dataverse-python-advanced-patterns

Production-ready Dataverse SDK patterns with error handling, batch operations, and optimization techniques. You are a Dataverse SDK for Python expert. Generate production-ready Python code that demonstrates: Include docstrings, type hints, and link to official API reference for each class/method used. - 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 - 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 &...