Wrangling Process

What is Wrangling Process?

Data wrangling, also known as data munging, is the process of cleaning, structuring and enriching raw data into a desired format for better decision making in less time. Data wrangling is increasingly ubiquitous in data science, particularly due to the advent of big data and machine learning tools that necessitate the conversion of raw data into an understandable format for machines.

Functionality and Features

Data wrangling tools and processes typically involve several stages, such as:

  • Discovering: Identifying what data is present.
  • Structuring: Organizing the data into a form appropriate for use.
  • Cleaning: Removing errors, discrepancies and correcting inaccuracies in the data.
  • Enriching: Enhancing the data by combining it with other data.
  • Validating: Deploying statistical checks and rules to ensure data integrity.

Benefits and Use Cases

Data wrangling allows businesses to make better decisions by enabling data-driven insights. By transforming and enhancing raw data into a more usable format, analysts can uncover actionable insights more quickly.

Use cases of data wrangling span across industries. In finance, for example, it can be used to create reliable financial models. In healthcare, it can streamline patient data to provide more efficient care.

Challenges and Limitations

A primary challenge in data wrangling is the time demanded in cleaning data. Given the vast volumes of unstructured data, it's estimated that data scientists spend approximately 80% of their time on this stage. Furthermore, the complexity of data, a lack of understanding about the tools, and a scarcity of skilled staff are major roadblocks in data wrangling.

Integration with Data Lakehouse

Data wrangling plays a crucial role in a data lakehouse environment. A lakehouse architecture combines the features of traditional data warehouses (structured data) and data lakes (unstructured data), providing the flexibility, cost efficiency, and performance that modern analytics needs.

In this context, data wrangling helps in preparing and transforming data for various use cases, such as reporting, machine learning, and advanced analytics. It also significantly enables the transition from a traditional data warehouse environment to a lakehouse setup.

Security Aspects

Data wrangling platforms must adhere to strict security measures to ensure the privacy and integrity of data. This includes access control, data masking, and secure data transport. In the context of a data lakehouse, data can also be secured using techniques such as encryption, tokenization, and pseudonymization.


The performance of the wrangling process depends on the complexity of the data and the tools used. Employing efficient tools and following best practices can significantly speed up the wrangling process.


1. What are some popular data wrangling tools? There are several data wrangling tools available, including Dremio, Trifacta, and OpenRefine.

2. Can data wrangling replace traditional data cleansing processes? While data wrangling does involve cleaning data, it's often more comprehensive, including steps for enriching and transforming data. It's an enhancement rather than a replacement of data cleansing processes.

3. How does data wrangling support machine learning? Machine learning models are only as strong as their inputs. By preparing and structuring data into a format that these models can learn from, data wrangling is a critical step in the machine learning workflow.

4. How long does the data wrangling process take? The duration of the wrangling process largely depends on the volume and complexity of the data, and the tools used. It's estimated that data scientists spend 60-80% of their time on data wrangling.

5. What is the relationship between data wrangling and a data lakehouse? Data wrangling helps in preparing and transforming data for various use cases in a lakehouse, easing the transition from a traditional data warehouse setup to a lakehouse setting.


Data Munging: Another term for data wrangling, emphasizing on converting or mapping data from one raw form into another format.

Data Lake: A large storage repository that holds a vast amount of raw data in its native format until it's needed.

Data Warehouse: A system used for reporting and data analysis, primarily consisting of structured data.

Data Lakehouse: A hybrid data management platform combining the features of data warehouses and data lakes.

Machine Learning: An application of artificial intelligence where systems learn and improve from experience without being explicitly programmed.

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