Database Engineer (Multi-SQL)

To detect dangerous DDL operations in SQL migrations, you can scan .sql files to identify destructive statements like DROP and TRUNCATE, along with unsafe ALTER operations. This process surfaces safety issues across dialects before deployment.

Dialect-aware SQL review applies specific checks for different database engines like T-SQL and PostgreSQL. It analyzes data types such as VARCHAR or SERIAL and provides indexing recommendations to improve database performance and reliability.

You can check Spark scripts for SQL performance anti-patterns by applying dialect-aware static analysis to .py files. This surfaces data-type issues and safety concerns within your Spark SQL logic, generating a structured remediation report.

Yes, you can review PostgreSQL and T-SQL migrations in the same scan because the tool applies dialect-aware checks across multiple SQL dialects. It identifies unsafe operations and anti-patterns regardless of the specific database engine.

You generate a database migration review report by running a scan against your migration set. This produces a standardized database_review.md file at the designated reports directory, detailing performance findings and remediation guidance.

Common SQL data type anti-patterns include misusing VARCHAR or MAX lengths and improper SERIAL implementations. Dialect-aware scanning identifies these specific issues across SQL files to prevent performance degradation and reliability problems.