AI engineering glossary · Data engineering
What is data cleansing?
Data cleansing (also called Data cleaning): Detecting and fixing or removing errors and inconsistencies in data, such as typos, wrong formats, missing values and duplicates, to make it fit for use. It is usually a large share of pipeline effort.
Data cleansing: an example
Standardising 'Bengaluru', 'Bangalore' and 'BLR' into one city value and trimming spaces from names.
Related terms
More Data engineering terms
- Data contract
- Data federation
- Data freshness
- Data governance
- Data ingestion
- Data lake
- Data lakehouse
- Data lineage
Where you learn it
Data cleansing is part of the AI Engineering Program, which teaches it with notes, a hands-on lab and an exam. The first lesson is free.