Organizations now have more information at their fingertips than ever before — and, in many cases, more information than they know what to do with. So how can you mine this massive, highly complex data to produce the keen data-driven insights you need for smarter business decision-making?

The answer lies in careful, systematic data preparation and cleansing. If you've already done some research into crunching your enterprise data, you might have come across the terms "data wrangling" and "ETL." Yet although ETL and data wrangling are closely related concepts, they aren't quite the same thing.

So then what is the difference between data wrangling and ETL, exactly? This all-in-one guide to ETL and data wrangling covers everything you need to know, including definitions, points of distinction, and how to choose between the two. 

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Table of contents

What is Data Wrangling?

Simply put, data wrangling (also known as data munging) is the act of extracting raw data and converting it into a more workable format. It's rarely the case that the data you have is already in the required format for you to perform data analysis and reporting. Thus, data wrangling is usually a necessary initial step in the business intelligence (BI) and data analytics pipeline.

Data scientists may perform data wrangling manually, or they may use automation with data wrangling tools such as Trifacta and Amazon SageMaker Data Wrangler. When performed manually, data wrangling can be extremely tedious and time-consuming. According to some estimates, data science workers spend as much as 80 percent of their time on data wrangling, and just 20 percent on modeling and analysis.

What is ETL?

ETL (extract, transform, load) is a systematic process for data integration that works with structured data. The three steps of the ETL process are as follows:

1. The process extracts data from one or more raw data sources (e.g. SQL databases, Excel spreadsheets, flat files, websites, SaaS applications, etc.).

2. The extracted data transforms in order to match the schema of the target location and to remove inaccurate, out-of-date, and duplicate information.

3. Finally, the transformed data loads into the target location, usually a data warehouse or data lake.

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Data Wrangling vs. ETL: 3 Crucial Differences

Data wrangling and ETL both seek to transform raw data for use in analytics, but here are three important points of distinction:

1. Types of Users

The end-users of data wrangling and ETL software are usually different:

Business users such as analysts and managers are most likely to use data wrangling tools to query and explore data. As such, many data wrangling software applications feature self-service functionality for non-technical users.

IT professionals whose job it is to construct ETL pipelines and ensure data proceeds smoothly from source to target are more likely to use ETL tools. Given business requirements about enterprise data, users of ETL tools build ETL workflows to make certain reliable access to this data is available for people who need it.

2. Types of Data

The data itself usually differs between ETL and data wrangling:

ETL works best with structured data (e.g. data from SQL relational databases). Working with unstructured data is more difficult. Because the data is inherently unstructured, it's hard to define formal, automated processes that can transform this data into a structured output.

Data wrangling can work with a wider variety of raw input data, largely thanks to the manual effort required. Data wrangling software is usually more agile and flexible than ETL software. Many data wrangling tools take advantage of advances in machine learning, artificial intelligence, visualization, and human-computer interaction.

3. Use Cases

Last but not least, the use cases of data wrangling and ETL also differ:

Data wrangling is typically more exploratory in nature, helping small teams answer ad hoc queries. Data wrangling can help uncover new connections and trends in big data.

ETL is a systematic process that extracts and transforms your enterprise data at regular intervals, ensuring that it is ready for analytics and reporting inside your data warehouse. An efficient, consistent ETL process is part of good data management.

Given these differences between ETL and data wrangling, what can we say about when to use them? Of course, it's not an either/or answer. You can use both data wrangling and ETL in different situations and even in combination, as best suits your business needs.

Data wrangling might be better if you have many nontechnical users, if you want to explore data-driven questions on the fly, or if you're working with large amounts of unstructured data.

ETL might be better if you have mostly structured data, if you want to establish a formal data management and data governance program, or if you perform large-scale reporting and analytics at regular intervals.

How Xplenty Can Help with Data Wrangling and ETL

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Whether you decide to go with data wrangling or ETL, you need the help of a powerful, trustworthy tool. Xplenty is a feature-rich yet user-friendly platform for ETL and data integration. With a simple drag-and-drop interface and more than 100 pre-built connectors to third-party services, Xplenty makes it easy to launch sophisticated ETL pipelines to your cloud data warehouse.

Want to find out how Xplenty can help with your ETL needs? Get in touch with our team of data integration experts today for a chat about your business goals or to start your 14-day pilot of the Xplenty platform.