Databricks when the estate needs it,
Microsoft Fabric when it does not.
We are Microsoft-first — Microsoft Fabric, Power BI and Azure. We build on Azure Databricks when the work calls for it: a real data-science team, production ML, or large-scale Spark engineering. Where a reporting-led operation is better served by Microsoft Fabric, we say so. Fabric and Databricks interoperate through OneLake shortcuts and Delta Lake.
Lakehouse + ML
Azure Databricks · Microsoft Fabric
Power BI · Direct Lake
+ GovernedMedallion · Delta Lake
Gold · Business-ready
Silver · Conformed
Bronze · Raw ingest
MLflow model · notebook
Training run · registered
OneLake · Delta Lake — one shared copy, no duplication
Who this is for
Databricks consulting built around your role
Azure Databricks and lakehouse work for data, platform, operations and finance leaders — the workload each one owns, mapped to what an honest platform decision changes.
Head of Data / Data Science Lead
The problem
Your team builds models in notebooks, but there is no reproducible training pipeline, no MLflow tracking and no serving path, so every promising model stalls before it reaches production.
What we build
Production ML on Azure Databricks — MLflow tracking, a model registry, scheduled feature pipelines and a serving path — with drift monitoring so a failing model is visible before the business notices.
Models that run in production, not just in a notebook
IT Head / Platform Architect
The problem
Two teams span up Databricks workspaces independently, clusters run around the clock with no cost owner, and you have no single view of who can read what across the estate.
What we build
Unity Catalog as one governance layer across workspaces, with access controls, lineage, cluster policies and a cost owner per workspace configured from the start rather than retrofitted.
One governance and cost model across the estate
Operations Director
The problem
The data science team ships models, but your operations review still runs on spreadsheet exports because nothing puts a governed, business-facing number on top of the science.
What we build
A governed Power BI layer reading Databricks Delta Lake tables through OneLake shortcuts on Direct Lake, so operations reads one reconciled number instead of exporting to a spreadsheet.
A governed number the operations review can act on
CFO
The problem
The Databricks bill grows every quarter with no accountable line, and you cannot tell whether the spend is buying production capability or idle clusters.
What we build
Cluster policies, autoscaling sized to the workload and a cost owner per workspace, with an honest read on whether Azure Databricks or Microsoft Fabric is the right-cost platform for each workload.
A Databricks bill with an accountable owner
The Problem
Patterns we see in Databricks estates
Azure Databricks is a strong platform in the right hands. The trouble starts when it is run without a plan — or bought for a reporting job Microsoft Fabric would do with less overhead.
A Databricks estate nobody planned.
A data scientist span up a workspace, then another team span up a second, and now clusters run around the clock with no cost owner and no shared governance. The bill grows and nobody can say which notebooks still matter.
Models that work in a notebook and nowhere else.
The model trains fine on a laptop or in an ad-hoc notebook, but there is no MLflow tracking, no reproducible pipeline and no serving path. Every re-run is manual and every result is hard to defend to the business.
Spark jobs written once and never revisited.
The engineering runs, but shuffles spill to disk, small files pile up in the Delta Lake tables and jobs that should take minutes take an hour. The team keeps adding cluster size instead of fixing the pipeline.
No governed reporting on top of the science.
The data science team ships models and features, but the business still exports to spreadsheets because there is no governed semantic model. The value stays trapped in notebooks the operations team never opens.
What we build
What we build
Six engagements. Each one either puts Azure Databricks to work where it earns its place, or draws the honest line to where Microsoft Fabric fits better.
Databricks vs Fabric — an honest fit assessment
Replaces
The assumption that a data-science platform is the right answer for a reporting-led operation.
- A read of your actual workloads — reporting, engineering, data science, ML
- Where Azure Databricks earns its place and where Microsoft Fabric is the better fit
- The cost and operational-overhead difference for your team size and skills
- A written recommendation you keep, whichever way it points
A platform decision made on your workloads, not on a vendor preference.
Production ML on Azure Databricks
Replaces
The model that only runs when its author runs it by hand.
- MLflow tracking, model registry and reproducible training pipelines
- A serving path — batch scoring or real-time endpoint — that the business can rely on
- Feature pipelines that refresh on a schedule, not on a person
- Monitoring so model drift is visible before the business notices
Models move from a notebook to a governed, repeatable production workload.
Spark data engineering, rebuilt clean
Replaces
The Spark job that grew a bigger cluster every quarter instead of getting fixed.
- Delta Lake tables optimised — compaction, partitioning and Z-ordering where they pay off
- Pipelines profiled and rewritten to cut shuffle spill and small-file overhead
- Cluster policies and autoscaling sized to the workload, not to the last incident
- A medallion structure so raw, conformed and business-ready data stay separate
Engineering that runs faster on smaller clusters — and stays that way.
Unity Catalog governance across the estate
Replaces
Two teams, two workspaces and no shared view of who can read what.
- Unity Catalog set up as the single governance layer across workspaces
- Access controls, lineage and audit configured from the start, not retrofitted
- A cost owner and cluster policy per workspace so the bill has an accountable line
- Sensitive data classified and controlled at the catalogue level
One governance and cost model across the whole Databricks estate.
Governed Power BI on top of Databricks
Replaces
The spreadsheet export that undoes everything the data science team built.
