In today’s data-driven era, you have more raw data than ever before. However, to leverage the power of big data, you need to convert raw data into valuable insights for informed decision-making. When it comes to preparing data for analysis, you will always come across the terms “data wrangling” and “ETL.” While they may sound similar, data wrangling and ETL are distinct yet closely related processes that play a crucial role in interpreting data. 

In this guide, we will explore data wrangling vs ETL in detail, including their definitions, distinctions, and how to choose between them.

What is Data Wrangling?

Data wrangling involves the process of cleansing, transforming, and preparing data. It converts raw data into a usable format suitable for analysis. Data wrangling includes extracting data from various sources, handling missing data, standardizing data formats, and correcting errors.

Data wrangling plays an important role in data analysis, as it ensures data quality and integrity, making it suitable for further analysis and insights. Effective data wrangling is essential to derive meaningful insights and make informed decisions from data. The six main steps in data wrangling are:

  • Data Discovery: Understand and explore the data to gain insights into its structure, content, and quality.
  • Data Structuring: Organize and structure the data into a format suitable for analysis, including formatting, normalization, and integration.
  • Data Cleaning: Identify and correct errors, inconsistencies, and inaccuracies in the data such as missing data, duplicate data, and outliers.
  • Data Enriching: Enhance the data with additional information, such as appending external data or deriving new features.
  • Data Validating: Check the data against predefined rules, business logic, or statistical measures to ensure its quality and accuracy.
  • Data Publishing: Share the wrangled data in a suitable format for analysis, including visualization, reporting, and documentation.

What is ETL?

ETL stands for Extract, Transform, Load and refers to extracting, standardizing, and loading data from diverse sources into a target system for analysis. It is a critical process in data integration and plays a key role in data management and analytics. The three main steps in ETL are:

  • Extract: In this step, data is extracted from various sources, such as databases, files, APIs, or external systems. The data may be in different formats, such as structured data (e.g., relational databases) or unstructured data (e.g., text files), and may come from multiple sources. 
  • Transform: After data is extracted, it needs to be transformed into a common format or structure to ensure consistency and accuracy. This step involves data cleansing, validation, enrichment, aggregation, and other data manipulation activities to standardize and prepare the data for analysis.
  • Load: Once the data is transformed, it is loaded into the target system or database. This step involves inserting, updating, or merging the data into the target system, often a data warehouse or a data mart.
Data Wrangling vs ETL : ETL Process
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Data Wrangling vs ETL: Similarities

Data wrangling and ETL have several similarities and understanding these similarities can help you choose the appropriate approach for data preparation needs.

Involve Data Transformation

Data wrangling and ETL both involve data transformation to prepare data for analysis. Data wrangling deals with cleaning, restructuring, and enriching data in order to enhance its usability. On the other hand, ETL involves extracting data from multiple sources, transforming it into a suitable format, and loading it into a data warehouse. Both methods focus on preparing data for further processing and analysis.

Aim to Improve Data Quality

Data quality is a crucial aspect of data preparation. Poor-quality data can lead to inaccurate insights and flawed decision-making. Data wrangling and ETL aim to improve data quality by detecting and correcting errors, removing duplicates, and filling in missing values. By ensuring that data is clean and consistent, analysts and data scientists can trust the results of their analyses.

Data Wrangling vs ETL: Differences

Data wrangling and ETL are distinct but related processes that involve preparing and managing data for analysis. While they share similarities, they also have differences in terms of users, data structure, and use cases. Let’s dive into ETL vs data wrangling.

Data Wrangling vs ETL: Users

ETL is typically implemented by data engineers who are responsible for managing and optimizing data workflows across different systems. With ETL, data engineers focus on extracting, transforming, and loading data into data warehouses. This data is then consumed with business intelligence tools or by data analysts for generating insights.

On the other hand, data wrangling is typically performed by data analysts or data scientists who work closely with the data on a day-to-day basis. Such data professionals are responsible for exploring, cleaning, and transforming data to meet their specific project requirements. Data used for data wrangling can come from a data lake or a data warehouse.

Data Wrangling vs ETL: Data

The data involved in data wrangling can come from various sources. This data can be structured, semi-structured, or unstructured and may include data types such as text, numbers, dates, images, or audio. Such data is used with data wrangling steps to obtain quality data for training machine learning or deep learning models. 

While ETL can handle semi-structured or unstructured data to an extent, its main focus is on processing structured data. This data may include transactional, customer, financial, or other operational data. 

Data Wrangling vs ETL: Use Cases

Data wrangling is used for exploratory analysis, helping small teams to answer ad-hoc queries and discover new patterns and trends in big data. Ad-hoc data wrangling means dealing with data in a flexible and customized way as per the needs of the specific situation, without following any fixed procedures. Data wrangling is often used in scenarios where quick data manipulation is necessary to answer data-driven questions in real-time.

