If you already have a clear picture of what data quality management is and which KPIs you want to track, the next step is choosing what tool to implement it with. The problem is that "data quality tool" isn't a single category: there are validation engines, observability platforms, catalogs with a built-in quality module, and testing frameworks that live inside your orchestrator. Choosing wrong means paying for an enterprise platform when four rules in dbt would have sufficed, or the opposite — trying to scale rules in notebooks when you already have 200 critical tables.
What a data quality tool needs to solve
Before comparing names, it's worth fixing which functions are non-negotiable. Any serious tool should cover, at minimum: defining rules or expectations about the data, automatically running those rules in the pipeline, alerting when something fails, and a history that lets you see trend, not just the latest result. From there, the differences lie in whether it detects anomalies on its own, whether it maps the error's lineage back to its source, and whether a non-technical person can read the result without opening a notebook.
Comparison of the main options
| Tool | Type | Best for | Learning curve |
|---|---|---|---|
| Great Expectations | Open source, declarative rule-based | Data teams with engineering capacity who want full control and no vendor dependency | Medium-high: requires manual pipeline integration |
| Soda (Core / Cloud) | Open source + SaaS layer | Teams that want to start fast with SQL-like checks and scale into business collaboration | Low for basic rules, medium for advanced checks |
| Monte Carlo | Data observability (SaaS) | Organizations that prioritize automatic anomaly detection over manually defined rules, focused on production incidents | Low: much of it is automatic, but cost rises with the volume of monitored tables |
| Collibra DQ | Enterprise platform integrated with a catalog | Companies already using Collibra for governance who want quality connected to the same catalog and lineage | High: typically a project-scale rollout, not self-service |
| dbt tests / dbt-expectations | Native to the transformation pipeline | Teams already working in dbt who want quality tests as part of the model itself, without an external tool | Low if you already use dbt, none if you don't |
Open source vs SaaS: the real decision
The question isn't "which is better" but "who is going to maintain it." An open source tool like Great Expectations has no license cost, but requires someone on your team to spend time maintaining the integration, updating versions, and fixing things when a deployment breaks. A SaaS platform like Monte Carlo or Collibra DQ shifts that maintenance to the vendor, but introduces a recurring cost that scales with data volume and, in some cases, dependency on their roadmap for features you need now.
How to choose based on your maturity level
- Early stage (no formal rules): start with basic checks built into your transformation layer (dbt tests) before buying anything. The goal is to build the habit, not find the perfect platform.
- Growth stage (scattered rules, no centralized visibility): Soda or Great Expectations give you a common language to define expectations and a dashboard where the business can see status without asking you for a report.
- Scale stage (dozens of sources, recurring production incidents): this is where automatic observability (Monte Carlo and similar) starts to justify its cost, because it catches what nobody wrote as a rule.
- Regulated stage (AI Act, high-risk systems): you need traceability between the rule, the result, and the decision made. This is where catalog- and lineage-connected platforms, like Collibra DQ, make it easier to produce the evidence an auditor will ask for.
Checklist before you buy
- Does the tool integrate with your current stack (orchestrator, warehouse, catalog), or does it require rewriting pipelines?
- Can someone outside the technical team read the result without asking for help?
- Is the results history exportable for audit purposes, not just visible on a dashboard that expires?
- Does cost scale with data volume, number of rules, or users? Ask for the exact pricing model before signing.
No tool replaces having a clear picture of which quality dimensions matter to you and what threshold is acceptable for each one. If you haven't defined that yet, start there: check out the 6 dimensions of data quality before evaluating platforms.