Data teams move fastest when analysts can contribute directly to pipelines without compromising quality or depending heavily on engineering support.
That’s the workflow we’re enabling with Zingle.
Data Engineers connect their production pipeline codebase once and define the rules and guidelines that every change must follow. Analysts then describe the output they need, and Zingle generates the complete plan: lineage, model structures, SQL files across all layers, and relevant data-quality tests.
Zingle enforces all project requirements, estimates cost and runtime, and automatically prepares a pull request with the full diff, metadata, downstream impact, and merge-safety indicators.
Analysts stay productive. Engineers stay in control.
And pipelines stay reliable.
If you're exploring how AI can safely accelerate your data engineering workflows, talk to us at Zingle AI Lab.
UvrajSB•1mo ago
Does this support only support dbt projects?
adadu2•1mo ago
No, it works for data transformations written in Spark/Scala as well.
adadu2•1mo ago
Data teams move fastest when analysts can contribute directly to pipelines without compromising quality or depending heavily on engineering support. That’s the workflow we’re enabling with Zingle.
Data Engineers connect their production pipeline codebase once and define the rules and guidelines that every change must follow. Analysts then describe the output they need, and Zingle generates the complete plan: lineage, model structures, SQL files across all layers, and relevant data-quality tests.
Zingle enforces all project requirements, estimates cost and runtime, and automatically prepares a pull request with the full diff, metadata, downstream impact, and merge-safety indicators.
Analysts stay productive. Engineers stay in control. And pipelines stay reliable.
If you're exploring how AI can safely accelerate your data engineering workflows, talk to us at Zingle AI Lab.