What is a lakehouse, exactly?
The term gets used loosely. At its core, a lakehouse is a data architecture that combines the low-cost, flexible storage of a data lake with the structure, performance and governance features of a data warehouse — in a single system.
Traditional architectures forced a trade-off. Data lakes were cheap and flexible but messy — no schema, no transactions, hard to query reliably. Data warehouses were structured and fast but expensive, rigid, and required moving data out of the lake just to run analytics on it.
The lakehouse collapses that into one layer: open-format storage (Delta Lake or Iceberg), with ACID transactions, schema enforcement and optimised query performance built on top.
How it works in Microsoft Fabric
In Microsoft Fabric, the lakehouse is built on OneLake — a single, tenant-wide storage layer that underpins everything. You don't move data between Fabric services; they all read from the same store.
- Data lands in OneLake in Delta Lake format
- Power BI connects via Direct Lake mode — no import, no copy, no scheduled refresh
- PySpark, SQL, and Data Wrangler all work on the same tables
- Microsoft Purview handles lineage and governance across the whole workspace
The practical effect: a BI developer and a data engineer can work in the same workspace, on the same data, without any integration layer between them.
"The lakehouse isn't a new product — it's the removal of an unnecessary boundary between where you store data and where you analyse it."
When it makes sense for mid-market companies
Not every company needs a lakehouse. If you have a single data source and 10 reports, a well-structured Power BI Premium model is probably enough. The lakehouse pattern earns its complexity when:
- You have multiple source systems that need to be combined (ERP + CRM + flat files)
- Different teams need different views of the same underlying data
- You have both operational and analytical queries hitting the same data
- Data volumes are growing and warehouse storage costs are becoming a line item
- You need auditability — who changed what, when
Common mistakes in lakehouse implementations
Most failed lakehouses fail for the same reasons.
1. No medallion architecture
Raw data lands in the same layer as certified, cleaned data. Within 6 months, no one knows which tables are safe to use in a report. The fix is a bronze/silver/gold structure from day one — raw ingestion, cleaned and conformed, and business-ready certified data in separate layers.
2. Too much data, too few consumers
Teams ingest everything "because we might need it later." Storage is cheap but maintenance isn't. Start with the data that answers a known business question.
3. No semantic model
Reports are built directly on raw tables. Every developer writes their own version of "revenue." The semantic model is where you define metrics once, certify them, and prevent the organisation from having 12 different answers to the same question.
Thinking about moving to Microsoft Fabric? We run a 5-day Migration Audit that maps your current stack, identifies readiness gaps and produces a written roadmap. Get in touch to scope it.
Getting started without over-engineering it
The pragmatic approach for a mid-market company with limited data engineering resource:
- Pick one business domain and one source system. Don't boil the ocean.
- Set up a Fabric workspace with a simple three-layer lakehouse (bronze, silver, gold).
- Build one Dataflow Gen2 pipeline to land raw data in bronze.
- Write a simple notebook to clean and conform data into silver.
- Build a semantic model on gold and connect Power BI to it via Direct Lake.
- Ship one report. Get feedback. Expand from there.
The goal of the first implementation isn't to build a complete platform — it's to prove the pattern works for your organisation, with your data, with your team.
If you're evaluating whether a lakehouse makes sense for your stack, we offer a free 30-minute scoping call. No pitch — we'll tell you honestly what we'd do in your position.
