We have a data format but we can't restrict our users to enter any particular format. How should we design our code to handle this situation?

Designing for flexible input requires a layered approach. First, store raw data in a data lake or raw bucket so you never lose the original format. Second, use a format‑agnostic ingestion layer that detects the file type (CSV, JSON, XML, Parquet, etc.) and dispatches to the appropriate parser via a Factory or Strategy pattern. Third, validate the parsed data against a generic schema or a schema registry (e.g., Avro, Protobuf) that allows optional fields and versioning. Fourth, transform the data into a canonical internal representation (e.g., a domain model or a unified JSON schema) before downstream processing. Fifth, provide clear error messages and fallback defaults for missing or malformed fields. Tools like Great Expectations or Schematron can enforce data quality. Finally, document the accepted fields and provide a sample payload so users know how to format their data. This architecture keeps the system robust while allowing users to submit data in any format.