ETL and Data Wrangling: What Makes Both Non-identical?

What is ETL?

ETL is nothing but a traditional extract, transform, and load process, it fetches data from numerous RDBMS source systems, transforms it (such as applying concatenations, calculations, etc.) and loads it into the Data Warehouse system.

It requires a complex ETL process and is not a simple extraction of data from many sources and loading into a Data warehouse database. This process involves active inputs from several stakeholders including the analysts, testers, top executives, developers and is challenging technically. MarkLogic, Oracle and Amazon RedShift are few of the prominent ETL tools currently available in the market.

Benefits of Extract, Transform and Load (ETL):

• Transforms the data from various sources and loading it into several targets
• Provides you a deep historical context for better businesses
• It allows the organizations to report and analyze data easily and more efficiently
• It evolves and adapts to the changes in technology and the integration guidelines
• Increases productivity, it moves data quickly without requiring the technical skills or coding at the beginning.

What is Data Wrangling?

Data wrangling or data preparation is a rapidly growing space in the analytics industry from over a few years. Data wrangling technology has come a long way from the painful and time-consuming work of preparing different data sources for analysis and reporting. It is nothing but the process of unifying and cleaning complex and messy data sets to make it easy to access and analysis.

This rapid growth and expansion of data sources and data, it has become very essential to organize the large amounts of data for analysis. Typically, this process includes mapping/converting data manually from a raw form into other formats to allow comfortable organization and consumption of the data.

Trifacta is a vendor selling data wrangling software. Often the partners, analysts, and clients of Trifacta ask what differs Data Wrangling from ETL because both the technologies overlap in functionality.

Benefits of Data Wrangling:

• It releases a deeper intelligence within the data by collecting data from numerous sources
• Provides actionable, accurate data to the business analysts with a timely matter.
• Reduces the time utilizedfor organizing and collecting the disordered data before it can be used
• Enables analysts and data scientists to focus on the analysis of the data, instead of data wrangling
• Drives better decision-making skills by the senior employees of the organization.

Differentiating ETL and Data Wrangling: 

Here are 3 major dissimilarities between the two technologies which give us a proper understanding.

1. Both technologies have different users 

Mainly, the idea behind the data wrangling solutions is that the professionals preparing and exploring the data should know their data at its best. This simply means that the intended users of these tools are line-of-business users, business analysts, and managers (among others). It takes loads of time to design and efforts to develop a product which enables the people to work by themselves intuitively.

Comparatively, ETL data integration technologies focus on information technology as the customers or end users. Employees of IT receive or fetch requirements from the business counterparts and implements workflows or pipelines using the ETL tools so that they provide the required data in the desired format into the systems.

The business users infrequently leverage or see ETL technologies while working with the data. Earlier, before the invention of data wrangling solutions/tools, the users would interact with the data only in the spreadsheets or the business intelligence tools.

2. Both the technologies use different data

Rise of the data wrangling solution software was a necessity due to the immense growth of data. Now we can analyze all the variety of growing data sources but there were no appropriate tools to clean, organize and understand this data in a proper format. The business data analyst need to deal with the growing variety (of shapes and sizes) which are too complex or too large for working with the self-service traditional tools like Excel. This data wrangling technology is specifically designed and architected to handle complex and diverse data to any extent.

Well, ETL is constructed to handle the well-structured data that originates from several databases and operational systems the company plans to report against. The raw, complex data or the large-scale data which requires substantial derivation and extraction to structure is not the strengths of ETL tools.

Further, environments in which the schema of their data is undefined or is unknown ahead of time, a large amount of analysis definitely occurs, which means the analysts doing wrangling determines as to how this data can be influential for analysis and the schema needed to perform this analysis.

3. Use Cases of both the technologies are different

Use cases among the users of the data wrangling solutions are more exploratory in character and are usually held by small departments and teams just before being presented across the company. For analytics initiative, the users of data wrangling technologies often work with new combinations of data sources and new data sources. It is seen that the data wrangling solutions make the existing analytics process more accurate and efficient because the users keep the eyes always on their data as it is being prepared.

In the 1970s, the ETL technologies gained popularity as these tools mainly extract, transform, and load the data in a centralized enterprise data warehouse for better analysis and reporting with the business intelligence applications. This is the primary use case for the ETL tools also they are immensely good at it.

In a few customers, it is seen that ETL and data warehouse solutions are deployed as a complementary element of a company’s data platform. IT influences the ETL tools to manage and move the data so that the business users get access to prepare and explore appropriate data via data wrangling solutions.

Conclusion

ETL and Data Wrangling are often confused, they have certain similarities such as they both are used for sorting of data. Although, both work very different, Extract, Transform and Load (ETL) is a tool used to fetch data from one database and place it into other databases, whereas data wrangling transforms and maps data from "raw" form into another format for appropriate use.