Value proposition
DQ that functional analysts can actually use.
Rigid DQ platforms require engineers for every change. Ad-hoc spreadsheets don't scale. NeptunoDQ closes that gap: audited lifecycle, Spark execution, language that functional analysts actually understand.
The problem
Data quality is still a communication problem.
The technology is there. What's missing is a bridge between the person who knows what to validate and the person who knows how to implement it.
Rules trapped in JIRA
The functional analyst knows exactly what to validate, but cannot express it in the platform. The rule gets written in a ticket, goes through an engineer, and by the time it reaches production nobody remembers the original context.
No real traceability
Spreadsheets and ad-hoc SQL queries leave no audited trail. When a check fails in production, there's no way to know who approved it, when it was last changed, or why that threshold was chosen.
Fragmented multi-engine
Spark locally, a notebook on Databricks, a query on Snowflake. Each team maintains its own in-house framework. Maintenance cost scales with the number of engines, not the number of rules.
The solution
How NeptunoDQ solves it.
A complete lifecycle: from the proposal in functional language to the audited execution on Spark or Databricks.
Proposals in functional language
The analyst proposes the rule in business terms. The engineer implements it in SQL or a notebook. The platform manages the full cycle: proposal, review, approval, and deployment.
Audited review and hotfix
Every change is recorded with author, timestamp, and reason. Urgent hotfixes have their own workflow, but they still leave a trace. Audit is not optional — it's the design.
DAG with Fair Scheduler
Rules execute respecting dependencies. If a parent rule fails, child rules are skipped automatically. Independent branches run in parallel via Spark Fair Scheduler.
True multi-engine (Spark + Databricks)
A single configuration format — JSON or YAML — runs on Apache Spark open source and Databricks. Same inventory, same lifecycle, different deployment target.
Comparison
Versus the usual alternatives.
Ad-hoc SQL is fast but brittle. Heavy DQ platforms are powerful but slow. NeptunoDQ fills the gap neither covers.
| Criterion | Ad-hoc SQL | Heavy DQ platform | NeptunoDQ |
|---|---|---|---|
| Functional learning curve | Medium | High | Low |
| Full traceability | No | Partial | Yes |
| Native multi-engine | No | Limited | Yes |
| Speed to production | Fast | Slow | Fast |
| Maintenance cost | High | High | Low |
Who uses it
A different kind of value for every role.
NeptunoDQ is not an engineering tool in disguise. It's designed for functional analysts, data engineers, and platform teams to work in the same system without stepping on each other.
Functional Analyst · Audit
Define what to validate in business language. Submit proposals, review changes, and sign off approvals with full traceability.
Data Engineer
Implement rules in SQL or notebooks, configure variables and thresholds, integrate with your existing pipeline on Spark or Databricks.
Platform Team
Deploy a single quality engine across multiple compute engines. No lock-in. No in-house framework to maintain.
Ready to see it in action.
Explore the interactive demos and see how NeptunoDQ fits your team.