Data & Analytics

Microsoft Fabric

One Lake, One Analytics Platform

Unified data platform — engineering, warehousing, real-time analytics and Power BI on one foundation.

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Key Capabilities

What Microsoft Fabric Delivers

OneLake — a single tenant-wide data lake, so data is stored once and read by every workload

Data Factory pipelines for ingestion from line-of-business systems, files and APIs

Data Engineering and Data Warehouse for transformation and modelling at scale

Real-Time Intelligence for event and streaming data that cannot wait for a nightly load

Data Science workspaces for forecasting and modelling against governed data

Power BI native on the same foundation — no separate refresh or duplicated dataset

Purview integration for lineage, sensitivity labelling and governance

Capacity-based licensing, so workloads share one purchased capacity rather than separate SKUs

Who this is for

  • Organizations whose analytics estate has accumulated — a warehouse, several pipeline tools, a lake nobody fully trusts and Power BI reading from all of them.
  • Teams where the same data is copied into several places and the copies have diverged.
  • Organizations needing operational and analytical reporting from one governed source rather than two contradictory ones.
  • It is over-specified for a small organization with two data sources and modest volumes, where Power BI against those sources is the proportionate answer.

What a Fabric engagement covers

Fabric is a platform decision, not a report. The engagement is about consolidating where data lives and who governs it, with reporting as the outcome rather than the scope.

  • Assessment of the current estate — sources, volumes, existing warehouse and Power BI footprint, and what is genuinely painful.
  • Capacity sizing, because Fabric is licensed by capacity and both over- and under-provisioning are expensive in different ways.
  • OneLake and workspace architecture, including how domains and workspaces map to your organizational structure.
  • Ingestion pipelines from line-of-business systems, files and APIs.
  • Semantic models and governed datasets so reporting is built on one definition.
  • Governance — lineage, sensitivity labelling and access, integrated with Purview where you use it.

How the engagement runs

  1. 1

    Estate assessment

    What exists, what it costs, and where the actual pain is. Sometimes the conclusion is that Fabric is not yet justified, and we would rather reach that in assessment than after purchase.

  2. 2

    Architecture and capacity design

    Workspace structure, storage model and capacity sizing, with a migration path from your existing Power BI and warehouse rather than a parallel estate.

  3. 3

    Foundation build

    OneLake, workspaces, governance and the first pipelines established as the pattern everything else follows.

  4. 4

    Workload migration

    Existing reporting and pipelines move in waves, validated against known numbers at each step.

  5. 5

    Enablement

    Your data team takes over operation, because a platform your team cannot run is a dependency rather than an asset.

What you need in place

  • A data team, or a plan to have one. Fabric is a platform that needs operating; it is not a report you commission and receive.
  • Access to source systems and their owners.
  • A realistic view of volumes and refresh requirements, which drive capacity sizing and therefore cost.
  • A governance position on who may access what, ideally before rather than after everything is in one lake.

Working with Sibasi on Fabric

Do we need Fabric, or is Power BI enough?

Power BI is enough for a great many organizations, and we will say so. Fabric earns its place when the number of sources, the volume of data, the need for real-time alongside batch, or the cost of maintaining several overlapping tools becomes the problem — rather than the reporting layer itself. The assessment phase exists to answer this honestly, including when the answer is that you are not there yet.

What happens to our existing Power BI work?

It carries forward. Power BI is a native workload within Fabric rather than a separate product, so existing reports and datasets have a migration path instead of being rebuilt. The work is in moving the data underneath them onto OneLake and pointing the semantic models at governed sources, which is also where the benefit is.

How does the capacity licensing work in practice?

Fabric is licensed by capacity shared across workloads rather than per product, which is an advantage when several workloads share it and a trap when capacity is sized on optimistic assumptions. Sizing is done against your real volumes and refresh patterns during design, and it is worth revisiting after the first months of actual use rather than fixing at the initial estimate.

Is this a rip-and-replace of our warehouse?

It does not have to be, and usually should not be at the start. Workloads move in waves, validated against numbers you already know, with the existing warehouse running until its consumers have moved. A simultaneous cutover of an entire analytics estate is a risk with no corresponding benefit.

Let's put Microsoft Fabric to work for you

Talk to our specialists about your goals, timeline and budget.

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