You've invested in Microsoft Fabric. Your lakehouse is humming. Your pipelines move data. But here's the question that keeps surfacing in architecture reviews: is Fabric's native data quality tooling enough, or do you need something else alongside it?

It's a fair question, and one without a simple answer. Microsoft has made significant strides in data quality through Purview Unified Catalog over the past year. At the same time, specialized tools like Great Expectations, Informatica, Soda, Ataccama, and dbt continue to push the boundaries of what data quality means in production environments. The right approach depends on where your organization sits on the spectrum between governance-first oversight and engineering-first validation.

This article walks through what Purview delivers today, where it falls short, and how the alternatives compare across capability, cost, and integration.

Where Microsoft Purview Stands Today

Data quality in Fabric doesn't live inside Fabric itself. It lives in Microsoft Purview Unified Catalog; a separate service that connects to Fabric workloads through managed identity authentication. This is an important architectural distinction. Purview is a governance tool that includes data quality features, not a dedicated data quality engine embedded in your pipeline.

That said, the capabilities have matured considerably through 2025 and early 2026. Purview now supports AI-powered data profiling that recommends columns for analysis, no-code and low-code rule creation across six quality dimensions (completeness, consistency, conformity, accuracy, freshness, and uniqueness), and multi-level scoring that aggregates results from individual rules up through data assets, data products, and governance domains. Custom SQL expression rules reached general availability in March 2026. Incremental scanning entered preview in February 2026, giving teams the option to scan only new or modified data rather than running full scans every cycle. Error record publishing; also GA as of February 2026; lets data engineers review and correct quality failures directly.

These are meaningful additions. For organizations that primarily need governance-level quality visibility across their Fabric estate, Purview provides a coherent framework. You define rules, scan assets, generate scores, and monitor trends through configurable alerts. It integrates natively with OneLake, Power BI, and the broader Microsoft security stack.

Where the Gaps Show

The limitations become clear when you move from governance oversight to operational data engineering.

First, Purview's data quality lifecycle is a multi-step process. Before you can profile a single column, you need to configure steward permissions, register data sources in the data map, create data products, set up connections, and then run scans. This setup overhead is manageable for a small number of governed assets, but it compounds quickly as your environment grows.

Second, there is no built-in data quality testing within Fabric's transformation workloads. Unlike dbt, where you can define and run quality checks as part of your transformation pipeline, Fabric separates quality management from data engineering. Purview scans happen outside of and after your pipelines execute. This means quality issues are detected downstream rather than caught at the point of transformation.

Third, cost predictability is a real concern. Purview bills through Data Governance Processing Units (DGPUs) on a pay-as-you-go basis: roughly $15 per DGPU for the Basic SKU, $60 for Standard, and $240 for Advanced. One DGPU equals 60 minutes of managed compute. On top of that, governed assets in the Unified Catalog cost approximately $0.50 per asset per month. Community users have reported that even a small proof-of-concept, 19 assets, incurred noticeable DGPU charges, raising legitimate concerns about how costs scale when you're governing hundreds of tables across multiple databases.

Finally, Purview measures data quality. It does not fix it. There is no data cleansing, no standardization, no master data management built into the platform.

The Competitive Landscape: What Other Tools Offer

Each alternative occupies a different niche. Understanding those niches helps you figure out which tools complement Purview, which ones replace parts of it, and which ones serve an entirely different use case.

Great Expectations: Pipeline-Native Validation

Great Expectations (GX) is the most widely adopted open-source data quality testing framework. Its core value proposition is simple: treat data quality expectations as code, version-control them, and run them inside your pipelines.

The 2025 release of ExpectAI introduced automated expectation generation; the system analyzes dataset patterns and recommends validation rules, reducing the time spent writing them manually. GX supports an extensive library of built-in expectation types, custom expectations for domain-specific logic, and row-condition filtering that applies checks only to specific subsets of data.

Because GX is Python-native, it integrates directly with orchestration tools like Airflow, Dagster, and Prefect. Expectations fit naturally into CI/CD workflows. If your engineering team already operates in a code-first, version-controlled environment, GX requires minimal behavioral change.

Where GX falls short: it doesn't provide data profiling, cleansing, observability, or governance scoring. It validates data; it doesn't manage it.

Informatica IDMC: Enterprise-Grade Quality at Enterprise-Grade Complexity

Informatica Data Quality, part of the Intelligent Data Management Cloud, sits at the other end of the spectrum. It offers comprehensive profiling, cleansing, standardization, and master data management in a single platform. The CLAIRE AI engine auto-generates quality rules, and a new Data Quality Agent; introduced in 2025; converts natural language business specifications into executable validation rules.

Informatica supports multi-cloud environments, consumption-based pricing, and integrated observability alongside data quality. For large enterprises with mature governance programs and complex data landscapes, it provides coverage that Purview cannot match.

The trade-off is real, though. Informatica is known for high implementation complexity, steep learning curves, and significant professional services requirements. Organizations with smaller data teams or tighter budgets often find that the platform demands more resources than they can justify.

Ataccama ONE: Unified Quality, Governance, and MDM

Ataccama takes a platform approach that combines data quality, governance, and master data management in a single architecture. Unlike some competitors assembled through acquisitions, Ataccama built its platform from scratch; which typically translates to a more cohesive user experience.

Its AI-driven automation includes anomaly detection, automated remediation, and rule suggestions. Ataccama supports business glossaries, lineage tracking, and end-to-end data profiling through delivery. For organizations that want quality, governance, and MDM under one roof without stitching together multiple vendors, Ataccama is a strong contender. It's best suited to organizations with mature data operations that can take advantage of the full platform.

