AltaSQL Eliminates SQL Statement Scripts! Here a comparison between AltaSQL and GIT
Typical Usage Patterns
Situation | AltaSQL | Git |
|---|---|---|
Starting a new query | Developer imports metadata from Snowflake, AltaSQL auto-generates a SELECT with all columns (no SELECT *). Developer edits the ready-to-run statement (rename, reorder, add expressions). | Developer creates a new .sql file and hand-writes the SELECT statement. Often starts by copy-pasting an old query and tweaking it. |
Reusing logic | Build a new “View Definition” by copying an existing one—metadata drives new SELECTs. Names, comments, tags, and column logic are inherited automatically. | Copy-paste old SQL code into a new file, edit it. Consistency depends on discipline. |
Adding documentation / comments | Add comments, tags, or business-friendly names directly in the repository. They flow into every generated SELECT and CREATE VIEW. | Add comments inside the SQL file. Not enforced, often skipped. No central source of truth. |
Sharing queries with teammates | Share a Virtual View (a named, reusable SELECT). Teammates call it directly without re-copying code. | Push .sql file to repo. Teammates must clone repo and copy logic into their workflows. |
Debugging errors | Generated SQL is always properly quoted, qualified, and syntactically valid. Developer focuses on logic, not syntax. | Debugging often means chasing down typos, missing commas, or inconsistent column naming. |
Maintaining large libraries of queries | Queries are modular, metadata-driven, and easily reused. Developers can assemble SELECTs like building blocks. | Queries grow as many files with overlapping, inconsistent logic. Maintenance means manual review and refactoring. |
Patterns in short
So in practice:
#AltaSQL #Snowflake #SQLAutomation #DataProductivity #MetadataManagement
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