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.
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