Data quality validation
Data quality validation is an important phase in data pipelines as it ensures the correctness of the data used in analyses. Without correct data, even if you use good analytical tools, the analytical insights will be incorrect. So, customers/developers need to focus more on the data quality phase to create accurate datasets for further analysis.
What is the difference between data quality and data cleansing? Some of us might be confused between data cleansing and data quality validation. In reality, there will be some overlap between the two phases, and some activities are used interchangeably:
- Data cleansing is the phase where we clean and deduplicate data and identify generic data issues, such as splitting data for more meaningful analysis, cleansing data errors, and so on. Without cleansing, the data might not be useful for analysis efforts. For example, in a student database and results table, the score column can have non-numeric values or missing...