Data Wrangling vs. ETL

When it comes to choosing a data preparation process, your choice could mean the difference between leading the pack or falling behind your competitors. 

With so much on the line, how do you choose between data wrangling and ETL? 

With 2.5 quintillion bytes of data created every day, finding the right process that harnesses information specific to your needs is largely dependent on the size, scope, and structure of your business. If your business is top heavy with analytic but tech-driven minds, then data wrangling may be the perfect choice. If your 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. 

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Understanding data wrangling and ETL

In order for a business to make an informed choice about which data preparation process is right for them, let’s take a look at both processes, and how they work.

In particular, 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 is completed by hand including reviewing, cleaning, formatting, testing, and distribution. It is best used in cases that 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, due to the propensity for human error. 
  • Semi-automated: Adding code-based tools and stored procedures, the data wrangling process becomes quicker because it allows for data profiling—a process which includes trend analysis, calculations and queries, and recurring tasks—to be performed more regularly and easily. 
  • Fully automated:  Repetitive and complex data wrangling requires early analysis, design, and development. Once in place using 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 burden off of analysts through automation.

On the other side of the coin, ETL can be used within a data wrangling process or by itself. Typically, ETL follows a standard process involving: 

  • Extract: Preparing data for analytics by copying data from a source
  • Transform: Transforming data into a format that matches its intended destination
  • Load: Loading data into a destination such as a data store, data mart, or data warehouse to be used by IT to create analytical reports

Data wrangling and ETL are alike in their purpose of transforming data for analytics, however, the similarities stop there. 

Data wrangling vs ETL: A comparison

The specific differences between data wrangling and ETL can be categorized into three areas: the end-users of the analytics, the types of data entering the system, and each process’s use-case.

 Data WranglingETL
UsersBusiness users: executives, managers, and analystsIT professionals
Data StructureDiverse, complex dataStructured, map-based data
Use Case ExploratoryReporting 

IT vs. business users 

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

On the other hand, IT professionals are the end-users of ETL. It is the ideal process for funneling business requests and creating data workflows specific to analytic results. With an ETL process in place, IT users are able to deliver data analytics straight to a data warehouse for business users to use. 

Diverse vs. structured data

Data wrangling is designed specifically to manage diverse data from a variety of sources and levels using visualization, machine learning, and human-computer interactions. Data wrangling is continuously learning and improving upon itself—making it more efficient and accurate over time by adapting to trending changes or specific business environment. That 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.

Exploratory vs. reporting use cases

Data wrangling is often used for the exploratory process of discovering new or unique ways of looking at data. Smaller teams or departments faced with “Big Data” scenarios use it to consider all the possibilities available for specific analytics. In other words, data wrangling transforms the data into a format that allows users to find exactly what they are looking for from the data. 

ETL tools are frequently used for data mining, transmitting, and gathering data for a data warehouse. Once the data has been well-defined and structured, it can be stored in a data warehouse where it becomes available for analytics. 

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Data wrangling vs. ETL: Which suits your needs?

Consider your data. If you’re hoping to combine customer, social, marketing, and point of sales, or e-commerce sales data to produce business insights, it must be transformed into a single format to be used for queries and analytics. The biggest questions then are, who will be transforming the data, who will be reading it, and how will it be used? 

Which process you 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. Once the system is up and running however, managers, users, and analysts can use the tools easily by themselves — less IT work upfront and a back-end punctuated with self-service capabilities. ETL is managed by your IT department. They receive requests from business associates and implement work paths for data that accommodates the system and format needed by the end user.

Data wrangling vs. ETL: Preparing trusted data the right way

Whether it’s data wrangling, ETL, or a combination of both that your business needs to outperform competitors, Talend has the tools to get you up and running in a snap. 

Talend Data Fabric is a comprehensive suite of apps that excels in data preparation, integration, and integrity. By managing your entire data lifecycle and easily connecting to a range of data sources, Talend Data Fabric empowers you to identify real-time analytics that drive decisions — all at the speed of business. 

Harness the power of ETL and data wrangling. Try Talend Data Fabric today.

| Last Updated: September 3rd, 2019