In contrast, ETL is a systematic process used to extract and transform enterprise data at regular intervals, ensuring that it is ready for analytics and reporting in a data warehouse. It is typically used for large-scale reporting and analytics and is an important component of good data management practices.

Data Wrangling vs ETL: Machine Learning 

When it comes to preparing data for ML, Data wrangling is typically more suitable than ETL. This is because ML algorithms require clean, pre-processed data ready for analysis. Data wrangling focuses on ensuring the data is accurate and consistent, which is critical for building effective ML models. Data wrangling can also involve feature engineering, which is the process of creating new features from existing data to improve ML models’ accuracy.

On the other hand, ETL is more focused on moving and transforming large amounts of data, which may not be ideal for ML. ETL can still be useful for preparing data for ML. But it may require additional Data wrangling steps to ensure that the data is ready for analysis.

Additionally, data wrangling processes are commonly used for working with unstructured or semi-structured data, such as text, images, and audio. This makes them well-suited for use in machine learning applications that rely on these types of data. In contrast, ETL processes are typically designed to work with structured data in databases and data warehouses.

Data Wrangling vs ETL: Flexibility

ETL is more rigid and designed to be a repeatable process. ETL processes are typically designed to follow predefined rules and workflows for extracting, transforming, and loading data. ETL workflows are less adaptable to changes in data sources or transformation requirements, often requiring extensive modifications.

On the other hand, data wrangling is known for its flexibility, it allows analysts to work with data more flexibly and iteratively. The Data wrangling process offers a wide range of functions that can be customized to meet specific data transformation needs. Analysts can easily manipulate and transform data, test their assumptions, and refine their workflows until they get the desired results. This flexibility enables analysts to be more creative and agile in their data processing tasks, as they are not bound by predefined rules and workflows.

Data Wrangling vs ETL: Which Approach is Best for You?

Both data wrangling and ETL can be employed independently in various scenarios or even combined. For instance, data wrangling can be employed after ETL to guarantee the quality and consistency of the data improved for specific machine learning use cases.

Here are some scenarios where Data wrangling is commonly used:

  • If you need to clean, transform, and prepare data for analysis in an ad-hoc manner, data wrangling may be more suitable.
  • Data wrangling can be advantageous in handling huge volumes of unstructured or semi-structured data, including text data, social media posts, or sensor data.
  • Data wrangling can be useful for data exploration and discovery tasks. It allows you to quickly explore and manipulate data to gain insights and make real-time data-driven decisions.

Here are some scenarios where ETL is commonly used:

  • ETL is often employed in data integration, migration, and consolidation scenarios, where data from various sources needs to be transformed and loaded into a target system.
  • ETL is commonly used when proper data management and governance practices are required. As a result, it is popular among regulated industries or when dealing with sensitive data.
  • If you need to perform large-scale reporting and analytics at regular intervals, then ETL is recommended.

The choice between data wrangling and ETL largely depends on the nature of your data and your specific needs. Data wrangling is typically best suited for smaller, less complex datasets that require cleaning and transformation before analysis. On the other hand, ETL is better suited for larger datasets that need to be integrated from multiple sources, transformed to fit a target schema, and loaded into a data warehouse for analysis.

Comparison FactorData wrangling ETL
UsersData Analysts, Data ScientistsData Engineers
DataDeals with diverse data such as unstructured and semi-structured data.Primarily deals with Structured data. 
Use CasesExploratory analysis, ad-hoc data manipulation.Large-scale reporting and analytics
Machine LearningIt’s suitable for Machine learning tasks.It can be useful but may require additional data wrangling steps.
FlexibilityMore flexible and iterative, offers customization for specific data transformation needs.Rigid and repeatable, follows predefined rules and workflows, less adaptable to changes.


Understanding the difference between data wrangling and ETL is essential in choosing the right approach for your data workflows. ETL offers a structured and scalable approach for large-scale data processing. Data wrangling should be used for better flexibility and agility in handling diverse data sources. 

The choice between data wrangling and ETL depends on factors such as the nature of the data, user requirements, data management practices, and processing needs. Careful consideration of these factors will help you decide on the best approach for your data integration tasks.

In case you want to integrate data into your desired Database/destination, then Hevo Data is the right choice for you! It will help simplify the ETL and management process of both the data sources and the data destinations. 

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Offering 150+ plug-and-play integrations and saving countless hours of manual data cleaning & standardizing, Hevo Data also offers in-built pre-load data transformations that get it done in minutes via a simple drag-and-drop interface or your custom python scripts. 

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Freelance Technical Content Writer, Hevo Data

Amulya combines her passion for data science with her interest in writing on various topics on data, software architecture, and integration through her problem-solving approach.

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