Extraction component to maximize the chances of a favorable outcome.

Simplify ETL using Hevo’s No-code Data Pipeline

Hevo is a No-code Data Pipeline that offers a fully managed solution to set up data integration from 100+ data sources and will let you directly load data to your data warehouse. It will automate your data flow in minutes without writing any line of code. Its fault-tolerant architecture makes sure that your data is secure and consistent. Hevo provides you with a truly efficient and fully-automated solution to manage data in real-time and always have analysis-ready data.

Get Started with Hevo for Free

Let’s look at some Salient Features of Hevo:

  • Fully Managed: It requires no management and maintenance as Hevo is a fully automated platform.
  • Data Transformation: It provides a simple interface to perfect, modify, and enrich the data you want to transfer. 
  • Real-Time: Hevo offers real-time data migration. So, your data is always ready for analysis.
  • Schema Management: Hevo can automatically detect the schema of the incoming data and maps it to the destination schema.
  • Live Monitoring: Advanced monitoring gives you a one-stop view to watch all the activities that occur within pipelines.
  • Live Support: Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
Sign up here for a 14-Day Free Trial!

What is Data Extraction ETL

data extraction ETL
Image Source

To put the significance of data extraction into perspective, consider the ETL process as a whole. In essence, ETL enables businesses and organizations to 1) aggregate data from several sources into a single location and 2) integrate various types of data into a common format. The ETL procedure is divided into three steps:

  • What is Data Extraction: Extraction is the process of extracting data from one or more sources or systems. The extraction process finds and identifies useful data before processing or transforming it. Extraction allows a variety of data types to be merged and mined for business intelligence.
  • What is Data Extraction Transform: After the data has been correctly extracted, it is now ready for refinement. Data is sorted, structured, and sanitized during the transformation step. Duplicate entries will be removed, missing information will be removed or supplemented, and audits will be conducted in order to provide data that is reliable, consistent, and useable.
  • What is Data Extraction Loading: For storage and analysis, the converted, high-quality data is supplied to a single, unified target location.

What is Data Extraction Types

In this section of the article, you will be introduced to the types of Data Extraction. Emphasis will be laid on Full Extraction and Incremental Extraction, and at the end of this section, you will have not only a deeper understanding of each type of Data Extraction technique, but you will be able to clearly differentiate between them and know which is more suitable for your business and data requirements.

The two types of Data Extraction are:

1) What is Data Extraction: Full Extraction

In Full Extraction, the data is extracted entirely from the source without recourse to any logic or existing conditions in the source system. The data is extracted as is and then exported. There are no checks performed on variables like when the last extraction occurred as each extraction is independent and is a complete download of the current state of the data. 

An example of Full Extraction is a SQL database dump of a table. As can be observed from the example, Full Extraction does not require complex logic to be initiated; however, the load on the system may be high if the data that is being extracted is significantly large. 

Full Extraction should be used when you do not want to track changes that may have occurred since the time of the last extraction but all you require is complete access to your data.

2) What is Data Extraction: Incremental Extraction

In this extraction technique, changes to data are tracked, and only the changed data from the point of the last extraction is extracted and loaded (migrated) into a new system like a data warehouse. 

In Incremental Extraction, only the relevant data regarding a metric like the timestamp of the last successful extraction is acted on. This means that the logic for such extraction will be more complex, however, the load on the source system is greatly reduced. The reduced load can lead to more efficient processes, especially when the extracted data is fed to a data warehouse as the next stage of the data pipeline will also have a reduced workload to process. 

Some implementations of Incremental Extraction use Change Data Capture (CDC) to ascertain what data has changed since the last extraction whereas some data warehouse tools rather import the whole data and compare it to the previous version of imported data to determine what has changed.

More information on how you can implement Change Data Capture can be found here.

What is Data Extraction: Use Cases

This section explores further by looking at how the implementation and automation of Data Extraction can lead to benefits in many scenarios.

Domino’s Problem

Domino’s is the world’s largest pizza company, thanks in part to its ability to accept orders via a variety of technology, including smartphones, watches, televisions, and even social media. All of these channels generate massive amounts of data, which Domino’s must combine to get insight into its global operations and customer preferences.

Domino’s employs a data management platform to handle their data from extraction to integration in order to consolidate all of these data sources. This system, which runs on Domino’s own cloud-native servers, collects data from point of sale systems, 26 supply chain centers, and other mediums such as text messages, Twitter, Amazon Echo, and even the US Postal Service. Their data management technology then cleans, enhances, and stores data so that multiple teams may simply access and use it.

What is Data Extraction: Improving Employee Productivity

Imagine a small business that deals with PDFs and scanned documents. Having a way to automate and extract valuable data from those documents into a Business Intelligence application for the purposes of indexing and searching that data can be a productivity boost. Also, mining the extracted data can lead to suggestions for improvement in processes, which ultimately streamlines wastage and increases output. The alternative approach, which is having an employee manually type in that data is less efficient with the possibility of introducing errors. Productivity is also reduced with such an approach as the employee performs repetitive, drudgery based tasks instead of using their time to complete more meaningful tasks that generate growth for the business. 

With Data Extraction, techniques like Optical Character Recognition (OCR) can be used to extract text from documents and the data could even be fed into a Natural Language Processing (NLP) system for further processing. Businesses that are innovative can use Data Extraction to improve employee productivity and standardize data processes.

What is the Data Extraction Future

The introduction of cloud storage and computing has had a significant impact on how businesses and organizations manage their data. The cloud has made the ETL process more efficient and versatile than ever before, in addition to advancements in data protection, storage, and processing. Without having to maintain their own servers or data infrastructure, businesses can now access data from all over the world and process it in real-time. More firms are beginning to transfer data away from traditional on-site systems by utilizing hybrid and cloud-native data choices.

The data landscape is likewise being transformed by the Internet of Things (IoT). Wearables like FitBit, autos, household appliances, and even medical equipment are increasingly producing data in addition to cell phones, tablets, and PCs. Once the data has been retrieved and converted, the result is an ever-increasing volume of data that may be leveraged to drive a company’s competitive advantage.

What is Data Extraction: Conclusion

In this article, you were introduced to the concept of Data Extraction and why it’s needed. You were also given an overview of the types of Data Extraction and the key differences between Full Extraction and Incremental Extraction. Finally, you were walked through a hypothetical example of a scenario in which integrating Data Extraction can improve the business process of a company. 

Visit our Website to Explore Hevo

Data Extraction is a vast field as the amount of data being produced is increasing exponentially. Various tools in the market seek to address the challenges presented by Data Extraction. One of such tools is Hevo, which allows you to extract data from 100+ data sources, transform it into a form suitable for analysis and connect it to your data warehouse or directly to a Business Intelligence tool of your choice to perform the required analysis.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand. You can also have a look at our unbeatable pricing that will help you choose the right plan for your business needs!

Share your experience of learning about Data Extraction! Let us know in the comments section below!

No-code Data Pipeline For Your Data Warehouse