Technology Basis

Patented SQL automation engine built on top of Snowflake, using secure UDFs and meta-data repositories​​

Natural language processing and large-scale machine learning models trained on vast corpora of code and text

How It Works

Uses structured meta-data (repositories, View Definitions) to generate SQL with consistent, repeatable transformations. No code execution – just SQL generation​​

Translates user prompts into SQL queries, often by learning patterns from training data. Executes within the AI environment or via plugins

Precision & Consistency

Extremely high – SQL is generated from well-defined, user-controlled meta-data. Output is always fully qualified, quoted, and editable​​

Varies – can make syntax or logic errors. Output may need human validation, especially with complex joins or edge cases

Customization

Meta-data-driven: naming conventions, data masking, tags, comments, transformations – all centrally managed​​

Customization relies on prompt clarity. Harder to enforce consistency across queries or projects

Error Reduction

Eliminates >90% of hand-crafted SQL issues like incorrect column lists or missing tags​

Reduces effort but not immune to hallucinations or misunderstood logic

Scalability

Built for large-scale SQL development: reuses View Definitions, supports plug-and-play SELECT, CREATE VIEW and CTAS creation, ideal for teams​​

Suited for quick, individual SQL generation; scaling for enterprise consistency is difficult without extensive prompt management

Governance & Auditability

SQL generation is transparent, auditable, and centrally managed via repositories​

Generated SQL is often ephemeral and not tied to a traceable process

Execution

AltaSQL never executes SQL – encourages “look before you execute” governance model​​

May execute SQL (if integrated into a tool), or requires user copy/paste for testing