As noted above, data cleaning is a distinct step within the data wrangling process. It involves identifying and rectifying inconsistencies, errors, and inaccuracies in a dataset to ensure accuracy and reliability. Data wrangling is a broader process that encompasses data cleaning and goes beyond to tasks such as reshaping, merging, and enriching data to make it suitable for analysis.

Data wrangling and ETL (extract, transform and load) are related but distinct processes of data management. While data wrangling focuses on cleaning and structuring data for analysis, ETL is a formalized process involving preparing data from multiple sources for storage in a target location, usually a data warehouse. ETL typically occurs on a much larger scale and is usually a much more automated process, using scheduled, batch-oriented workflows for data integration.

In DataOps, the process begins with gathering data from multiple sources, typically in raw, unstructured, or incomplete formats. Data wrangling fits in as a pre-analysis step that prepares this raw data for further use by transforming it into a clean, structured, and usable format.

A major focus of DataOps is maintaining data integrity, which specifically aligns with the cleaning and validation steps of the data wrangling process.

Data wrangling helps to facilitate the collaboration sought by DataOps by allowing business users and analysts to quickly access actionable insights from data.

In DataOps, data wrangling processes are often automated as part of CI/CD pipelines, which ensure new data is continuously cleaned, structured, and validated in near real-time before being passed downstream for analysis.

Data wrangling plays a role in compliance within DataOps by ensuring that PII data is handled correctly to comply with privacy laws like HIPAA and GDPR.

Data wrangling fits with DataOps by ensuring that data is “fit for purpose,” or ready-to-use as efficiently as possible when it reaches data scientists and analysts.

DataOps emphasizes continuous monitoring of data pipelines to ensure data remains consistent and high-quality. Data wrangling processes—especially those that are automated—can be monitored to identify quality issues or bottlenecks, allowing for continuous improvement and adjustments based on real-time stats.

Data wrangling provides the data consistency needed for DataOps to scale operations across larger and more complex datasets without compromising performance.