Stop Building Pipelines. Start Driving Them with Evoke’s Metadata Framework in Microsoft Fabric

Most enterprise data platforms struggle not because of technology, but because of design. Every new data source means a new pipeline. Every schema change requires a code update. Every fix lead to redeployment. Over time, platforms become fragile, expensive to maintain, and slow to evolve.

Microsoft Fabric gives us a real opportunity to break this cycle — by shifting from pipeline-driven execution to metadata-driven platforms.

This article explains how Evoke’s Metadata-Driven and Data Quality Frameworks help organizations standardize ingestion, automate transformations, ensure data quality, and onboard new data sources faster than ever.

Most traditional ETL platforms today are:

• Hard-coded and tightly coupled:
  Pipeline logic is embedded directly in code, making even small changes risky and time-consuming.
• Difficult to maintain:
  Fixes and enhancements require code changes, testing, and redeployment across multiple pipelines.
• Slow to onboard new sources:
  Each new dataset often needs a custom pipeline, delaying delivery and increasing effort.
• Fragile when schemas change:
  Minor schema updates can break pipelines and trigger unexpected failures.
• Data quality issues:
  Validation is often manual or inconsistent, allowing issues to reach downstream reports unnoticed.

As a result, data teams spend more time maintaining pipelines than delivering insights.

Evoke’s Microsoft Fabric Data Accelerator resolves these challenges using built-in metadata-driven and data quality frameworks

By the end of this article, you will understand:

• What a metadata-driven and data quality framework really means
• Core components of a Metadata-driven and Data Quality frameworks.
• Impact of metadata-driven design

A metadata-driven framework replaces pipeline logic with metadata.

Instead of building separate pipelines for each source, you create:

• Reusable central execution ingestion and transformation logic
• Metadata that drives behavior
• Update metadata instead of code to adapt to new sources, schema changes, and rules—without redeploying pipelines.

This approach forms one of the core pillars of Evoke’s Microsoft Fabric Data Accelerator, ensuring consistent, repeatable ingestion patterns across projects and teams.

At the heart of the framework is a central orchestration engine built on Microsoft Fabric and packaged within Evoke’s Microsoft Fabric Data Accelerator.

This engine is fully metadata-driven and:

• Reads configuration from metadata
• Ingests data dynamically
• Applies standardized transformations
• Executes built-in validations and data quality checks
• Captures logs and execution metrics automatically

The same engine adapts seamlessly to any type of data source, enabling consistent ingestion and processing without creating new pipelines.

A metadata-driven approach delivers clear, measurable outcomes:

• Faster Onboarding: New data sources are onboarded in hours instead of weeks.
• Built-in Standardization: Every dataset follows consistent naming, audit fields, quality rules, and transformation patterns.
• Automation at Scale: Config-driven execution, automated validations, and centralized monitoring replace manual effort.
• Lower Cost of Ownership: Less custom code means fewer failures, reduced maintenance, and lower operational costs.

Framework Components

1. Metadata Repository
   Central store for source configuration, load behavior, transformations, validations, and schedules.
2. Ingestion Engine
   Reusable, metadata-driven engine supporting schema drift and incremental loads.
3. Transformation Engine
   Standardized Bronze → Silver → Gold processing driven entirely by metadata.
4. Data Quality & Validation Layer
   Built-in checks for nulls, uniqueness, referential integrity, with optional AI-based anomaly detection.
5. Logging & Monitoring
   Automatic capture of execution metrics, failures, and data quality results.
6. Orchestration Layer
   Handles dependencies, retries, parallel execution, and scheduling using Fabric Pipelines.

The framework naturally enforces:

• Bronze – raw ingestion
• Silver – Cleaned, validated data
• Gold – Business-ready datasets

Each layer is fully metadata-driven, ensuring consistent and repeatable outcomes across implementations.

Medallion Architecture

Microsoft Fabric unifies storage, pipelines, notebooks, SQL, governance, and Power BI in a single platform.

A metadata-driven framework complements Fabric’s design and amplifies its strengths—making it the foundation for scalable, standardized data platforms.

Real-World Impact
Client Case Study: Evoke helped a client unify multiple CRM systems along with structured and unstructured data into a single Microsoft Fabric platform, eliminating data silos, simplifying integrations, and enabling faster, more accurate insights for advanced analytics and Power BI reporting.

Final Thought
The future of data engineering isn’t about building more pipelines—it’s about creating intelligent platforms that adapt to change, scale effortlessly, and deliver trusted data faster.

In Microsoft Fabric, metadata isn’t an add-on—it’s the engine.

What’s Next
At Evoke Technologies, we help enterprises accelerate Microsoft Fabric adoption using practical, production-ready patterns. The Metadata-Driven and Data Quality Frameworks are two of the four core frameworks designed to standardize delivery, speed up onboarding, reduce rework, and maintain consistent momentum across projects.

If you’re looking to shorten time-to-value and standardize scalable data engineering from day one, contact us now!