By: Syed Tanveer Hussain, Associate Principal - LTIMindtree

Introduction

When I first got my hands on Microsoft Fabric, I was genuinely excited. It felt like this might finally be the platform to tame the wild beast that is enterprise analytics. But as I dug deeper, it became clear that simply choosing the right tool was not enough for building scalable, reusable pipelines. It was really about shaping the right approach.

Having spent years wrangling data in older systems such as Oracle and SQL Server, I knew firsthand the frustration of manual setups and fragile logic. Every new data source felt like starting a whole new mini project. So, I began sketching out a smarter way—a metadata-driven framework.

The idea was to automate repetitive tasks, freeing engineers to focus on more meaningful challenges. My goal was simple: design something dynamic, configurable, and scalable enough to serve different clients. And if it could trim effort and cost by 30-40%, well, that would be even better.

Why the current state doesn’t work

Let’s face it, data engineering today is messy. Workflows often feel fragmented, naming conventions are inconsistent, and pipelines tend to break the moment you add a new source. In large migration projects, particularly during acquisitions or divestitures, onboarding new data assets can be painfully slow. Each client brings unique table and naming standards, which only adds to the complexity.

Manual mapping is not just tedious, it invites errors. Plus, without central governance or solid audit processes, it becomes nearly impossible to track what is happening. Microsoft Fabric offers powerful tools such as lakehouses, warehouses, notebooks, pipelines, but without a smart, standardized framework, you can easily fall back into the same old traps.

This is where metadata makes the difference. By clearly defining systems, mappings, objects, and workflows in metadata tables, we can automate ingestion and transformation. Bringing a new source online becomes a matter of updating a few records. The pipeline adapts automatically. Transformations apply consistently. And comprehensive audit logs capture what happened, when, and where. It is not just automation—it is regaining control.

Crafting a modular solution

The framework I built is highly modular, designed around six layers: system definition, system mapping, object mapping, transformation logic, workflow orchestration, and stage management.

System definition registers each source and target, whether Azure SQL, Oracle, or even flat files, and dictates how to connect and interact with them. System mapping links these sources to their respective targets, specifying the load type, whether pipeline, notebook, or stored procedure. Object mapping then defines tables, files, execution order, parallel groups, and load strategies.

Transformation logic applies field-level and group-level rules using SQL, PySpark, or even rule-based scripts, all entirely driven by metadata. Workflow orchestration coordinates execution across different stages, supports retries, and ensures jobs run in the correct sequence. Finally, stage management actively tracks status— start, fail, restart, complete, giving instant, real-time visibility into pipeline health.

This framework naturally supports the bronze–silver–gold layering: bronze for raw ingestion, silver for cleansed and conformed data, and gold for crucial business-ready datasets. It  is also designed to be restartable, scalable, and easily deployable across development, test, and production environments without heavy DevOps dependencies. Job sequences are fully configurable in JSON, allowing flexible workflows.

The power of configuration tables

At the core sit carefully crafted configuration tables: system, system mapping, object mapping, transformation configuration, workflow, stage, and audit. These define everything, from source systems to transformation logic and execution order. Adding a new source is straightforward: replicate the pipeline and adjust the connection type. Transformations are set up in notebooks and then linked directly through metadata.

Procedure loads support tables, files, and custom configurations. Audit tables meticulously track progress and status, making restartability and error handling a breeze. The framework is not just technical plumbing; it is a reusable solution ready to be deployed across multiple clients. Every source, destination, lakehouse, and warehouse is configured and dynamically driven by metadata. Workflows and mappings are fully dynamic, supporting both parallel and sequential execution.

Barriers and success stories

Designing the metadata schema was the toughest nut to crack. If it was too abstract, people struggled to follow. If it was too rigid, it could not scale. Finding the sweet spot took trial and error. Auditability posed another challenge. I wanted traceability without burying users in endless technical jargon. The result was a lightweight audit layer—clear enough for troubleshooting, but not overwhelming.

One of the most satisfying moments came during a migration from an old Microsoft SQL Server setup. Instead of rewriting pipelines, we defined everything in metadata, leveraged JSON configurations and notebooks, and let the framework orchestrate the process. The migration was faster, cleaner, and completely traceable. That project became a brilliant showcase of how Microsoft Fabric for cloud migration can be harnessed effectively.

Another major win was slashing onboarding time for new sources—from nearly three weeks to just a few days. Developers stopped rewriting redundant logic. Governance teams finally had real visibility. And project managers could, for the first time, estimate timelines with genuine confidence.

Closing thoughts

This framework has genuinely changed how I look at data engineering. It is more than a technical solution; it is a shift in mindset. By moving so much logic into metadata, we have created a system that is easier to scale, simpler to maintain, and faster to adapt.

If you are tackling a cloud migration or are tired of rebuilding pipelines from scratch, I believe this approach can save significant time and headaches. Whether you are a data engineer, an architect, or a project manager, this framework was designed with you in mind. And if you’re exploring Microsoft Fabric for cloud migration, this strategy can help unlock real speed and resilience.

Let’s connect, exchange ideas, and push this further. There is so much more we can do to make data engineering less painful and far more rewarding.

References

1. Microsoft Fabric documentation: https://learn.microsoft.com/en-us/fabric/
2. What is Microsoft Fabric, May 9, 2025: https://learn.microsoft.com/en-us/fabric/fundamentals/microsoft-fabric-overview