processingcleaning
Processingcleaning refers to the practice of embedding data cleaning activities within the data processing stage of a workflow, with the goal of improving data quality as data is ingested, transformed, and prepared for analysis. Rather than treating cleaning as a separate post-processing step, processingcleaning aims to detect and address quality issues early in the pipeline, producing more reliable downstream results.
Techniques commonly involved include validation checks, handling missing values, deduplication, outlier treatment, normalization and standardization, data
In practice, processingcleaning is implemented in ETL/ELT pipelines or streaming processing architectures. Cleaning steps may be
Tools and methods span programming libraries for data manipulation, workflow automation, and, in some cases, dedicated
Benefits of processingcleaning include higher data quality, reduced remediation effort, and more reliable analytics. Potential drawbacks
The term is not universally standardized and may be used variably across organizations. It relates to data