Microsoft Fabric Consulting Services

Tightest Power BI, Microsoft 365, and Purview integration. Direct Lake mode skips data movement for BI workloads. Single F-sku covers compute across data engineering, warehousing, BI, and AI.

Younger platform. Expect feature gaps versus Databricks and Snowflake on advanced ML and multi-cloud workloads.

Mature for SQL pools and dedicated DW workloads. Existing Synapse customers can keep running it for years.

Microsoft is investing roadmap in Fabric, not Synapse. New greenfield Microsoft customers should default to Fabric.

Strongest ML platform (MLflow, Mosaic AI), broader open-format support, runs on AWS / Azure / GCP. Unity Catalog is the most mature lakehouse governance layer.

Pricing model is complex. Less native Microsoft 365 integration than Fabric.

Operational simplicity, separation of storage and compute, mature data sharing. Cortex adds AI features.

Premium pricing at scale. Native BI is improving but does not match Fabric's Power BI integration.

Pilot, single small workload, dev environment. Not production-ready for most enterprises.

Small production workload. One BI workspace, light data engineering.

Most mid-market production deployments. Multi-workspace BI, scheduled pipelines, light ML.

Enterprise BI, cross-team data engineering, larger ML workloads. Most Fortune 500 starts here.

Heavy enterprise deployments, real-time intelligence, large concurrent BI usage.

What is Microsoft Fabric and how is it different from Synapse?

Microsoft Fabric is Microsoft's unified analytics platform that consolidates Azure Data Factory, Synapse Analytics, Power BI, and Data Activator into a single SaaS experience built on OneLake (a tenant-level data lake) and Direct Lake mode (BI without data movement). Synapse is the previous-generation platform: it is mature for dedicated SQL pools, but Microsoft is investing roadmap in Fabric. New Microsoft-shop greenfield deployments should default to Fabric.

OneLake is the single tenant-wide data lake that sits underneath Fabric. All Fabric workloads (Lakehouse, Warehouse, KQL DB, BI semantic models) read from and write to OneLake by default, so the same data is available to every workload without copying. Direct Lake mode is Power BI's ability to query OneLake parquet files directly at near-Import-mode speed, with no scheduled refresh and no data duplication. The combination is why Fabric is faster to operate than the Synapse + Power BI stack it replaces.

Microsoft Fabric is the default if you are a Microsoft 365 / Power BI shop, your data volumes are small to mid-size, and BI is the dominant workload. Databricks wins for ML-heavy and multi-cloud customers. Snowflake wins for pure SQL warehousing where operational simplicity matters most. We do not pick a platform on principle: we pick what maps to your existing tooling, your skill mix, and the workloads you actually run. See our platform comparison block above for specifics.

Fabric is sold as F-skus (compute capacity in CU). F2 and F4 are pilot-only. F8 to F16 fit a small production workload. Most mid-market production lands at F32 or F64. Enterprise deployments typically start at F128. Pricing is roughly proportional to CU. Capacity is shared across all Fabric workloads, so the 'what F-sku do I need' question has to factor in BI users, pipeline frequency, ML training, and Real-Time Intelligence usage at the same time. We size capacity as part of the engagement.

Copilot in Fabric provides natural-language assistance across the platform: code generation in notebooks, semantic model creation in Power BI, DAX explanations, and Data Factory pipeline suggestions. It is grounded in your tenant data and respects existing access controls. Safe to enable, but should be paired with three guardrails: tenant-level Copilot access policy, sensitivity label propagation, and a clear data classification scheme so Copilot does not surface restricted data to users who shouldn't see it. We configure all three as part of the rollout.

Yes. Most Synapse SQL pools and notebooks port to Fabric Warehouse and Lakehouse with minimal code changes. The trickier pieces are Synapse Pipelines (rebuild as Fabric Data Factory), Spark pools (rebuild as Fabric Spark), and security model differences (Fabric uses workspace roles plus OneLake ACLs, not Synapse's role-based model). We do these migrations in phases: discovery, Lakehouse build, parallel-run validation, cutover, decommission.

Same playbook. We have moved on-prem SQL Server, SSIS, and SSRS environments to Fabric (typically into Lakehouse for raw + curated, Warehouse for star-schema reporting, and Power BI semantic models on top via Direct Lake). The Microsoft tooling for this is improving fast: SSIS packages can run in Fabric Data Factory in compatibility mode while you rebuild them as native Fabric pipelines.

Yes. Fabric and Power BI are the same product underneath: a Power BI Premium capacity is a Fabric capacity. We design semantic models that take advantage of Direct Lake mode (no scheduled refresh, no Import-mode duplication), and we wire Copilot in Power BI plus Power BI Pulse-style features into the deployment. See our /partners/power-bi page for our Power BI work specifically.

Microsoft Fabric is the default platform if you live in Microsoft 365 and Power BI. We design the OneLake foundation, choose Lakehouse versus Warehouse for each workload, set up Direct Lake mode so Power BI runs without scheduled refresh, and put governance guardrails on Copilot in Fabric before you turn it on. We also do the unglamorous parts: F-sku capacity sizing, Synapse migration, and Purview integration.

One tenant-wide data lake that every Fabric workload reads from and writes to. No more shipping the same dataset to a Synapse pool, a Power BI dataset, and an ML feature store.

Power BI queries OneLake parquet files at near-Import-mode speed. No scheduled refresh, no Import-mode data duplication, no DirectQuery latency.

Code generation in notebooks, semantic model creation in Power BI, DAX explanations, pipeline suggestions in Data Factory. Grounded in tenant data and respects existing access controls.

One capacity covers data engineering, warehousing, BI, real-time intelligence, and AI. No per-workload SKU math. See capacity sizing below.

We do not pick a platform on principle. We pick what fits your existing tooling, your skill mix, and the workloads you actually run. Here is how the four serious 2026 contenders compare for a Microsoft-leaning enterprise.

F-skus measure compute capacity in CU (Capacity Units). One F-sku covers every Fabric workload at the same time, so sizing has to factor in BI users, pipeline frequency, ML training, and Real-Time Intelligence usage together. Microsoft documents this poorly. The shortcut:

We size capacity as part of the engagement: a sizing exercise on assumed workloads almost always under-provisions for the first six months of real usage. Better to start at F32 or F64 and resize than to buy F8 and throttle every BI user.

The clean Lakehouse + Warehouse layer underneath a Fabric deployment. Pipelines, modeling, quality.

Metric certification, Purview integration, and Copilot-safe data classification on top of Fabric.

Platform rationalization across BI tools. Same exercise we run when a customer is choosing between Fabric and an existing stack.

30-day audit of your current data stack. The most common output is a Fabric or Fabric-plus-Databricks recommendation with sized capacity and a 90-day migration plan.

Start with a 30-day Analytics Truth Audit. We map your current data estate, recommend Fabric scope and capacity, and ship a 90-day delivery plan. Senior consultants on every engagement, fixed deliverables, no junior staffing.