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FabricCapacityFinOps

Fabric Capacity Review

Are you paying for the right Fabric capacity — and using it wisely?

A focused review of capacity consumption, workload behaviour and optimisation opportunities before the answer becomes “buy a bigger SKU”.

Start with what is actually happening.

  • Capacity peaks appear without an obvious explanation.
  • Background operations consume more than expected.
  • Refreshes collide or run at inconvenient times.
  • Some workloads behave well individually but poorly together.
  • The first proposed fix is to increase the capacity size.

Good work starts with better questions.

01

What actually consumes your capacity?

02

Which workloads are causing avoidable pressure?

03

Is a larger capacity really necessary?

04

Where can scheduling, modelling or architecture reduce consumption?

The scope follows the problem.

I do not force every engagement through the same checklist. These are the areas that may matter; the agreed scope determines how deep we go.

Capacity Metrics and workload patterns

Interactive vs background operations

Semantic-model refresh behaviour

Dataflows, notebooks and pipelines

Scheduling conflicts

Bursting and smoothing behaviour

Workload distribution across environments

Opportunities to reduce recurring consumption

Something you can use after the conversation ends.

The output should help you make a decision, change a practice or move the work forward. It should not exist simply to prove that work happened.

01

Capacity Consumption Review

A readable view of the main consumption patterns and pressure points.

02

High-Cost Workload Findings

The operations most worth investigating or changing.

03

Optimisation Opportunities

Concrete ways to reduce or redistribute consumption.

04

Scheduling Recommendations

Where workload timing can reduce avoidable contention.

05

Capacity Decision Guidance

A more informed basis for deciding whether a capacity change is justified.

Enough structure to make the work clear. Not so much process that the process becomes the work.

01

Context

We start with a short conversation about what is happening, what matters and what has already been tried.

02

Review

I analyse the relevant parts of the solution or operating model at the depth agreed for the engagement.

03

Prioritise

I organise observations around impact, risk and usefulness — not around how many issues I can find.

04

Walkthrough

We go through the findings together, challenge assumptions and discuss trade-offs.

05

Next step

You decide what happens next: implement internally, continue together or stop because the focused engagement was enough.

I keep the client-side time demand deliberately light. A focused assessment usually needs a kickoff conversation, access or materials, and a final review session. We agree the exact involvement before we start.

This is likely to help if...

  • Capacity usage is growing faster than your confidence in what drives it.
  • You are considering resizing and want evidence first.
  • You want to connect technical optimisation with cost awareness.
  • You only need procurement support for a licensing transaction.
  • There is no meaningful workload history to analyse yet.

If the problem is real but the format is wrong, that is useful to know early. I would rather redirect the conversation than force the challenge into the wrong service.

Are you paying for the right Fabric capacity — and using it wisely?

Bring me the problem, not a polished brief. A few sentences about what is happening and what you would like to change are enough to start.

Tell me what's happening

Send me a message.

You do not need a polished brief. Whether the topic is Power BI, Fabric, AI, team training, mentoring, coaching or simply a challenge that needs untangling, leave a few details and we can start from there.

SIGNALMESSAGECONVERSATION

AI & TRANSPARENCY

AI supports selected parts of my work — it does not replace my judgement, responsibility or relationship with the client.

I use AI tools for tasks such as research, organising information, structuring content and preparing materials. I treat AI as support for my work — not as an autonomous author or decision-maker. Anything I publish or use professionally remains my responsibility.

In coaching, I do not use AI to automatically assess, diagnose or make decisions about a client. AI does not replace attention, confidentiality or human responsibility for the coaching relationship.

AI may support the process. Human responsibility remains.