You can use the BigQuery Batch SQL Translator which is an excellent choice for converting your SQL DDLs to BigQuery, particularly in light of the challenges you've faced with your Python script. This tool offers several advantages tailored to your needs:
Suitability:
- Specifically Designed for SQL DDL Conversion: The Batch SQL Translator is purpose-built for this task, ensuring more accurate and efficient translation compared to a general-purpose Python script.
- Support for Various SQL Dialects: It can handle different dialects such as Oracle, MySQL, SQL Server, etc. However, it's important to check the specific dialects and versions supported, as there may be variations in compatibility.
Effectiveness:
- Bulk Conversion Capability: The tool can translate multiple DDL files at once, offering a time-efficient solution compared to manual conversions.
- Configurable Translations: You can tailor translation rules, target data types, and schema modifications using configuration files for greater precision.
Ease of Use:
- Simple Setup and Automated Process: Uploading DDL files to Cloud Storage and running the translator via command or API simplifies the process. It reduces manual mapping and conversion errors, though some manual review might still be necessary for complex SQL constructs.
Considerations:
- Learning Curve: Familiarity with Google Cloud services is beneficial, as there might be a learning curve for new users.
- Limited Control in Complex Scenarios: For highly complex or non-standard DDL modifications, additional scripting might be required.
- Integration with Google Cloud Services: Seamless integration with other Google Cloud services can be a significant advantage for those already using the platform.
- Performance and Optimization: Post-translation, it's crucial to evaluate the performance and optimization of the SQL for BigQuery.
- Cost Implications: Be aware of any potential costs associated with using the Batch SQL Translator, especially for large-scale translations.
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