Tablif Town/Ariftly/Problem page
Problem page

How to move legacy Oracle SQL to Snowflake with reviewable checks

Your company still runs important logic in old Oracle SQL: stored procedures, views and jobs that nobody fully remembers writing. The data team wants to move to Snowflake, but every estimate turns into a long rewrite project with a lot of risk. Converting the code is only half of it; you also have to prove that the new version gives the same answers as the old one. Until you can show that, nobody wants to sign off on switching.

2 min readWritten from ariftly.ioWelcome page from Tablif · 23 Oct 2026
The short answer

Ariftly is building Ariftly Forge, software that records how a workflow happens today, redesigns repeatable steps around AI and trusted tools, and runs the result as a controlled operation. Its first domain focus is AI-native modernization, with Oracle SQL to Snowflake as the example flow, built around reviewable conversion and explicit proof of behavior.

On this page
  1. Why the usual way fails
  2. How Ariftly helps
  3. Step by step
  4. Tips
  5. Questions

01Why the usual way fails

Legacy SQL is full of dialect-specific features and hidden dependencies between objects, so a line-by-line translation often breaks in subtle ways. Automatic converters can produce code quickly, but they rarely show why a change was made or whether the result behaves the same. The decisions about edge cases usually live in people's heads, not in documents. Without clear owners and acceptance checks, migrations drag on or get rolled back after the first surprise.

02How Ariftly helps

Ariftly is building Ariftly Forge, software that records how a workflow happens today, redesigns repeatable steps around AI and trusted tools, and runs the result as a controlled operation. Its first domain focus is AI-native modernization, with Oracle SQL to Snowflake as the example flow, built around reviewable conversion and explicit proof of behavior. Work starts by inventorying source SQL and its dependencies, capturing decision points, and agreeing a scoped boundary with a named owner. The company describes the product as being shaped, and its walkthrough is illustrative, not a live run.

“Move legacy logic into a modern platform with reviewable conversion and explicit proof of behavior.”
From ariftly.io
“Objects, dependencies and dialect-specific constructs”
From ariftly.io

03Step by step

  1. Book a discovery call and describe the SQL estate, the systems it touches and what a verified result means for you.
  2. Inventory the source objects, dependencies and dialect-specific constructs.
  3. Capture the review rules, exceptions and acceptance criteria with the people who own them.
  4. Run a pilot on one bounded workflow against representative cases, with human review visible.
  5. Decide on production deployment based on the recorded evidence of what passed and what needs another approach.

04Tips

  • Start with one bounded slice instead of the whole database. Pick a set of procedures with a clear owner and clear outputs, and prove that slice first.
  • Agree on what counts as correct before converting anything. Write the acceptance criteria and comparison inputs up front, so the result can be checked rather than argued about.
  • Keep human sign-off on every release. Automated conversion speeds up the work, but an owner should approve before anything replaces a live system.
  • Ask every vendor to make tool, model, hosting and support costs explicit in the proposal. Hidden running costs are a common surprise after a migration goes live.

05Questions

Which other workflows does Ariftly cover?

Besides modernization, it points to data and platform migration, integration workflows such as EDI onboarding, and custom enterprise workflows.

Source: ariftly.io
What shapes the price?

Workflow complexity, systems involved, deployment environment, validation needs, data volume, human review and security constraints.

Source: ariftly.io
Does the workflow improve over time?

It learns from outcomes, exceptions and human corrections, and changes are reviewed before reaching production.

Source: ariftly.io

Transforms complex enterprise work into controlled, AI-operated workflows with validation and human approval built in.