Data wrangling vs. data cleansing vs. ETL vs. ELT: Understanding key differences

With the growing complexity of analytics use cases, businesses are scrambling to derive actionable insights from unprecedented volumes of data pouring in from multiple sources. As the cloud landscape evolves rapidly, IT teams are under immense pressure to modernize their data management infrastructure and simplify time-consuming processes. As a result, there is a pressing need to deliver clean, accurate, and complete data at lightning speed for multiple analytics use cases.

Data preparation remains a major focus area, as it lays the foundation for advanced analytics. However, within data preparation, terms like data cleansing and data wrangling are often used interchangeably due to certain similarities. Some of the steps involved in these processes also overlap with ETL (extract, transform, load), leading to further confusion. Let’s take a closer look at the differences in these three processes and understand how each can help you maximize the potential of your data.