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Data Wrangling and ETL

When it comes to selecting a data preparation process the choice will mean the difference between leading the pack or falling behind our competitors. With so much on the line, we choose between data wrangling and ETL.

With all the 2.5 quintillion bytes of data created every day, find the right process that harnesses information specific to your needs that are largely dependent on the size, scope, and structure of our business. If our business is top, heavy with analytic but tech-driven minds then the data wrangling may be a perfect mode. If the IT department is forward-thinking, savvy, and on point to provide timely findings to business executives and users, then ETL could be the way to go.

Understanding Data Wrangling and ETL:

For a business to be informed, a choice about that data preparation process is right for them. Data wrangling is the process of cleaning, parsing, and proofing data. The process can be formatted in three ways.

  • Manual- This method requires that everything will be completed by hand including reviewing, cleaning, formatting, testing, and distribution. It will be used in cases that will require one–time analysis or when reviewing a design for an ongoing analytics project. Manual tasks are tedious, time-consuming, and not necessarily the most effective process that is due to the propensity for human error.
  • Semi-automated- Adding code-based tools and also stored procedures, the data wrangling process becomes quicker because it allows for data profiling- a process that includes trend analysis, calculations, and queries, and also recurring tasks that are to be performed more regularly and easily.
  • Fully automated- All the repetitive and complex requires analysis, design, and development. Once the place used an enterprise data warehouse and automated ETL workflows, this data wrangling method practically runs itself. Reusable ETL processes continuously run on a schedule, enlisting regular data loads and taking some of the burdens off of analysts through automation.

ETL can be used within a data wrangling process or by itself. ETL follows a standard process involving:

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  • Extract- Preparing data for analytics by copying data from a source.
  • Transform- Transform data into an intended destination format.
  • Load- Loading data into destinations such as a store, data mart or data warehouse to be used by IT to create analytical reports.

IT vs. business users:

Data wrangling for end users will be business executives, managers, and analysts. IT must design, engineer, and develop the data wrangling process on the front end once it will be set up, business users experience a user-friendly, simple self-service functionality.

IT professionals will be end users of ETL. It is the process of funneling business requests and creating data workflows specific to analytic results. With the ETL process in place, IT users will provide data analytics to a data warehouse for business users to use.

Diverse vs structured data

Data wrangling is designed specifically to manage diverse data from many sources and levels by using visualization, machine learning, and also human-computer interactions. The Data wrangling will be continuously learning and improving upon itself making it more efficient and accurate over time by adapting to trending changes or specific business environments. It means more timely and effective business intelligence for users.

ETL lends itself to more structured, map-based data that has already been organized within a database or operational system, like a data warehouse.

Data wrangling vs ETL which suits your needs?

The data as we are hoping to combine customer, social, marketing, point of sales, or e-commerce sales data to produce business insights, must be transformed into a single format to be used for queries and analytics. The process we choose may be as simple as asking the question ”how tech-savvy are my business executives”? Data wrangling requires intense preparation in design, engineering, and development. ETL is managed by your IT department.


  1. Explain between Data wrangling and ETL.
  2. Which will suit the needs?
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