Understanding the Difference between ETL and Data Wrangling

Over the period of past few years, the process of data wrangling, also known as “the preparation of the data,” has appeared as a swiftly developing domain within the analytics industry. Once an analysis bottleneck because of time-consuming and laborious work required to prepare different data sources for analysis and reporting, the technologies associated with data wrangling have come a long way.

Being in the data wrangling business, a question that is commonly asked by the clients is that, “what is the difference between ETL (Extract, Transform and Load) and data wrangling?” Considering the way characteristics of both of the technological domains are similar in term of functionality, it is not surprising that people often get confused. There is a need to more clearly define the differences between the ETL and data wrangling.

To provide the market with a clear understanding of the differentiation between the ETL and data wrangling, here are the top three major dissimilarities between the two technologies:

The Users of Both Technologies Are Different

 

The basic concept of the data wrangling technologies is that the individuals who have the best understanding of the data, explores and prepares that data. This means that among others, business managers, a line of business users, and the business analysts are the proposed users of the tools meant for data wrangling. There has been a large amount of labor put into the task of designing, engineering and development of a product that empowers business executives to innately perform the preparation of data all by themselves.

On the other hand, the ETL technologies have been focused on IT as its end users. Employees of the IT department gets demands from their business associates and establish workflows with the help of tools designed for ETL technologies to deliver the required data to the systems in the supported formats.

While working with the data, business users hardly view any advantage for the ETL technologies. Prior to the availability of the tools for data wrangling, these interactions of the users with the data would only take place in business intelligence tools or spreadsheets.

The Data in Data Wrangling is Different than Data in ETL

 

The growth of software solution for data wrangling happened because of its need. The ever-increasing types of different data sources at present can be explored; however, data analysts did not have the appropriate tools to comprehend, clean, and consolidate the data in the required format. Most the data, which business analysts need to handle these days, comes in an extensive range of forms and volumes that can be either too complicated or extremely large in size to manage using the customary self-service tools such as Excel. This is why solutions for data wrangling are exclusively planned and architected to deal with varied sources of complicated data at different levels.

On the other hand, ETL has been designed to manage data, which is usually already well organized, mostly sourcing from a wide range of databases or operational systems against which the organization wishes to report. Complicated sources in their original state or comprehensive data, which demands considerable data mining and derivation to assemble, are one of the fundamental capabilities of tools used in ETL technologies.

In addition to that, an increasing level of analysis happens in settings where the representation of the data is not clear or is identified ahead of time. This means that the data analyst performing the task of data wrangling is actually anticipating the way data can be leveraged for exploration as well as the presentation needed to carry out that data exploration.

The Data Wrangling Use Cases Are Different From the ETL Use Cases

 

The used cases that have been observed among the users of solution for data wrangling tends to be more empirical in nature and are mostly carried out by the small sized teams or departments before they are spread throughout the organization. The users of technologies used in the domain of data wrangling basically attempt to perform with a fresh source of data or a unique combination of different sources of data for an analytics initiative. It has also been observed that solution for data wrangling tend to make prevalent analytics procedures extra resourceful and precise as the users can have their concentration of the data all the time during the process of data preparation.

On the other hand, the technologies related to ETL mainly achieved popularity during the era of the 70s as the tools used in ETL technologies are particularly designed for mining, transmitting, and stocking the data into a consolidated data warehouse of the organization mostly for further analysis and reporting through other business intelligence applications. This remains to be the major use case for the tool uses in ETL technologies and this is what they have been exclusively capable of.

With some of the clients in the data analytics business, the ETL and data wrangling solutions are organized as the balancing rudiments of the data platform of their organization. While IT leverage ETL tools to transport and handle data, business users are provided with the access to explore and prepare the suitable data with the assistance of solutions designed for the process of data wrangling.