Microsoft Fabric Unifies Your Data and AI. Datagaps Proves You Can Trust It.

Q: What can Datagaps validate in Fabric that Fabric itself cannot?

Datagaps provides row-level source-to-target comparison, five-layer migration testing, transformation validation across Bronze, Silver, and Gold Medallion layers, Power BI report testing, continuous anomaly detection, failed-record isolation, and synthetic test data generation.

Q: How does AI rule generation work on Fabric data?

Describe what you want validated in plain English. Data Quality Monitor uses table structures, column types, and data patterns to generate SQL-backed data quality rules mapped to quality dimensions for Microsoft Fabric data.

Q: Do we need Microsoft Purview to get governance value?

No. Datagaps provides capabilities including data cataloging, BI cataloging, column-level lineage, quality scoring, and audit-ready reporting. Where Microsoft Purview is already in use, Datagaps can complement the broader governance workflow with data validation and quality capabilities.

Q: What sources beyond Fabric does Datagaps support?

Datagaps supports data sources across cloud, on-premises, and hybrid environments, including SQL Server, Oracle, Azure Synapse, Databricks, Snowflake, Redshift, BigQuery, PostgreSQL, Teradata, SAP, Salesforce, files, and APIs. DataOps Suite can validate data across heterogeneous source and target environments.

DataOps Suite validates, monitors, and governs every stage of Microsoft Fabric — OneLake ingestion, Lakehouse and Warehouse transformations, Power BI reporting, and the Copilots and AI agents built on top of it.

Fabric delivers data. It doesn't guarantee its quality.

We close the gap with Microsoft Fabric data validation, quality monitoring, and governance before it drives decisions.

What Microsoft Fabric gives you

A unified platform that moves, stores and presents data.

What Datagaps proves on top

The evidence layer that shows every number is correct.

Every migration verified row by row before go-live
Quality scored continuously across every Fabric table
Every dashboard number traced back to its source record
Lineage and audit evidence generated automatically
Test environments run on synthetic data, never production PII

Six problems Fabric teams solve with Datagaps.

Inaccurate data after migration

Data lands in Lakehouse but precision mismatches, missing records, and broken transformations go undetected for weeks.

Unreliable Power BI dashboards

Numbers shift after every semantic model refresh. Business teams can’t tell if the data is wrong or the report is wrong.

Data quality that doesn't scale

Manual rules cover 20 Lakehouse and Warehouse tables. Your environment has 100+. No continuous monitoring in between.

AI built on unvalidated data

Copilots and RAG workloads produce answers no one can stand behind because the underlying OneLake data was never verified.

No audit trail from source to Power BI

Compliance needs documented lineage across every transformation layer. Spreadsheets don’t survive scrutiny.

Sensitive data exposed in testing

Copying production data into dev and test environments creates PII leaks, GDPR violations, and compliance risk no one catches until it’s too late.

One platform behind your Fabric trust layer

Deploy any product on its own, or connect all four for the full validation, quality and governance stack across your Fabric estate. One platform for every Microsoft Fabric testing need.

ETL Validator

Solves: Fabric migrations and pipeline validation

Five-layer migration validation from metadata through BI. AI generates test cases from mapping specs. Self-healing tests adapt when schemas change

BI Validator

Solves: Power BI report accuracy on Fabric

Visual and cell-level regression testing across Power BI dashboards built on Fabric semantic models. Traces every number in a dashboard back to its source record in the Warehouse or Lakehouse and performance testing under simulated load.

Data Quality Monitor

Solves: Continuous quality across Fabric data

AI generates Data Quality rules from plain-English prompts. Anomaly detection catches drift before business users do. Live scoring with bad-record isolation.

Test Data Manager

Solves: Sensitive Fabric data in dev and test

ML-generated synthetic data that preserves relational integrity. Auto-detects PII and PHI. GDPR, CCPA, and HIPAA aligned. No production data copied.

DataOps Suite — unified trust across the Fabric lifecycle

One platform covering the full Fabric lifecycle: unified lineage across every stage, a data catalog and BI catalog for cross-team discovery, column-level audit trails and generatable compliance reports.

AI handles the repetitive work. Your team handles the decisions.

Microsoft Fabric testing automation — powered by Agentic AI.

Accelerated test authoring

AI generates test cases from mapping docs, SQL prompts, or schema metadata. Days of work done in minutes.

Self-healing test scripts

When Fabric schemas evolve, validation rules update automatically. Zero manual rework when Medallion layers change, tables get restructured or columns are added.

Instant root-cause analysis

AI explains every discrepancy in plain language – column, error type, record count. Fix the problem, not hunt for it.

AI-Generated Data Descriptions & Quality Rules

AI adds column descriptions and suggests data quality rules for your Fabric data, so it’s easier to find, faster to govern, and ready for AI.

Measurable impact for Fabric teams.

Faster test cycles vs. manual testing

500

AI-generated DQ rules in under two hours

Reduction in validation effort and spend

Beyond validation. Toward continuous Fabric data trust.

Datagaps is expanding from a data validation platform into a full trust platform spanning validation, Microsoft Fabric quality monitoring and governance across your Fabric footprint. Six ways that shows up in practice.

Validate 100% of Fabric records, not samples

No blind spots, no gaps, no sample spot-checks.

Cut Fabric migration testing from weeks to days

AI generates test cases from mapping docs. Self-healing tests adapt when schemas change.

