What is Zingle?
Zingle is an AI-powered data platform that helps analytics and data engineering teams build production-grade, governed data pipelines without writing boilerplate SQL from scratch.
You describe what you need in plain language — or paste existing SQL — and Zingle generates schemas, transformations, data quality tests, and semantic layer definitions. Every change ships as a GitHub pull request, so nothing touches production without your team's review.
Why Zingle exists
Most data teams face the same bottleneck: turning business requirements into clean, tested, version-controlled data models takes too long. Analysts wait on engineers. Engineers spend time on repetitive scaffolding. Governance gets bolted on after the fact.
Zingle closes that gap by combining:
- AI-assisted modeling — generate dbt-style SQL, schemas, and tests from natural language descriptions
- End-to-end governance — lineage tracking, layer enforcement, PII controls, approval workflows, and budget management
- Git-native delivery — all artifacts land in your GitHub repo as reviewable pull requests
- Warehouse-aware optimization — right-size Snowflake compute, consolidate duplicate models, and track cost savings
Platform at a glance
| Surface | What it does |
|---|---|
| Data Pipelines | Create and manage AI-assisted data models with lineage, tests, and scheduling |
| Playground | Explore data with natural language, run queries, and visualize lineage |
| Semantic Layer | Define and manage metrics, dimensions, and entities in YAML |
| Compute Optimization | Analyze pipeline costs and apply compute right-sizing suggestions |
| Data Model Optimization | Detect duplicate logic across models and merge via PR |
| Data Ingestion | Configure source-to-destination ingestion pipelines |
| Connections | Manage GitHub, Snowflake, and Airflow integrations |
| Approval Console | Review and approve pipeline and semantic layer changes |
| Access Management | Control user group permissions, budgets, and PII access |
| Settings | Configure workspace YAML, modeling guidelines, and AI behavior |
How it works — end to end
Connect your infrastructure
Link your GitHub repo, Snowflake warehouse, and optionally Airflow from the Connections page. Zingle validates every credential before saving.
Describe what you need
Open Data Pipelines and click Create. Describe a table in plain English, paste SQL, or upload a notebook. Zingle proposes schemas for intermediate and gold layer tables.
Review and refine
Use the visual lineage canvas and split SQL editor to review generated models. Accept or modify schemas, SQL, tests, compute engine, and schedule.
Ship via pull request
When every quality gate passes, click Review changes and raise PR. Zingle creates a branch, commits all artifacts (SQL, YAML, tests), and opens a PR in your GitHub repo.
Monitor and optimize
Use the Compute Optimization and Data Model Optimization surfaces to continuously reduce cost and eliminate redundancy across your pipeline fleet.
Architecture
┌─────────────────────────────────────────────────────┐
│ Zingle Platform │
│ │
│ ┌─────────┐ ┌──────────┐ ┌──────────────────┐ │
│ │ Next.js │ │ FastAPI │ │ PostgreSQL │ │
│ │ Frontend │──│ Backend │──│ (metadata store) │ │
│ └─────────┘ └──────────┘ └──────────────────┘ │
│ │ │ │
│ │ ├── GitHub API (repos, PRs) │
│ │ ├── Snowflake API (validation) │
│ │ └── Airflow API (orchestration) │
└─────────────────────────────────────────────────────┘
- Frontend: Next.js application with a dark-themed UI (Obsidian Alpine design system)
- Backend: FastAPI with SQLAlchemy ORM, handling auth, connections, and PR workflows
- Storage: PostgreSQL for workspace configuration, connection metadata, and audit trails
- Integrations: Direct API integration with GitHub, Snowflake, and Apache Airflow
Next steps
- Getting started — set up your workspace in under 10 minutes
- Data Pipelines — build your first AI-assisted model
- Integrations — connect GitHub, Snowflake, and Airflow