- Databricks Delta Lake tables surfaced to Microsoft Fabric via OneLake shortcuts
- Power BI reading on Direct Lake — no second copy, no scheduled refresh
- A governed semantic model so the business reads one reconciled number
- Engineering and ML stay in Databricks; reporting lives where the business works
The science reaches the operations team through a governed reporting layer.
Fabric and Databricks interoperability plan
Replaces
The false choice of migrating everything to one platform.
- A map of which workloads stay in Azure Databricks and which sit in Microsoft Fabric
- OneLake shortcuts and open Delta Lake as the shared, no-copy data layer
- A boundary that keeps data science in Databricks and reporting in Fabric
- A phased plan that avoids a rip-and-replace migration
Both platforms run where each is strongest, on one shared copy of the data.
How we work
From workload review to first value in 6 weeks
We start with the workloads, not the platform. The recommendation follows what your data science, engineering and reporting actually need.
01
Review — read the workloads
Two weeks. We read your actual workloads — reporting, engineering, data science and ML — and your current Azure Databricks or Microsoft Fabric footprint. We name where Databricks earns its place and where Fabric is the better fit.
02
Prototype — harden the first slice
Two to three weeks. We build the first working slice — a production ML workload with MLflow, a rebuilt Spark pipeline, or a governed OneLake and Power BI layer over the existing estate — on your real data.
03
Deploy — govern and interoperate
Three to five weeks. We set up Unity Catalog governance, cluster policies and cost ownership, wire OneLake shortcuts so Fabric and Databricks share one Delta copy, and hand over documented, monitored workloads.
Technology stack
Azure Databricks
Microsoft Fabric
Machine Learning
Data Engineering
Azure Platform
Sources
Why MyData Insights
Why choose MDI for Databricks consulting
Microsoft-first, and honest about it
Microsoft Fabric, Power BI and Azure are our core stack. We build on Azure Databricks when the estate needs it and we say so when Microsoft Fabric is the better, cheaper fit — we do not sell a data-science platform to a reporting-led operation.
Databricks where it earns its place
A real data-science team, production ML, or large-scale Spark engineering as a standing capability — that is where Azure Databricks pays for itself, and where we build on it without apology.
Fabric and Databricks interoperate
Through OneLake shortcuts and the open Delta Lake format, a table written by one is readable by the other with no copy — so both platforms run where each is strongest on one shared dataset.
Governed reporting on top of the science
We put a governed semantic model and Power BI on Direct Lake over your Databricks estate, so models and features reach the operations team instead of staying trapped in notebooks.
No rip-and-replace migration
If you already run Azure Databricks, we keep engineering and ML where they belong and add the governed reporting layer around them, rather than forcing a move to a single platform.
First value in six weeks
A working slice on your real data in six weeks on a fixed scope — one production ML workload, one rebuilt Spark pipeline, or a governed OneLake and Power BI layer — not a discovery deck to review.
Common questions
What buyers ask us
Is MyData Insights a Databricks specialist or a Microsoft partner?
We are Microsoft-first — Microsoft Fabric, Power BI and Azure are our core stack. We build on Azure Databricks when the estate genuinely needs it: a real data-science team, production ML, or large-scale Spark engineering as a standing capability. We are honest about that boundary rather than selling Databricks to a reporting-led mid-market operation that Microsoft Fabric would serve better and more cheaply.
When does Azure Databricks earn its place over Microsoft Fabric?
Azure Databricks earns its place when you have data scientists working in notebooks day to day, production ML models that need MLflow tracking and serving, Unity Catalog governance across many workspaces, or Spark engineering at a scale where cluster tuning is a core skill on your team. If your main need is governed reporting and a few models, Microsoft Fabric usually does the job with less operational overhead.
We already run an Azure Databricks estate. Can you get governed Power BI reporting on top of it?
Yes — this is a common engagement. Your Databricks Delta Lake tables can surface to Microsoft Fabric through OneLake shortcuts, so Power BI reads them on Direct Lake without a second copy of the data. We keep engineering and ML in Databricks where they belong and put a governed semantic model and reporting layer on top, rather than forcing a migration.
Do Microsoft Fabric and Azure Databricks work together, or do we have to choose?
They interoperate through OneLake shortcuts and the open Delta Lake format, so a table written by one is readable by the other with no copy. Many estates run both — Azure Databricks for data science and heavy engineering, Microsoft Fabric for the governed reporting and business-facing analytics. You do not have to pick one for the whole organisation.
How quickly do we see something working?
Our cadence is first value in 6 weeks — a working slice on your real data, not a slide deck. For a Databricks engagement that is typically one production ML workload hardened, or one Spark pipeline rebuilt cleanly, or a governed OneLake and Power BI layer landed over an existing Databricks estate. We scope the exact first slice after a short review of your workloads.
Engagement
Free Databricks & Lakehouse Review
Thirty minutes with Amit on your ML and engineering workloads: what runs on Azure Databricks, what belongs on Microsoft Fabric, and how the two interoperate through OneLake and Delta Lake. No slides, no obligation.
What you get
- A read of your ML and Spark engineering workloads
- An honest call on where Azure Databricks fits and where Microsoft Fabric wins
- An interoperability plan via OneLake shortcuts and open Delta Lake
- A cost and governance view of your current estate
- A 6-week first-value plan you keep