Bad data is expensive.
Industry research shows that data quality failures cost organizations millions every year in rework, bad decisions, and compliance risk. When you layer AI and self-service analytics on top, the stakes rise even higher: one poorly governed dataset can ripple through dashboards, machine learning models, and executive decisions in ways that are hard to see and even harder to undo.
Microsoft Fabric promises to reduce that risk by bringing your analytics estate into a single, unified platform: OneLake for storage, shared governance, integrated experiences for data engineering, warehousing, and BI.
But Fabric does not magically create trusted data.
In our previous article, Top Data Quality Challenges When Using Microsoft Fabric (And How to Solve Them), we looked at the most common data quality pitfalls Fabric teams face and how to address them with native capabilities plus TimeXtender.
This one goes one level deeper. Here, we focus on the common thread that sits underneath every successful Fabric implementation: metadata.
Our core argument is:
In Microsoft Fabric, metadata is the backbone of data quality. If you don't treat metadata as a first-class product, you will struggle to deliver trustworthy analytics at scale – no matter how powerful the platform.
Microsoft is investing heavily in governance, the Purview Hub, Data Quality, and OneLake Catalog and this is a practical guide on how to use metadata wisely, where the gaps still exist, and how a metadata-driven platform like TimeXtender can help you go further, faster.
Why Metadata Is the Backbone of Data Quality
Metadata is “data about data.” It includes:
- Technical metadata: schemas, data types, table and column names
- Business metadata: definitions, owners, usage context, data products
- Operational metadata: lineage, run history, quality scores, SLAs
- Security metadata: sensitivity labels, access policies, classifications
In Microsoft Fabric, all of this is woven into the platform fabric:
- OneLake and the OneLake Catalog: OneLake gives you a single logical data lake for the tenant, built on Delta Parquet in Azure Data Lake Storage Gen2. The OneLake Catalog acts as the front door for that lake, with Explore, Govern, and Secure tabs for discovery, governance insights, and security configuration.
- Purview integration and the Purview Hub: Fabric pushes metadata to Microsoft Purview’s Data Map, which powers a unified catalog and data estate-wide governance. Within Fabric, the Purview Hub surfaces insights about sensitive data, endorsement, and governance posture without leaving the product.
- Security and access control: Workspace roles, item-level sharing, OneLake Security (for table/folder/schema), and sensitivity labels all depend on metadata.
- Fabric Data Quality and Data Activator: Purview Data Quality uses profiling metadata and rule definitions to score Lakehouse tables and generate quality scorecards. Data Activator listens to metrics and events – including quality scores – and triggers alerts or actions when thresholds are breached.
When metadata is rich, consistent, and connected, it fundamentally acts as the control plane for data quality. This means that profiling and validation rules can accurately identify the relevant fields and their expected structure. Lineage clearly shows the data's origin and all transformations it underwent. The connected metadata also enables impact analysis to predict who will be affected by a schema change prior to deployment. Along with those, essential attributes like security policies and sensitivity labels can follow the data consistently as it moves across various workspaces and workloads.
But when metadata is fragmented or missing, data quality becomes reactive and brittle. That is where many real-world Fabric implementations start to struggle.
The Metadata Challenges Hiding Inside Fabric Implementations
We’ve covered the lack of a fully integrated data quality module, performance surprises, and migration pitfalls. Now, we’ll focus on what those challenges look like specifically through the lens of metadata.
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No single place to manage data quality metadata
Fabric now offers strong building blocks for data quality. You can use Great Expectations in Spark environments, configure Purview Data Quality for no-code rule definition and profiling, and lean on Data Factory activities or materialized lake views with constraints to enforce checks closer to storage.
What’s still missing is a single, dedicated data quality module that ties everything together. There is no central place that holds all rule definitions, thresholds, and owners, applies those rules consistently across pipelines, Lakehouses, Warehouses, and semantic models, and surfaces a unified, cross-domain view of data quality.
Because of that, quality logic tends to spread across notebook code in data engineering, T-SQL in warehouses and views, Purview Data Quality rules and scorecards, and custom audit tables or dashboards. Each of these depends on metadata, but they aren’t driven from one shared metadata model, which increases effort and makes consistency harder to achieve.
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Metadata fragmentation and partial lineage
Out of the box, Fabric’s metadata story is improving quickly, especially with the OneLake Catalog’s Govern tab reaching general availability and deeper integration with Purview. That said, many teams still run into fragmentation in day-to-day work.
Different artifacts – Power BI reports, pipelines, Lakehouses, Warehouses, and notebooks – each manage metadata in slightly different ways. Cross-workspace lineage is often incomplete for non–Power BI items, particularly when custom orchestration or external tools are in the mix. Column-level lineage is strong inside Power BI semantic models, but less granular for some upstream layers.
The net effect is that lineage and impact analysis often stop at the boundaries of a workspace or workload. To compensate, teams export metadata with Scanner APIs or build their own registries to close the gaps. This isn’t a flaw in Fabric’s direction so much as a normal stage in the evolution of a fast-moving platform. But if you don’t design a metadata strategy up front, your data quality strategy will end up scattered by default.
