Metadata-Driven Data Ingestion with Microsoft Fabric
A U.S. based SaaS company replaced dataset-specific ETL pipelines with a reusable Microsoft Fabric ingestion framework for hundreds of tables.
Business Process Challenges
The client was managing hundreds of similar data ingestion workflows independently. Maintaining separate ETL pipelines for each dataset resulted in duplicated logic, inconsistent processing patterns, and increasing operational overhead. As the number of datasets grew, onboarding new tables also required additional development effort instead of simple configuration.
Key challenges included:
- Pipeline Sprawl: Separate pipelines were maintained for individual datasets.
- Duplicated Logic: Similar ingestion and transformation logic was repeated across workflows.
- Inconsistent Processing: Full and incremental loads were implemented differently across datasets.
- Watermark Management: Incremental load watermarks were maintained separately for different pipelines.
- Scheduling Overhead: Each pipeline required its own scheduling and operational management.
- Limited Centralized Metadata: There was no single control layer to clearly define source, target, load type, schedule, and processing rules.
Our Strategic Approach
The solution focused on replacing dataset-specific ETL pipelines with a reusable, metadata-driven ingestion framework in Microsoft Fabric. Instead of embedding source, target, load, and scheduling logic inside individual pipelines, these rules were centralized in metadata and processed through a generic pipeline. This approach aligns with Fabric’s support for scalable pipelines, orchestration, and lakehouse ingestion.
Key implementation areas included:
- Metadata-Driven Control: Centralized source, target, table, load type, watermark, schedule, and transformation rules in metadata.
- Reusable Generic Pipeline: Designed one common pipeline to read metadata and dynamically process different datasets.
- Incremental Processing: Used watermark information to support controlled incremental data loads.
- OneLake & Lakehouse: Landed ingested data into OneLake and organized it within the Fabric Lakehouse for analytics.
- Data Transformation: Used Dataflow Gen2, Notebooks, PySpark, and SQL based on transformation requirements.
- Validation & Monitoring: Added validation for record counts, keys, execution status, and pipeline outcomes before updating operational state.
- Scalable Onboarding: New tables could be introduced through metadata configuration rather than creating a separate pipeline for every dataset.
- Migration & Modernization: Existing ADF/Synapse ingestion patterns were progressively consolidated into reusable Microsoft Fabric patterns.
How we delivered it.
Explore the Solution Through visuals
What changed for the client.
The metadata-driven approach established a reusable ingestion framework that reduced dependency on dataset-specific pipelines and provided a more consistent operating model for data onboarding and processing. The framework centralized ingestion controls in metadata while allowing a generic Fabric pipeline to handle different datasets. Microsoft Fabric supports this pattern through pipelines, OneLake, Lakehouse, Dataflow Gen2, and notebooks for ingestion and transformation.
Key outcomes included:
- Reusable Ingestion Framework: Common ingestion logic could be reused across multiple datasets.
- Reduced Pipeline Duplication: New datasets could be onboarded through metadata configuration rather than creating separate pipelines.
- Standardized Processing: Full and incremental ingestion followed a consistent framework.
- Centralized Operational Control: Source, target, load type, watermark, scheduling, and transformation rules were managed through metadata.
- Improved Scalability: The framework was designed to support hundreds of datasets without multiplying pipeline definitions.
- Better Maintainability: Changes to common ingestion behavior could be managed centrally.
- Modernized Data Platform: The approach established a scalable foundation using Microsoft Fabric, OneLake, and Lakehouse for broader data engineering and analytics modernization.
The numbers behind the rollout.
The full integration layer.
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