Mandelbulb Technologies
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Optimization on a cadence,
not a rescue.

Pay for the work your estate does, measured on your own workloads, with a cost baseline your team keeps.

  • The bill, read

    Spend traced to the workloads that drive it.

  • Your workloads, benchmarked

    Measured on your estate before anything changes.

  • A baseline you keep

    Your team revisits it as the workloads change.

Book a 30-Minute Working Session

Health & Cost Check in two weeks · Fixed scope · The dashboard is yours

Spend by workloadBaselineDriftRecoveredNext workload

Keep the estate tuned.
Make the baseline a habit.

WindowBaselineMisses windowHolds window

Capacity added for a deadline stays after it passes, and jobs keep running after their reports fall out of use. Slowly, the bill and the value it carries drift apart.

  • Measured, not guessed

    We benchmark your workloads on your estate before we change anything.

  • The window still holds

    A cheaper run that misses its operating window is not a saving.

  • A habit, not a rescue

    The baseline stays with your platform team, to revisit as workloads change.

This is the right engagement when

  • The Databricks or Fabric bill grows faster than the work it carries.
  • Nobody can say which workloads drive the spend.
  • Capacity was added for a deadline and never came back down.
  • Recovered spend needs a measured baseline before it funds the next bet.

Read the estate.
Tune the workloads.

Start with the two-week Health & Cost Check, or bring a known workload straight into a platform engagement.

BillWorkloadsDashboard

Lakehouse Health & Cost Check

The way into either platform engagement: bring the bill, benchmark your own workloads, and see what deserves attention first.

What we assess

  • The bill against the work

    Spend set beside the workloads and operating requirements it pays for.

  • Workload benchmarks

    Measured on your estate, so the findings describe your configuration and your data.

What you keep

  • One cost and health view

    A dashboard that ties the bill to the workload evidence.

  • An ordered fix list

    The findings that make the next tuning scope a decision you can defend.

Engagement shape

Scope
One running lakehouse estate, in its Databricks DBU or Fabric CU edition.
Typical duration
Two weeks.
Price posture
Fixed price, fixed scope, priced up front.
What you keep
A cost and health dashboard and the findings behind it.
BeforeAfterRecovered

Databricks DBU Sprint

A two-week, fixed-scope sprint that runs the playbook from Optimizing Databricks Workloads, the Packt book our co-founder wrote, on your running estate.

What we assess

  • Compute and jobs

    Cluster size, autoscaling and runtime fit, including Photon. Spark joins, shuffles, skew and caching.

  • Layout and cost habits

    Delta layout, partitioning and file compaction. Tagging, budgets and DBU-per-workload baselines.

What you keep

  • Measured findings

    Prioritized fixes, with before-and-after benchmarks on your workloads.

  • Cost-governance baseline

    A baseline your platform team can revisit after the sprint.

Engagement shape

Scope
A bounded set of running Databricks workloads and the bill behind them.
Typical duration
Two weeks.
Price posture
Fixed price, fixed scope, priced up front.
What you keep
The findings report, before-and-after benchmarks and cost-governance baseline.
ThrottledBigger SKUTuned

Fabric CU Edition

The same discipline on Microsoft Fabric capacities, with capacity sizing and workload tuning decided together.

What we assess

  • Capacity and workload fit

    Capacity use read against the work it serves, to find where tuning or SKU sizing improves the fit.

  • Governed spend

    The chosen workloads benchmarked against their operating requirements.

What you keep

  • Cost and health dashboard

    The dashboard from the two-week check, with the findings behind it.

  • Prioritized next actions

    The tuning and sizing work to scope next.

Engagement shape

Scope
The CU edition of the Health & Cost Check on a running Fabric estate. Implementation scope follows the findings.
Typical duration
Two weeks for the Health & Cost Check. Tuning is scoped in the first working session.
Price posture
Fixed price, fixed scope, priced up front.
What you keep
The cost and health dashboard and diagnostic findings.

Bill in.
A measured baseline out.

Four stages, and every change is measured against the baseline it started from.

Two weeksLater readsAttributeBaselineTuneProveHand overBillWorkload runsBaseline kept

  1. Tie the platform spend to the workloads that consume it, and agree what the check covers.

    You receive

    Cost baseline

    Platform spend tied to the workloads that consume it.

    Lakehouse Health & Cost Check

What this is not.

This practice tunes an estate that already runs, and stops before the move or the next bet.

The story this practice stands on.

Rows of parked semi-trucks and trailers in a wet freight yard at dusk, under floodlights

FleetEnergies

Fleet & Logistics

Lower operating cost
28%
Lower fleet carbon emissions
40%

FleetEnergies on Microsoft Fabric: Real-Time Fleet Telemetry, 40% Less Carbon

FleetEnergies ran fleet intelligence on 300+ fragmented on-premise databases, and its reports took hours. We consolidated them onto one Microsoft Fabric estate with a real-time telemetry path. Operating cost fell 28%, and load, fuel and empty-ride optimization on the platform cut fleet carbon emissions 40%.

Explore Fleet & Logistics

With the successful implementation on Fabric, it has become an integral part of our new platform supporting advanced data-driven features at scale.

Robin QuittardCEO, FleetEnergies

Partners and recognition.

  • Microsoft 2024 Partner of the Year, Analytics Award Finalist
  • Microsoft Fabric Featured Partner
  • Microsoft Solutions Partner
  • Google Partner
  • Databricks
  • Anthropic
  • OpenAI

Questions buyers ask.

What is data estate optimization?

Data estate optimization tunes a running Databricks or Fabric estate so you pay for the work it does. We tie the platform bill to the workloads that drive it, benchmark those workloads on your estate before changing anything, tune compute, job execution and data layout where the evidence supports it, and hand back the result beside a cost baseline your platform team keeps.

How do you reduce Databricks DBU costs?

We run a two-week, fixed-scope DBU Sprint on your running workloads. It covers cluster size, autoscaling and runtime fit, including Photon; Spark joins, shuffles, skew and caching; Delta layout, partitioning and file compaction; and tagging, budgets and DBU-per-workload baselines. You keep the findings, before-and-after benchmarks and a cost-governance baseline. A saving counts only if the workload still meets its window.

How do you optimize Microsoft Fabric capacity costs?

We read capacity use against the work it serves, then tune workloads and SKU sizing together. The Fabric CU Edition starts as the two-week Health & Cost Check on your running estate, measured on your own workloads, and ends in a cost and health dashboard and prioritized next actions. The tuning that follows is scoped in the first working session from those findings.

How is this different from a one-off cost-cutting exercise?

It measures your own workloads and checks that each still meets its operating window. A cheaper run that misses its window is not a saving, so every change is compared with the baseline it started from. The result is a cost-governance baseline your platform team keeps and revisits as workloads change, which makes optimization a habit rather than a rescue.

What do we need before a Lakehouse Health & Cost Check?

You need a running Databricks or Fabric estate and its bill. The check takes two weeks: we tie the spend to the workloads that consume it, benchmark your own workloads and hand back a cost and health dashboard you keep. A 30-minute working session on your estate, one workload and its bill agrees the scope first.

What comes next.

When the recovered spend needs a plan, the estate returns to Decide. The refreshed cost baseline and the findings cross with it. The question changes from what the estate costs to which bet it should fund next.

This practice

Tune

Data Estate Optimization

When the recovered spend needs a plan

The loop closes here. Decide picks up where Tune ends: the estate is a cycle, not a project.

Bring the bill.
Keep the baseline.

Book a 30-minute working session on your estate, one workload and its bill. It ends in an agreed scope for the check or the tuning.

Book a 30-Minute Working Session