Microsoft Fabric consulting for data teams moving beyond Power BI

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Fabric migration from Power BI

Fabric migration from Power BI is less about moving reports and more about moving the data logic underneath them. Many Power BI estates have heavy Power Query transforms repeated across datasets, refreshes that time out, and gateways pulling the same tables several times a day.

We move that logic upstream into Fabric pipelines and lakehouses once, then point semantic models at the curated tables using Direct Lake or import mode as appropriate. Reports keep working for users while the plumbing underneath gets simpler. We migrate in stages, one subject area at a time, so finance or sales reporting is never offline.

• Inventory of datasets, dataflows, gateways and refresh schedules
• Consolidation of duplicated Power Query logic into shared tables
• Semantic model rebuild on lakehouse or warehouse tables
• Parallel run and reconciliation before switching users over

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OneLake and Fabric data engineering

OneLake removes a lot of copying. Shortcuts let Fabric read data already sitting in Azure Data Lake Storage, Amazon S3 or Google Cloud Storage without moving it, and mirroring can replicate databases such as Azure SQL or Snowflake into OneLake in near real time.

For Fabric data engineering we typically use a medallion layout (bronze, silver and gold) in lakehouses, with Spark notebooks in PySpark or Spark SQL for transforms and pipelines for orchestration. Where an existing ERP such as SAP or Dynamics 365 is the main source, we plan extraction carefully so it respects source system load and licensing.

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Governance and enterprise rollout

Fabric makes it easy for anyone to create items, which is good for speed and risky for governance. We set up workspace structure, domains, roles and deployment pipelines or Git integration so changes move from dev to test to production through review.

Access runs through Entra ID, sensitive data can be labeled and tracked with Microsoft Purview, and capacity metrics are monitored so one heavy job does not slow everyone else. As one example from our case studies, a European procurement firm working on nuclear infrastructure saved about 30 hours a month with a supplier intelligence tool built on automated data collection and AI summaries.

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Microsoft Fabric vs Snowflake vs Databricks

Fabric is strongest for Microsoft-centric companies where Power BI is the main consumer of data and IT wants one capacity-based bill. Snowflake is a mature, cloud-agnostic warehouse with strong SQL performance and data sharing. Databricks is the deeper platform for large-scale Spark engineering and machine learning.

These are not always either-or choices. Fabric can mirror Snowflake and read Delta tables produced by Databricks through shortcuts, so some clients keep an existing platform and use Fabric mainly as the Power BI and self-service layer.

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What Microsoft Fabric consulting costs

We quote a fixed price after a free review. Cost depends on the number of sources, the size of the Power BI estate being migrated, and governance requirements. A readiness assessment and first lakehouse with one subject area typically take 3-4 weeks, and full migrations run in stages over 4-8 weeks or longer for large estates.

Fabric capacity is billed by Microsoft. We size capacity from your actual workloads, recommend pausing or scaling where it fits, and check whether report viewers still need Power BI Pro licenses at your capacity size.