Monitor Fabric data quality continuously

Six-dimension DQ checks with anomaly detection. Catch drift before it reaches Power BI.

Govern your Fabric data with confidence

Data catalog, BI catalog, column-level lineage and generatable audit reports covering SOX, GDPR and industry frameworks.

Trust Power BI dashboards through every refresh

Cell-level regression against Fabric source data, with AI-generated diff summaries that explain exactly what changed.

Make Fabric AI-ready

Profiled, validated, and described — the three inputs copilots and RAG systems actually need.

Power BI dashboards your business can defend.

Power BI is where Fabric data becomes decisions — and where every unvalidated transformation becomes visible to the wrong audience.

Cross-environment dashboard comparison

Compare Power BI reports across Fabric dev, UAT and prod. Detect visual, data and layout drift before users see it.

Cell-level data validation

Trace every number back to its source record.

Semantic model and DAX measure testing

Validate measure accuracy across semantic models on Fabric. Catch DAX calculation errors before they land in board reports.

Performance and load testing

Simulate concurrent user loads to test report performance under realistic conditions. Prove SLAs before they break in production.

Datagaps Complements Purview, And Stands On Its Own.

Datagaps extends your Purview investment. It handles the validation, quality monitoring, and reconciliation Purview isn’t built for — then publishes results back into Purview.

Don’t use Purview? Datagaps stands on its own. Data catalog, lineage, quality scoring, and audit-ready reports come built in.

Microsoft Purview · Governance layer

Datagaps · Validation + Quality + Governance

Catalog and discovery

Lineage across the Fabric estate

Ownership and glossary

Classification and sensitivity

Access and compliance policies

Answers: what is this Fabric data?

Broader Coverage Without Source Constraints

Purview works from a fixed source list. Datagaps connects through JDBC, ODBC, or any supported interface — 200+ sources and counting.

Comprehensive Validation Across the Data Lifecycle

Purview shows where data went. Datagaps proves it arrived correctly — from ingestion through transformation to BI.

More Predictable Economics at Scale

No per-check processing charges. DQ, ETL, reconciliation, and BI validation consolidated in one platform that scales with you.

See how Datagaps and Purview work together

A deep dive into how Datagaps handles validation, quality, and reconciliation while Purview handles governance — and how results flow between them.

Get Started Today

Experience Datagaps Live - Trust your Data With Confidence

Our team will run a live Microsoft Fabric automated testing demo against your specific use case: your stack, your data problem, your pipeline. Or start a 14-day free trial and validate your own Fabric data today.

SOC 2 Type II certified | ISO 27001 certified | No credit card required for trial | Your data never leaves your environment

FAQs: Enterprise Fabric buyers.

Common questions from Enterprise Buyers

How does Datagaps connect to Microsoft Fabric?

Through JDBC with OAuth authentication — the same way you’d connect any of the 200+ sources ETL Validator supports, including Snowflake, Databricks, and Azure Synapse. Setup takes minutes. See it running on your own Fabric tables with a free 14-day trial or a live demo.

Does Datagaps validate 100% of records or just a sample?

100%, at the row level — every row, column, and transformation is verified, never sampled. That matters most for financial data, regulatory reporting, and migration projects, where a sampled “pass” can still hide a compliance failure. See it in practice in a Fortune 100 mainframe-to-Snowflake migration run with zero added headcount.

How does Datagaps validate a migration to Microsoft Fabric?

Whether you’re moving from Synapse, SQL Server, or a legacy warehouse, ETL Validator runs five-layer migration testing — metadata, profile, row-level comparison, transformation, and BI — before, during, and after go-live. A Fortune 100 team used this exact approach for a mainframe-to-Snowflake migration. Model your own timeline with the ROI Calculator.

What can Datagaps validate in Fabric that Fabric itself cannot?

Row-level source-to-target comparison, five-layer migration testing, transformation validation across Bronze/Silver/Gold Medallion layers, Power BI report testing, continuous ML-based anomaly detection, failed-record isolation, and synthetic test data generation — none of which Fabric’s native tooling covers today. Full methodology in the ETL Testing Complete Guide.

How does Datagaps handle Power BI dashboards on Fabric?

BI Validator runs visual and cell-level regression across Power BI reports built on Fabric semantic models, traces every number back to its source record, generates AI-powered diff summaries, and load-tests performance under simulated concurrency. The same engine covers Tableau and Oracle Analytics too. Full methodology: BI Testing Complete Guide.

How does AI rule generation work on Fabric data?

Describe what you want validated in plain English. The AI reads your Fabric table structures, column types, and data patterns, then generates SQL-backed rules mapped to six quality dimensions — teams routinely reach 500+ rules in under two hours. It’s the same Data Quality Monitor engine behind our AI-readiness guide for Fabric-powered Copilots and RAG.

Do we need Microsoft Purview to get governance value?

No. Datagaps natively provides a data catalog, BI catalog, column-level lineage, quality scoring, and audit-ready reports. If you do use Purview, Datagaps extends it — validating sources beyond Purview’s fixed, native-connector list and publishing results back in. See governance and validation working together in this data governance case study.

What sources beyond Fabric does Datagaps support?

Over 200 sources across cloud, on-prem, and hybrid — SQL Server, Oracle, Azure Synapse, Databricks, Snowflake, Redshift, BigQuery, PostgreSQL, Teradata, SAP, Salesforce, files, and APIs. One DataOps Suite deployment validates all of it if your estate spans more than Fabric — full connector list on the ETL Validator page.

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