Formal reply:
If you're dealing with inconsistent formats, I highly recommend leveraging schema validation tools like Pydantic or Cerberus in Python. They help enforce structure early in the data parsing pipeline.
For larger datasets, consider Apache Spark—it handles distributed processing well and has built-in functions for cleaning.
Validation is tricky, but statistical outlier detection (e.g., using PyOD) can flag anomalies before they cause issues.
If you're dealing with inconsistent formats, I highly recommend leveraging schema validation tools like Pydantic or Cerberus in Python. They help enforce structure early in the data parsing pipeline.
For larger datasets, consider Apache Spark—it handles distributed processing well and has built-in functions for cleaning.
Validation is tricky, but statistical outlier detection (e.g., using PyOD) can flag anomalies before they cause issues.