Microsoft Fabric for GCC data operations: how would you structure the platform for scale?

A GCC implementation we examined involved more than just implementing a data platform; it required the realization of a scalable data engineering function to adapt to growing data volumes, expansion of pipelines, reporting, and business teams.

The ecosystem consisted of many data sources and data engineering tasks, with teams focusing on aspects such as data ingestion, transformation, quality control, pipeline supervision, analytics, and platform support. With the GCC progressing, a key question arose: how to harmonize the workloads without an excessive increase in operational costs?

If I were asked to implement this today using Microsoft Fabric, I would like to see how many processes of the stack could be combined into one solution instead of having separate services at each stage.

An available solution might be:

• OneLake and Lakehouse for the central data hub with a reliable data source
• Data Factory/Dataflows Gen2 used to ingest and transform data
• Fabric Notebooks for complex data engineering, validations, and transformations
• Pipelines for managing, scheduling, connecting, and tracking the process
• Power BI and Direct Lake for creating reports based on selected data
• Microsoft Purview for data management and governance processes
• Access control with RBAC to work with development, testing, and production processes
• Git integration and pipelines to have more organized engineering and release management

However, based on my experience with data solutions, I believe there is no question whether Fabric will be able to create and support the above-mentioned environment, but about engineering consistency.

It is essential to establish definite standards regarding:

• Naming conventions and the architecture of the workspace
• Medallion and layered designs
• Patterns for ingestion
• Validation and quality checks
• Balancing failure and retry events
• Monitoring alerts and behvaviors
• Modern practices in CI/CD and versioning
• Who owns datasets, models and governance policies

One of the most important things here is how one can staff his/her engineering team. More engineers do not mean that you are creating a scalable data platform. If you fail to put standard patterns in place, you may end up getting more than one pipeline, duplicated transformations, no workspace management and various datasets.

One thing I would be especially interested in finding out from the Fabric community is the way non-centralized governance is effectively combined with the level of autonomy allowing GCC dev teams to work without inconveniences.

For those running Microsoft Fabric at scale, how are you structuring your workspaces, CI/CD, and engineering standards when multiple data engineering teams are contributing to the same Fabric environment?