Integrations
Translate, Validate, and Deliver
A three-stage AI-driven pipeline powered by Datachecks’ Synthetic Data Engine — translating logic, comparing results, and delivering fully validated code
Data Aligner
The Translation Agent starts with synthetic test data generated by Datachecks’ Synthetic Data Engine. This data mirrors your production environment — structure, relationships, and volume — without exposing PII. Safe, realistic data for instant validation.
Feedback Loop
Each query is translated, executed, and compared against baseline results. The agent iterates automatically until output parity is achieved. Every translation improves through continuous comparison.
Data Comparison
Validated SQL and procedures are delivered with data diff reports and confidence scores, ensuring every line behaves as expected. Clean, explainable, deployment-ready code.
Why It Matters?
The Translation Agent is designed to solve modernization challenges— automating the repetitive 80% of code migration while keeping humans in control of the critical 20%
Manual Rewrite
SQL engineers manually edit 1000s of lines
LLM + rule engine rewrites automatically
Code Quality
Breaks due to dialect mismatch
Semantic-aware, tested with synthetic data
Traceability
No audit trail
Versioned translation logs and diff views
Confidence
QA only at the end
Continuous translation + test loop
Metrics:
Manual Rewrite
Code Quality
Traceability
Confidence
Traditional:
SQL Engineers manually edit 1000's of lines
Breaks due to dialect mismatch
No audit trail
QA only at the end
Datachecks:
LLM + rule engine rewrites automatically
Semantic-aware, tested with synthetic data
Versioned translation logs and diff views
Continuous translation + test loop
Datachecks has undergone SOC 2 Type II attestation and undergoes annual audits.
Infrastructure that meets ISO standards.
%201.svg)
Deployed to your infrastructure, so no data leaves your network.
Every user action is audit trailed.
.svg){kind=icon}
Your questions, Our answers
How does the Translation Agent convert SQL or ETL logic between platforms?
It uses LLM-powered transformation models fine-tuned for SQL dialects. The agent rewrites queries, stored procedures, and ETL logic into the target platform syntax — Oracle to Databricks, SQL Server to Snowflake, etc.
How accurate is the translation output?
Translation accuracy is typically 80–95%, depending on the code complexity. The platform provides explainable transformations and lets engineers review or edit rewrites before deployment.
Can Datachecks handle procedural logic or complex stored procedures?
Yes. Beyond simple SQL, the agent supports PL/SQL, T-SQL, and Teradata BTEQ transformations, translating both procedural and declarative code with detailed audit logs.
Does it integrate with my existing CI/CD or DevOps pipeline?
Yes. Translation outputs can be exported as scripts or API-fed directly into dbt, Airflow, Jenkins, or Git-based workflows, keeping migrations aligned with your release process.
How do you ensure reliability after translation?
Each translation is version-controlled, validated syntactically, and passed automatically to the Validation Agent for cross-environment testing before deployment.
How much faster can we migrate using the Translation Agent?
Teams report up to 80% reduction in manual code rewrite time, accelerating database or warehouse migration timelines from months to weeks.
© 2025 Datachecks. All rights reserved.
