One Lakehouse.
Every Data Source.
Zero Pipeline Tax.
MDI implements data lakehouse architecture on Microsoft Fabric — replacing fragmented Azure data estates with a single OneLake, Delta Lake format throughout, and a medallion structure your team can actually maintain. Real-time. Governed. Production-grade.
Trusted by operations teams at
The Problem
Why most data estates break at scale
These are the four patterns we see in almost every organisation that comes to us for a lakehouse build. Usually all four, not just one.
Six Azure services doing the job of one
A Blob container for raw files. A Synapse workspace for transformation. A dedicated data warehouse for reporting. An ADF instance nobody fully owns. And a Power BI dataset that nobody can explain. The fragmentation costs money and creates maintenance overhead that compounds every quarter.
Batch reports, not real-time decisions
When your data sits in a warehouse built for nightly loads, your operational decisions are always running on yesterday's data. Plant managers, supply chain directors, and finance teams all need live operational intelligence — not a report that closed at midnight.
Data engineers spend 70% of time on pipeline fixes
Brittle ETL pipelines that break on schema changes. Data quality issues that surface three layers downstream. Source systems that nobody documented properly. If your data team's backlog is dominated by pipeline fixes rather than analytics delivery, the foundation is wrong.
ML and AI projects fail for the same reason every time
Machine learning models need clean, labelled, historic data at scale. If your data is in a warehouse or scattered across flat files, standing up a data science workload requires a separate infrastructure build before a single model gets trained. The lakehouse eliminates that barrier.
What changes
What a production lakehouse actually delivers
One platform, all workloads
SQL analytics, Python ML, real-time streaming, and Power BI Direct Lake — all from the same OneLake. No data movement between tools.
Sub-second Power BI queries
Direct Lake mode reads Delta Lake files directly from OneLake. No import, no DirectQuery overhead. Full dataset at full speed.
Real-time operational data
Fabric Eventstream and Mirroring bring live data from SAP, ERP, IoT, and databases into the lakehouse in seconds — not hours.
Governance built in, not bolted on
Unity Catalog / Purview-connected lineage, role-based access, and column-level security enforced at the storage layer — not the BI layer.
80% lower storage cost
Delta Lake on OneLake costs a fraction of a proprietary data warehouse. Compression, Z-ordering, and vacuum keep storage lean as data volumes grow.
Data engineering team freed up
When pipelines stop breaking, engineers stop fixing them. Fabric Pipelines with auto-retry, schema drift handling, and monitoring reduce operational overhead significantly.
How we work
From audit to first data product in 6 weeks
01
Data estate audit — what you have, what it costs, where it breaks
Before we design anything, we map your current data sources, understand your query patterns, and calculate what your existing infrastructure actually costs to run. We need to know where the pain is before we prescribe the architecture.
02
Lakehouse architecture and migration plan
We design the OneLake structure, medallion layer definitions (bronze → silver → gold), Delta Lake schema, and governance model. We build the migration plan showing which workloads move first, what the cutover sequence looks like, and how business continuity is maintained throughout.
03
Build, connect, govern — first data product in 6 weeks
We connect your priority source systems — ERP, MES, SAP, IoT — into the bronze layer, run transformations into silver, and build the first governed semantic model and Power BI dataset on gold. You have a working, production-grade data product within 6 weeks. Then we scale.
Technology stack
Platform
Format
Processing
Orchestration
Analytics
Source Systems
Common questions
What buyers ask us
What is a data lakehouse and how does it differ from a data warehouse?
A data lakehouse combines the low-cost storage of a data lake with the query performance and governance of a data warehouse. Unlike a traditional warehouse, it stores data in open formats (Delta Lake / Parquet) on cloud storage — so you can run structured SQL analytics, machine learning, and real-time streaming from the same platform. Microsoft Fabric delivers this as OneLake, with Delta Lake format throughout and Power BI Direct Lake for sub-second query response without data movement.
How long does a data lakehouse implementation take?
A working data lakehouse — with bronze, silver, and gold layers, 2-3 source systems connected, and a Power BI semantic layer on top — takes 6-10 weeks. Full enterprise deployment with SAP integration, IoT feeds, and AI workloads is typically 14-20 weeks depending on data volume and source system complexity. We scope by data volume, number of sources, and governance requirements, not by hours.
Does our team need to learn new tools to use a lakehouse?
No. On Microsoft Fabric, business users continue to use Power BI. Data engineers use familiar tools — Spark, SQL, Python, Data Factory pipelines. The lakehouse layer is infrastructure that sits behind the tools your team already knows. We build the platform, govern it, and train your team to extend it — you do not need to hire a data engineering team to maintain it.
We already have a data warehouse — do we need to replace it?
Not necessarily. We assess what you have first. If your warehouse is performant and well-governed, we build the lakehouse alongside it — handling the raw data ingestion, transformation, and AI workloads that your warehouse was never designed for. If your warehouse is costing too much and struggling with volume, we plan a phased migration. We do not recommend rip-and-replace without first understanding your specific data volumes, query patterns, and team capability.
Can you integrate SAP with a Microsoft Fabric lakehouse?
Yes — this is one of our core capabilities. We integrate SAP B1, SAP ByDesign, SAP S/4HANA, and SAP ECC into Microsoft Fabric lakehouse using Azure Data Factory, Fabric Pipelines, and SAP OData connectors. Data lands in the bronze layer in real time or near-real time, transforms through silver, and surfaces in the gold layer as governed Power BI datasets. Your SAP data stops being a reporting bottleneck.
Ready to move
Start with a data estate audit, not a sales call
First call is 45 minutes. Bring your source systems list and your biggest data headache. We'll tell you exactly what the lakehouse architecture looks like for your environment — and what it would cost to build it.