Your SQL conversion is green. Does that mean the report is correct?
Imagine moving a familiar business report to a new analytics system. An AI-powered tool converts the old SQL, and the migration dashboard marks it green.
That is welcome news. But two questions have different answers: Did the converter complete the migration task? And does the converted query return the same answer as the original?
1. What the green status means
Databricks documents Agentic Code Converter support for source dialects including T-SQL, Snowflake SQL, Redshift SQL, Oracle, BigQuery and Teradata. The SQL target framework became generally available on October 15, 2026; the notebook target is subject to Beta requirements.
Its file-level status labels are:
- Green — converted successfully
- Yellow — needs review
- Red — conversion failed
- Gray — in progress
The converter does not simply copy strings. It analyzes scripts, validates output and retries parts that fail. Nevertheless, the manufacturer's testing recommendations explicitly include running the converted SQL and comparing original versus converted results on equivalent test data.
2. A simple fictional comparison
Consider three imaginary orders of ₩100,000, ₩200,000 and ₩300,000. An original report would show three rows totaling ₩600,000.
Now imagine a proposed converted report accidentally returning only the first two orders. It might execute without a syntax error and still show the wrong result: two rows totaling ₩300,000.
This is not an observed bug or failure rate of Databricks. It is an invented fixture that explains the difference between “code runs” and “data answers match.”
3. Three checks before accepting a migrated report
Check 1: Execution. Run the converted query in the target Databricks environment. Confirm whether it executes without errors.
Check 2: Equivalent outputs. With comparable test inputs, compare row counts, totals, groups, missing records, duplicates and NULL handling between source and target.
Check 3: Edge conditions. Examine boundary dates, time zones, dialect-specific behavior, grouped aggregates and other cases that might change business meaning.
If the old and new systems do not use equivalent inputs, mark the comparison as not yet conclusive. Save the source query, converted revision, test data version and observed comparison result.
4. Keep the tool's actual limits in view
Databricks documents a 300-file maximum per conversion batch and says Teradata BTEQ scripts are not supported. The notebook target has separate Beta prerequisites. For a practical starting point, the vendor recommends trying a small batch first, then reviewing both the generated code and real results.
5. Frequently asked questions
Does green prove my data matches? No. Green is the converter's file-level conversion status; semantic results still warrant comparison.
Doesn't the converter validate its output? It does perform automated checks, but Databricks still recommends execution and comparison on equivalent test data.
Did you find a real failure in Databricks? No. This article does not run Databricks and makes no claim about observed customer errors, accuracy rates, performance or cost.
Takeaway: The useful question is not just whether SQL was successfully converted. It is whether both versions answer the same business question with the same data.
Sources and example disclosure
Official Databricks Agentic Code Converter guide (updated October 16, 2026) and October 2026 product release notes, listed in the official references below. All order values and the mismatch are fictional, not a real conversion test.
Two terms made simple
Related published article
Read the existing validation guide