standardize automated data input for inconsistent datasets

We're working with datasets that have inconsistent formatting and structure, making automated processing challenging. The data comes from multiple sources with varying field names, data types, and organizational patterns that need to be normalized before we can run our standard analysis workflows.

Our main requirements include:

• Detecting and mapping inconsistent field names to standardized schema

• Converting data types automatically (dates, numbers, text formatting)

• Handling missing or null values consistently across all datasets

• Maintaining data integrity during the transformation process

We need this to work with both batch processing for historical data and real-time processing for incoming data streams. The standardization should be configurable so we can adjust rules as new data sources are added to our system.

The end goal is having all incoming data conform to our internal data model before it reaches our analysis pipeline, eliminating the manual cleanup work our team currently does.