Data Wrangling

What Is Data Wrangling?

Data Wrangling, often referred to 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 operations and used to prepare data for insights, predictive modeling, and analytics.

Functionality and Features

The main functionalities of Data Wrangling include data discovery, data structuring, cleaning, enriching, and validation. It allows data scientists to transform and map data from one "raw" data form into another format, enabling more convenient consumption of the data. Features often include the ability to discover, cleanse, transform, enrich, validate, and publish the data.

Benefits and Use Cases

With Data Wrangling, businesses can improve decision-making processes as it ensures data quality and reliability. It's particularly advantageous in handling big data, where large volumes of data need to be cleaned and validated for analysis. Use cases include CRM data cleaning, social media data structuring, and IoT data normalization.

Challenges and Limitations

Despite its advantages, Data Wrangling can be time-consuming, complex, and require advanced skills. Moreover, it can be difficult to maintain data consistency, and there may be a potential for data loss if performed incorrectly.

Integration with Data Lakehouse

In a data lakehouse environment, Data Wrangling helps in managing the diverse data types and structures that are stored. It enables the transformation of raw data into a suitable format for further analytics and business intelligence operations.

Security Aspects

Security is crucial in Data Wrangling, particularly in sensitive industries. Most data wrangling tools have built-in security features, including access control and data masking, to ensure data privacy and compliance with regulations.


Efficient Data Wrangling improves data analysis performance, promoting faster and more accurate business decisions. However, the performance can be impacted by the size and complexity of the data sets to be wrangled.


What is Data Wrangling? Data Wrangling is the process of cleaning, structuring, and enriching raw data into a format suitable for analysis.

Why is Data Wrangling important? It is essential for ensuring data quality, reliability, and consistency, which are fundamental for data analysis and decision-making processes.

What are the challenges of Data Wrangling? It can be time-consuming and complex, with the potential for data loss if performed incorrectly.

How does Data Wrangling relate to a data lakehouse? Within a data lakehouse, Data Wrangling helps in managing and transforming the diverse data types into a suitable format for analysis.

What are some common use cases for Data Wrangling? CRM data cleaning, social media data structuring, IoT data normalization are some of the common use cases.


Data Munging: Synonymous with Data Wrangling, it is the process of manually converting or mapping data from one "raw" form into another format.

Data Lakehouse: An emerging architecture combining the benefits of traditional data warehouses and data lakes.

Data Enrichment: The process of enhancing, refining, and improving raw data.

Data Validation: The process of ensuring that a program operates on clean, correct and useful data.

Data Discovery: The process of collecting data from various sources by detecting patterns and outliers with the help of guided advanced analytics.

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