Soda: Engineering-Friendly, AI-Native Quality

Soda approaches data quality with a clear engineering orientation. Its declarative language, SodaCL, lets teams define quality expectations in a readable, Git-native format. SodaGPT generates these checks from natural language descriptions.

What stands out is Soda's anomaly detection performance. The company reports that its proprietary algorithms deliver 70% fewer false positives compared to Facebook Prophet across curated benchmark datasets; and can process 1.3 billion records in roughly 64 seconds. For data engineering teams in regulated industries where false alarms are expensive and pipeline-embedded validation is a requirement, Soda addresses a specific pain point that Purview's governance-oriented approach does not.

dbt Tests: Quality as Part of Transformation

dbt's built-in testing framework is the simplest option on this list; and intentionally so. It provides four core tests (unique, not_null, accepted_values, and relationships), unit tests for validating SQL modeling logic before production runs, and model contracts that enforce schema expectations at the transformation layer. Everything is YAML-defined, version-controlled, and executes as part of dbt test runs.

The scope is deliberately narrow. dbt testing covers validation only. There is no profiling, cleansing, anomaly detection, or monitoring. But for teams already using dbt for transformations, the marginal cost of adding quality checks is near zero; and the checks run exactly where data changes happen.

Monte Carlo: Observability Over Testing

Monte Carlo occupies a different category entirely. Rather than asking you to define and maintain quality rules, it automatically monitors data assets using ML models trained on historical patterns. It tracks freshness, volume, schema changes, and distribution anomalies across the full data stack, and provides lineage-based root cause analysis when issues surface.

The distinction matters: Monte Carlo watches for problems you didn't anticipate. Rule-based tools catch problems you already know about. Most production environments need both.

Feature Comparison at a Glance

What This Means for Fabric Teams

Purview works well as a governance layer. It gives data stewards and domain owners visibility into quality scores across the organization. It connects natively to Fabric, Power BI, and OneLake. For teams whose primary concern is governance reporting and compliance, Purview delivers the scoring, alerting, and audit trail capabilities they need.

But governance visibility and engineering-grade quality are different problems. Most production Fabric environments benefit from a layered approach:

• Purview for governance-level quality management; scoring data products, mapping critical data elements, and monitoring quality trends across governance domains.
• Pipeline-native tools for engineering validation; dbt or Great Expectations to catch issues at the point of transformation, before bad data propagates downstream.
• Observability tools for automated monitoring; Soda or Monte Carlo to detect anomalies and drift that rule-based checks miss.
• Cleansing and MDM tools for remediation; Informatica or Ataccama for organizations that need to fix data quality problems, not just measure them.

Common stacks in production include Purview paired with dbt for transformation-layer testing, Purview paired with Great Expectations for detailed pipeline validation, and Purview paired with Soda or Monte Carlo for always-on observability.

Where TimeXtender Fits

Throughout this series, a recurring theme has emerged: Microsoft Fabric provides the compute, storage, and integration infrastructure for modern data estates. But relying on native tools alone for data quality forces teams into a cycle of manual coding, fragmented monitoring, and "post-load" debugging. Engineering teams spend 30–40% of their capacity writing validation scripts, debugging pipelines, and tracing lineage manually.

TimeXtender addresses this by placing a unified metadata framework across the entire data lifecycle; from ingestion through preparation to delivery. Rather than replacing Purview or any of the tools discussed above, TimeXtender operationalizes your Fabric environment so that governance tools like Purview have clean, well-documented data to work with in the first place.

Here's how that plays out in practice.

Quality before the governance layer. TimeXtender's Data Quality module runs automated profiling and rule-based validation at the ingestion and preparation stages. By the time data reaches your curated layer and Purview scans it, the obvious problems; nulls, duplicates, format violations, referential integrity failures; have already been caught and handled. Purview catalogs a governed environment instead of cataloging your chaos.

Metadata-driven lineage. Because TimeXtender generates the underlying code (Spark notebooks, T-SQL, pipeline logic), it knows exactly when a job starts, stops, fails, or succeeds. You get column-level lineage from Power BI dashboards all the way back to source tables. When a schema change happens upstream, you see the blast radius before you deploy; a capability that Fabric's native lineage and most of the tools listed above do not provide out of the box.

Platform portability. TimeXtender deploys to Fabric, Azure SQL, Snowflake, AWS, and on-premises environments through a single abstraction layer. Your quality rules, transformations, and business logic are defined once and deployed wherever your data lives. This protects your investment if your infrastructure evolves; or if you need to support multiple platforms simultaneously.

Lower total cost of ownership. By automating quality checks, code generation, and orchestration, TimeXtender reduces the manual engineering effort that drives hidden costs in Fabric environments. You define validation rules through a low-code interface rather than writing and maintaining custom Python or PySpark. You deploy those rules consistently across environments without duplicating work for each Fabric workspace.

Choosing Your Stack

No single tool covers every data quality requirement. Purview's recent enhancements; custom SQL rules, incremental scans, error record publishing; have closed some gaps. But the separation between governance-oriented quality management and engineering-oriented pipeline validation remains. The tools you pair with Purview should depend on your team's priorities: code-first testing, automated observability, data cleansing, or a unified platform that handles quality before governance takes over.

If you're building on Fabric and want to reduce the complexity of managing quality across your data estate, explore how TimeXtender integrates with Microsoft Fabric to automate the data lifecycle from ingestion to delivery. Or revisit the earlier posts in this series for practical guidance on metadata management, automating quality checks, and data governance strategies for Fabric environments.