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Manual effort to encode quality rules as code
Across community examples and real projects, the same pattern keeps showing up. Null checks are implemented manually at ingestion into the raw Lakehouse. Pattern validations for emails, phone numbers, and IDs are written in PySpark or T-SQL. Referential integrity checks between fact and dimension tables are coded explicitly, and business rule validations (age limits, date ranges, domain logic) are embedded directly inside notebooks and stored procedures.
In practice, you often see a series of PySpark snippets for null detection, duplicate detection, outlier detection, and referential integrity checks, all hand-coded and wired into pipelines.
All of this is metadata, but it lives inside code rather than in a central, declarative model. That makes it harder to reuse rules across domains, to hand work off when key engineers move on, and to prove to auditors how, when, and where data was validated.
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Configuration and governance overhead for Purview Data Quality
Purview Data Quality is a meaningful step forward: it brings profiling, rule recommendation, scorecards, and integration with the Purview Hub into the picture.
At the same time, data teams still report friction in getting it fully operational. There are multiple prerequisites around admin API responses, service principals, managed identities, and workspace permissions. You need to work with specific file formats and Lakehouse targets. Much of the configuration is managed through the UI rather than code, which complicates infrastructure-as-code practices and makes promotion across environments more manual than teams would like.
So while Purview Data Quality strengthens the platform, the configuration and governance overhead means many organizations still lean on custom, code-centric approaches for day-to-day quality checks unless they put an explicit metadata strategy in place.
Fabric and Purview evolved separately and are now being brought closer together. As a result, many organizations still lean heavily on custom, metadata-light code for day-to-day quality checks, while Purview focuses on profiling and high-level scorecards.
What “Good” Metadata-Driven Data Quality Looks Like
So what does “good” look like if you want metadata, not code, to be the backbone of data quality in Fabric?
Think in terms of a metadata-driven control plane that sits alongside Fabric and drives your pipelines.
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Centralized control tables that drive behavior
Rather than scattering logic across notebooks and stored procedures, you define it once in control tables stored in your Lakehouse or Warehouse, such as:
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Source registry: System name, connection details, domain, owner, expected refresh cadence and SLAs, data sensitivity classification
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Dataset catalog: Canonical name, business owner, description, layer (Bronze, Silver, Gold), domain, workspace, endorsement status and quality tier
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Field-level metadata: Data type, nullable flag, reference table, allowed values, business definition and example values, sensitivity labels and masking requirements
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Quality rules and thresholds: Rule templates: completeness, uniqueness, referential integrity, format, range, custom logic, applicable datasets and fields, thresholds (e.g., max 0.5% nulls), severity (error/warning), owner
In this model, pipelines read metadata instead of hardcoding logic. That means adding a new quality rule for a column is a metadata change, not a code change. Data stewards can manage rules through a governed UI or process rather than raising engineering tickets. Audit logs for rules and thresholds live in one place.
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Metadata-driven orchestration across the medallion architecture
Your architecture still follows Fabric’s medallion pattern – Raw/Bronze, Clean/Silver, Curated/Gold – but transitions between layers are driven by metadata:
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Ingestion gates: Control tables specify which sources and tables to ingest, pipelines use that metadata to create or extend Lakehouse tables, basic profiling and schema drift checks run automatically, logging issues to a quality journal.
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Cleansing and standardization: Rules for standardizing formats (dates, currencies, identifiers) are defined once and reused across datasets, pipelines pick them up by matching domain, data type, or tags in your metadata.
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Business logic and referential integrity: Foreign key relationships live in metadata, pipelines validate those relationships and log violations, with severity and next actions controlled by metadata.
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Consumption gates: Only datasets that meet quality thresholds and endorsement criteria progress to Gold and semantic models, dashboards draw from certified data products with documented quality SLAs.
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Lineage and impact analysis as everyday tools, not “nice to have”
With strong metadata practices, lineage is not just a diagram; it becomes the backbone of change management:
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Every dataset, transformation, and quality rule is associated with an owner, domain, and downstream consumers.
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When a column changes, you can quickly see which quality rules, semantic models, and reports depend on it.
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Purview lineage provides a platform view, while your own metadata adds business context and quality rules on top.
This is the state many Fabric teams are aiming for, but building it from scratch in native tools alone requires time, expertise, and discipline.
That is where TimeXtender comes in.
How TimeXtender Uses Metadata to Strengthen Data Quality in Fabric
TimeXtender’s role is to help you turn metadata into the operating system for your data estate, and then connect that estate into Fabric.
A three-layer architecture aligned with Fabric’s medallion pattern
TimeXtender implements a three-layer architecture that maps neatly to how most teams structure Microsoft Fabric. At the Ingestion stage, it connects to various sources using fully managed connectors, lands data in Fabric Lakehouse in open Delta Parquet format, and captures schema, data types, and source-level metadata as first-class objects. In the Prepare stage, it automates dimensional modeling and star schema creation, optimizes Delta tables or Fabric SQL Databases for performance, and keeps transformation logic in metadata, not in scattered code. Finally, in the Deliver stage, it publishes semantic models into Fabric workspaces using Power BI endpoints and aligns business definitions, measures, and hierarchies with upstream metadata.
