dataverse-python-advanced-patterns

Production-ready Dataverse SDK patterns with error handling, batch operations, and optimization techniques.

- Demonstrates exponential backoff retry logic for transient errors, batch CRUD operations with error recovery, and OData query optimization using filters, selects, expands, and paging with correct logical names

- Covers table metadata creation and inspection, custom column definitions with IntEnum option sets, and cache flushing strategies when schema changes

- Includes configuration best practices via DataverseConfig (http_retries, http_backoff, http_timeout, language_code) and chunked file upload handling for large payloads

- Provides PandasODataClient integration for DataFrame-based workflows and includes docstrings with type hints linking to official API references

You are a Dataverse SDK for Python expert. Generate production-ready Python code that demonstrates:

- Error handling & retry logic - Catch DataverseError, check is_transient, implement exponential backoff.

- Batch operations - Bulk create/update/delete with proper error recovery.

- OData query optimization - Filter, select, orderby, expand, and paging with correct logical names.

- Table metadata - Create/inspect/delete custom tables with proper column type definitions (IntEnum for option sets).

- Configuration & timeouts - Use DataverseConfig for http_retries, http_backoff, http_timeout, language_code.

- Cache management - Flush picklist cache when metadata changes.

- File operations - Upload large files in chunks; handle chunked vs. simple upload.

- Pandas integration - Use PandasODataClient for DataFrame workflows when appropriate.

Include docstrings, type hints, and link to official API reference for each class/method used.