One Lakehouse.
Every Data Source.
Zero Pipeline Tax.

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.

A working data lakehouse — with bronze, silver, and gold layers, 2-3 source systems connected, and a Power BI semantic layer on top — takes 8-10 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.

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.

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.

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.

Delta Lake is the open table format that gives a data lake the reliability of a warehouse: ACID transactions, schema enforcement, time travel, and fast upserts on top of Parquet files. In Microsoft Fabric every lakehouse table is Delta, stored in OneLake, so the same data supports SQL analytics, Spark, and Power BI Direct Lake. It is what makes a lakehouse trustworthy enough to run operational reporting on.

A data lake stores raw files cheaply but has no transactions, schema, or governance — it becomes a "data swamp" nobody trusts. A lakehouse adds the Delta Lake table layer on top, so the same low-cost storage now supports governed SQL, BI, and ML. For a manufacturer it means MES, ERP, and IoT data can land cheaply and still be query-ready and auditable.

Medallion structures the lakehouse in three layers: bronze holds raw data as ingested, silver holds cleaned and conformed tables, gold holds business-ready models. In Microsoft Fabric each layer is Delta tables in OneLake, so a raw SCADA feed becomes validated downtime records and then a governed OEE model. The pattern keeps lineage auditable and isolates failures to one layer.

OneLake is the single, tenant-wide lake in Microsoft Fabric — one storage layer in Delta format that every workload reads from. Because engineering, analytics, and AI all read the same copy, there is one version of each table rather than copies scattered across services. That single governed copy is what lets finance, operations, and supply chain quote the same number.

Direct Lake lets Power BI read Delta tables in OneLake directly, with no import and no scheduled refresh, while keeping query speed close to an in-memory model. The report reflects new data as it lands in the lakehouse. It removes the refresh window that breaks large Import models.

Fabric is the successor to Synapse, so the warehouse, Spark, and pipeline workloads move into one platform on OneLake. We assess each Synapse artefact, re-platform the warehouse and notebooks to Fabric, and point Power BI at Direct Lake. The data movement Synapse required between stages disappears because everything reads the same Delta tables.

Map each Azure Data Factory pipeline to its source, transformation, and destination, then rebuild as Fabric Dataflows Gen2 or Data Pipelines, landing output as Delta in OneLake. Where the source supports it, replace the pipeline with Fabric Mirroring. We validate row counts against the old output before cutover.

We assess the existing warehouse first — query patterns, volumes, cost — then plan a phased migration: land sources in bronze, rebuild the conformed model in silver and gold, and run the lakehouse in parallel before cutover. We do not rip and replace; the warehouse keeps running until the lakehouse proves the numbers. A full migration is typically 8-10 weeks for a manufacturer, longer for high-volume enterprise estates.

Yes. If the warehouse is performant and governed, we build the lakehouse beside it to handle the raw ingestion, ML, and real-time workloads it was never designed for. The two coexist on OneLake, and we migrate selectively only where the business case is clear. Rip-and-replace is rarely the right first move.

OneLake can shortcut to existing ADLS Gen2 storage, so data appears in Fabric without a physical copy and current pipelines keep running. You migrate workloads onto OneLake incrementally, repointing pipelines one at a time rather than in a big-bang cutover. This keeps existing jobs live while the lakehouse takes over.

We map and prioritise sources, set up OneLake and the medallion layers, connect the priority source (ERP/SAP first) into bronze, conform it through silver, and build the first governed model and Power BI report on gold. The first data product is live in 6 weeks; production at 8. Then we add sources and workloads layer by layer.

A Fabric lakehouse connects to ERP and SAP (S/4HANA, ByDesign, B1), Dynamics 365, Oracle, NetSuite, SQL Server, WMS/TMS, IoT and SCADA via Eventstream, files, and REST APIs. Ingestion uses Mirroring, Azure Data Factory, Dataflows Gen2, or shortcuts depending on the source. Everything lands as Delta in OneLake regardless of origin.

Scope tightly to one decision, then run the build in steps with a named tool at each. Week 1: stand up the Microsoft Fabric workspace and OneLake, and confirm the priority source. Weeks 1-2: Azure Data Factory pipelines or Dataflows Gen2 land that source into the bronze layer as Delta Parquet. Weeks 2-3: Fabric notebooks (PySpark) conform bronze into a clean silver layer and a gold star schema built for the one decision. Weeks 3-4: a Direct Lake semantic model sits on gold and the Power BI report is built against it. Weeks 5-6: reconcile every number to source and hand to the operations owner who makes that decision. First value in 6 weeks, production at 8.

A lakehouse replaces a stack of separate services — blob storage, a transformation engine, a warehouse, and ADF jobs — with one platform on OneLake, so there are far fewer moving parts to break. Mirroring removes hand-built extract pipelines, and the medallion pattern localises failures. Fewer brittle pipelines means data engineers build new products instead of firefighting schema changes.

Machine learning needs clean, labelled, historic data at scale — and most of it sits in a warehouse built for reporting or scattered across flat files. Without a lakehouse, every model needs a separate infrastructure build before training starts, which is where projects stall. A lakehouse keeps governed historic data in one place, so the model trains on the same Delta tables the reports use.

Yes. Fabric Data Science runs notebooks, MLflow experiment tracking, and model scoring inside the same workspace as the lakehouse and Power BI — one OneLake copy of the data, no export to a separate ML stack. Delta time travel gives the point-in-time snapshots a model needs, so it is not trained on data that leaked from the future. For document intelligence and copilots we add Azure OpenAI and Copilot Studio on the same governed tables. You add a second platform when scale or a specific framework demands it, not by default.

Land every source in OneLake and conform it once in the gold layer, with each measure — margin, OTIF, inventory cover — defined a single time. Because finance, operations, and supply chain all read that one governed model, they stop arriving at meetings with different numbers. The argument moves from whose figure is right to what to do about it.

A lakehouse stores data as Delta Parquet on low-cost object storage in OneLake, typically far cheaper per terabyte than a traditional warehouse that bundles compute and proprietary storage. You also stop paying to duplicate data across separate services. Compute is billed through Fabric capacity separately, so you scale storage and compute independently.

Connect SAP S/4HANA through OData, CDS views, or Fabric Mirroring, landing data in the bronze layer of OneLake on a governed schedule — no CSV exports. It conforms through silver and surfaces as governed Power BI datasets on gold. This is one of our core integrations and stays within standard SAP licensing.

Yes. SAP Business One connects via its SQL database (HANA or SQL Server) using Azure Data Factory or the On-Premises Data Gateway, with incremental extraction to keep the B1 server light. Data lands as Delta in OneLake and reports through Power BI on Direct Lake. No manual exports.

Land ERP and WMS data in bronze, then conform them in the silver layer to shared keys — items, locations, orders — so they join cleanly in the gold model. One governed lakehouse layer then serves inventory, OTIF, and fulfilment from a single set of definitions. That is what turns two systems into one operational picture.