This architecture gives you a consistent metadata model that stretches from raw ingestion all the way to Power BI, while still taking full advantage of Fabric’s native capabilities.
Unified metadata framework and automation
The key difference between native-only approaches and TimeXtender is where the complexity lives. In native Fabric, your metadata is split across source systems and ad-hoc documentation, Power BI models and workspaces, the Purview Data Map and Data Quality configurations, and custom tables used for tracking and audit.
In TimeXtender, everything flows through a unified metadata framework that stores data sources, transformations, relationships, and quality rules centrally, generates Spark, T-SQL, and Fabric notebook code from that metadata, keeps lineage and documentation in sync as you evolve your data estate, and produces audit trails for changes, deployments, and validations.
This has direct impact on data quality outcomes. You get faster rule implementation because, instead of writing new PySpark for each check, you use a low-code rule designer to configure completeness, format, range, and referential integrity rules, and the platform generates and orchestrates the underlying code automatically. You get consistent application of rules because once a rule template exists in metadata, it can be reused across data products and domains, and changes roll out in a controlled way instead of patching individual pipelines. You also gain rich lineage and documentation: column-level lineage is captured across all layers, not just inside Power BI, and auto-generated documentation helps both auditors and new team members understand what the data means and how it is validated.
Complements Fabric’s native capabilities
TimeXtender fortifies and complements Fabric’s governance tools. With Purview Data Quality, you can still profile Lakehouse tables and create high-level scorecards, while TimeXtender feeds well-modeled, high-quality tables into those scans, reducing noise and false positives. With Data Activator and alerting, TimeXtender can write quality metrics and status flags into tables that Data Activator watches, powering reflexes for alerts, ticket creation, or pipeline pauses when quality drops. With the OneLake Catalog and Purview Hub, assets that TimeXtender creates or manages still appear with lineage and sensitivity labels, so your governance teams continue to use Fabric-native tools as their main console, while TimeXtender provides the metadata-driven logic underneath.
Fabric provides the unified analytics platform and governance foundation, and TimeXtender adds a metadata-driven control plane that makes data quality and governance easier to operationalize.
Where to Start Checklist
If you're leading a Fabric implementation today, you likely already know that there are a lot more data products, more self-service, more AI use cases – with the same or smaller team.
Here is a practical checklist to start making metadata the backbone of your data quality strategy.
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Assess your current metadata and data quality
Inventory your critical data products and the domains they support. For each, ask: Do we know the source systems, owners, and SLAs? Are quality rules documented anywhere? Do we have lineage from source to semantic model?
Compare your findings against the best practices for governance and metadata management.
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Define conventions and structure in Fabric
Before adding more tooling, standardize how you use the platform. Revisit:
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Naming: adopt consistent patterns that encode domain, layer, and object type.
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Workspace strategy: align workspaces to business domains and lifecycle (dev/test/prod).
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Endorsement: define what it means for a dataset to be Promoted or Certified, and who approves it.
- Documentation: require descriptions for datasets, notebooks, and semantic models; link them to a business glossary in Purview where possible.
These steps cost little but have outsized impact on discoverability, trust, and change. management.
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Identify a “metadata-first” pilot
Pick one high-value data product – for example, a finance dashboard or customer health score – and design its lifecycle with metadata at the center. Build control tables for sources, datasets, fields, and quality rules. Wire your Fabric pipelines or TimeXtender project to read those tables and act on them. Track quality metrics (completeness, timeliness, accuracy proxies) and share them with stakeholders.
Use this pilot to refine your patterns, then extend them to other domains.
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Decide which parts to own, and which to automate
Some organizations are comfortable building a full metadata framework themselves using Fabric, Purview, and custom code. Others want to focus their team’s capacity on domain logic and delegate the rest.
For this it’s important to know the engineering bandwidth to build and maintain an in-house metadata-driven framework. How much time is currently being spent on wiring quality rules, lineage, and documentation by hand? What is the opportunity cost if those engineers instead focused on higher-order analytics and AI use cases?
If the answer points toward acceleration, it may be worth evaluating a metadata-driven platform like TimeXtender to sit alongside Fabric and automate the heavy lifting.
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Explore how TimeXtender can help
If you’re already using Fabric or planning to, TimeXtender can help quickly stand up a metadata-driven pipeline from source to Fabric Lakehouse and semantic model. It can help eliminate manual coding while improving lineage and documentation, and it will enable business users to see and adjust quality rules without editing code.
Explore other Fabric-related content on TimeXtender's blog to see how organizations are using a metadata-first approach in practise here.
Microsoft Fabric gives you a powerful foundation for unified analytics and governance. Metadata is the bridge that connects that foundation to trusted, high-quality data products. If you treat metadata as an afterthought, you will keep fighting the same quality fires, just on a more modern platform.
If you treat metadata as the backbone of your architecture and use tools that are built around it, you can turn Fabric into the enabler of a truly governed, AI-ready data estate.
See it in action for yourself:
