Your Data Is Already There.
It Just Needs to Connect.

A focused single-source integration (e.g. SAP ByDesign into Fabric) typically takes 4–8 weeks from kick-off to production. A full enterprise integration across 5–10 source systems with a medallion architecture build is a 3–4 month engagement. We give a firm timeline after a 1-hour discovery call where we scope the source systems and data volumes.

No. We work with what you have. If ADF pipelines are running and working, we leave them running. We build on top of or alongside your current estate, and migrate selectively where the business case for Fabric Mirroring is clear. We don't rip and replace — we improve incrementally.

Yes. We use the On-Premises Data Gateway for ADF and Fabric connections to on-premise SQL Server, Oracle, and file shares. For plant-floor OT systems (SCADA, PLC, MES), we use Azure IoT Hub or Event Hubs with edge agents where needed.

Every integration we build follows a medallion pattern — raw data in bronze, validated and conformed data in silver, business-ready aggregates in gold. Access controls, sensitivity labels, and lineage tracking are configured from day one, not retrofitted later. Microsoft Purview is configured as part of the workspace setup.

Yes. We offer a retainer model for pipeline monitoring, incident response, schema change management, and new source onboarding. Most clients start with a 3-month post-go-live support agreement and convert to an ongoing retainer once they see what proactive management looks like.

Use Fabric Eventstream to ingest OPC-UA and MQTT feeds from the plant floor, landing them in a lakehouse or KQL database in OneLake. Where an edge gateway is needed, Azure IoT Hub or Event Hubs forwards the signals into Eventstream. The data is queryable within seconds, so a real-time OEE or condition-monitoring dashboard reads live machine state, not an overnight extract.

Fabric Eventstream is the real-time ingestion engine in Microsoft Fabric — it captures high-velocity events from IoT sensors, SCADA tags, Kafka, or Event Hubs and routes them to OneLake, a KQL database, or Power BI. For operational data it replaces a custom streaming pipeline: you configure sources and destinations rather than build and maintain code. It is the layer behind real-time plant-floor and logistics dashboards.

Bridge OPC-UA from the plant with an edge agent — Azure IoT Edge or an OPC-UA-to-MQTT gateway — that forwards tags to Azure IoT Hub or Event Hubs, then into Fabric Eventstream and OneLake. This keeps the OT network isolated while the analytics platform reads a governed copy. Machine signals then join ERP and MES data in the same lakehouse.

Fabric Eventstream makes events queryable in seconds — typically sub-10-second end to end for plant-floor signals into a KQL database. That is fast enough for live line monitoring and threshold alerting, though not a substitute for the millisecond control loop the PLC handles. For most operational dashboards, seconds-fresh is the practical target.

Land ERP and WMS data in OneLake through Fabric Mirroring or Azure Data Factory, stream movement events via Eventstream, then build the dashboard in Power BI on Direct Lake. Dispatchers see OTIF risk and dwell time as shipments move, not in a next-day report. Fabric Activator can raise an alert when a shipment breaches its window.

Replace nightly extracts with Fabric Mirroring, which replicates the source database into OneLake continuously, and Direct Lake, which lets Power BI read the live Delta tables with no refresh. The batch window disappears because data is never loaded once a day — it is always current. Reports reflect the running shift instead of last night.

Connect SAP ByDesign through its standard OData services and custom analytical queries, pulled into OneLake via Azure Data Factory or Fabric pipelines — no ABAP development required. Data lands in the bronze layer, conforms through silver, and surfaces as governed Power BI datasets. This stays within standard SAP licensing and survives ByD updates.

Connect SAP S/4HANA through OData, CDS views, or Fabric Mirroring rather than manual CSV exports, so data flows on a governed schedule into OneLake. Power BI reads it on Direct Lake, current and reconciled to SAP. The CSV habit ends — and with it the version-control and audit problems it creates.

Connect SAP Business One via its SQL database (HANA or SQL Server) using Azure Data Factory or the On-Premises Data Gateway, landing data in OneLake for near-real-time reporting. Incremental extraction keeps the load light on the B1 server. Power BI on Direct Lake then reports live without nightly exports.

OData uses SAP's standard service layer, stays cleanly within licensing, and survives upgrades, but can be slower for very large extracts. Direct database access (HANA or the underlying SQL) is faster for bulk and delta loads but must respect SAP licensing terms and is more sensitive to schema changes. We default to OData for governed, supported extraction and use direct database access only where volume requires it and licensing permits — confirmed with your SAP team first.

Use change-data-capture or timestamp/watermark logic so each run pulls only records changed since the last load, rather than a full extract. On Fabric, delta-enabled pipelines or Mirroring land the changes in the bronze layer and merge them through silver. This keeps the SAP load light and the lakehouse current without nightly full reloads.

Connect Dynamics 365 through Fabric's native Dataverse link, which writes Dataverse tables to OneLake as Delta with no custom pipeline. Supply chain entities — orders, inventory, shipments — land governed and current. Power BI on Direct Lake then reports across Dynamics and other sources in one model.

Assess each Azure Data Factory (ADF) pipeline first, then re-platform the ones worth keeping as Fabric Data Pipelines or Dataflows Gen2, which share much of the ADF design surface. Where a source supports it, replace the pipeline entirely with Fabric Mirroring. We migrate selectively — pipelines that work and are low-cost can keep running via the mounted ADF in Fabric.

Map each SSIS package to its source, transformation, and destination, then rebuild the logic as Fabric Dataflows Gen2 (Power Query) or Data Pipelines, landing output in OneLake as Delta. Lift-and-shift via Azure-SSIS is possible as an interim step, but the durable move is to rebuild on Fabric so there is no separate runtime to maintain. We migrate package by package, validating row counts against the old output.

Inventory the pipelines, then score each on usage, business value, run cost, and failure rate. The high-value, frequently-run pipelines migrate first; orphaned or duplicate pipelines retire. As with reports, a small fraction usually carries most of the value — migrating everything is wasted effort.

Mirroring continuously replicates an entire source database into OneLake as Delta with near-zero configuration — best when you want a live copy of a system like SQL Server, Azure SQL, or Cosmos DB. Dataflows Gen2 is for transformation: shaping, cleaning, and combining data with Power Query before it lands. Use Mirroring to get data in fast and unchanged; use Dataflows Gen2 where the data needs work on the way. Most estates use both — Mirroring for ingestion, Dataflows Gen2 for the silver layer.

Cut the number of moving parts: replace hand-built extract pipelines with Fabric Mirroring where possible, consolidate transformations into governed Dataflows Gen2, and standardise on the medallion pattern so failures are localised. Fewer custom pipelines means fewer schema-change breakages — the main source of data-team firefighting. The result is engineers building new data products instead of fixing brittle ETL.

Land WMS, TMS, and ERP data in OneLake and conform them in the silver layer to a shared logistics model — orders, shipments, and inventory keyed consistently. Once joined, OTIF, dwell time, and cost-to-serve read from one governed model in Power BI. The unified view is what turns three systems into a single logistics picture.

Connect a 3PL system through its API, SFTP file feed, or database, landing the data in OneLake via Azure Data Factory or Fabric pipelines on a governed schedule. Conform it in silver to the same keys as your ERP so 3PL shipments reconcile to orders. You then track 3PL performance in the same Power BI model as in-house operations.

Bring each warehouse's stock data — from WMS, ERP, or spreadsheets — into OneLake and conform it to one item-and-location model in the silver layer. A single governed measure for on-hand, in-transit, and available-to-promise then reports across all sites in Power BI. Planners see one inventory position instead of reconciling separate site reports.

Fabric Eventstream connects natively to Apache Kafka and Azure Event Hubs as sources, routing supply-chain events into OneLake, a KQL database, or Power BI. No custom consumer code is required — you configure the source and destination. This is how order, shipment, and telemetry event streams land in the same platform as ERP data.

Build every pipeline on the medallion pattern — raw in bronze, conformed in silver, business-ready in gold — with access controls, sensitivity labels, and lineage configured from day one, not retrofitted. Microsoft Purview catalogues the data and tracks lineage across sources. Governance is part of the build, so you always know where a number came from.

Purview integrates with Fabric to catalogue OneLake data, classify sensitive fields, and map end-to-end lineage from source system through bronze, silver, and gold to the Power BI report. An auditor or analyst can trace any figure back to its origin. It turns governance from a spreadsheet exercise into a queryable, current map of the estate.

Apply validation rules in the silver layer — referential checks, range checks, and reconciliation against control totals — so bad records are caught and quarantined before they reach a dashboard. Conforming keys across ERP and WMS surfaces mismatches (an order with no shipment, stock with no item master) as data-quality exceptions. The result is one reconciled dataset rather than three systems that quietly disagree.