OneLake set up with a plan
Workspaces, domains and permissions are laid out before the first table lands, so finance data, operations data and the sandbox your analysts experiment in never end up tangled in the same folder.
Microsoft Fabric implementation services
Microsoft Fabric implementation services that give your data one governed home
Senior US-based engineers stand up Fabric in your own tenant: OneLake and workspaces configured, a Lakehouse organized into raw, cleaned and reporting layers, pipelines that load it on a schedule you choose, and the spreadsheets, SQL Server jobs and legacy reports you rely on today moved across and checked against the originals. One written price, typical delivery in 2–4 weeks, 30 days of hypercare.
Representative dashboard — sample data
What you get
A data platform you can actually operate
Fabric bundles storage, integration, engineering and reporting into one service. The value comes from setting those pieces up deliberately, in the right order, rather than switching everything on at once.
A Lakehouse in layers
Raw data lands untouched, a cleaned layer fixes types and duplicates, and a reporting layer holds the tables people actually query. When a number looks wrong, you can trace it back one layer at a time.
Pipelines that run on their own
Fabric data pipelines and Dataflow Gen2 load each source on a schedule, move only what changed where the source allows it, and alert a real inbox or Teams channel when a run fails.
On-premises sources, reached safely
An on-premises data gateway inside your network connects outbound, so SQL Server databases and file shares feed Fabric without anyone opening an inbound firewall port.
Shortcuts instead of extra copies
Where data already sits in Azure storage or another supported location, a OneLake shortcut makes it readable in Fabric without building yet another copy job to keep in sync.
Source control from day one
Workspaces are connected to a Git repository and promoted through development and production stages, so a change is reviewed and tested before it reaches the reports leadership reads.
How it works
Three steps from scattered sources to one platform
- 1
Inventory and scope
A free consultation covers which systems hold your data, what reporting runs on it today, how fresh it needs to be, and what breaks. You get a written scope, one fixed price, and a delivery date.
- 2
Build the platform
Workspaces, OneLake, the Lakehouse layers and the gateway go in first; then pipelines load each source and legacy logic is rebuilt, with every table reconciled against the system it came from.
- 3
Report, promote, hand over
Semantic models are connected, workspaces are promoted from development to production through Git, and you get run-book documentation, a walkthrough, and 30 days of hypercare.
What Fabric is, in practical terms
Microsoft Fabric is a software-as-a-service analytics platform that puts data integration, data engineering, warehousing, real-time analytics and Power BI behind one sign-in and one storage layer. Microsoft's Microsoft Fabric overview (opens in new tab) describes that layer, OneLake, as a single logical data lake for the whole organization. For a small or mid-size business, the practical effect is that the pieces you might otherwise buy and wire together separately — a storage account, an integration tool, a warehouse and a reporting service — arrive already connected.
Already connected is not the same as already set up. Fabric will let anyone with access create a Lakehouse, a pipeline or a report in any workspace, and an estate that grows that way ends up exactly as scattered as the spreadsheets it replaced. Our engagement is the setup: the structure, the loads, the migrations and the handover, built in your tenant at a fixed fee agreed after your free consultation, by senior US-based engineers working remotely from our Orange County, CA base.
OneLake and the Lakehouse, set up deliberately
Every Fabric tenant has exactly one OneLake, and every workspace is a folder inside it. Tables are stored in the open Delta Parquet format, which is why a Lakehouse, a Warehouse and a Power BI semantic model can all read the same data without exporting it. Microsoft's OneLake documentation (opens in new tab) also covers shortcuts, which let Fabric read data held in Azure storage and other supported locations without copying it first.
We decide the workspace layout, who can see what, and which data is copied versus reached through a shortcut before anything is loaded. Microsoft's Lakehouse overview (opens in new tab) explains how files and tables live together in one item; we organize that item into layers — raw, cleaned, and reporting — so every reporting figure stays traceable to the source row it came from.
What each part of Fabric does, and what we build in it
Fabric has more components than most small teams need on day one. The table maps the parts that matter for a first implementation to the work each one carries; your written scope includes only the rows that fit your estate.
| Fabric component | What it does | What we build |
|---|---|---|
| OneLake and workspaces | The single storage layer for the tenant, divided into workspaces with their own permissions | A workspace layout by domain and environment, access roles, and shortcuts to data that should stay where it is |
| Lakehouse | Files and Delta tables together, with a read-only SQL analytics endpoint over the tables | Raw, cleaned and reporting layers with naming conventions, so every table has an obvious home and owner |
| Warehouse | Full T-SQL over structured data, including inserts, updates and stored procedures | Used where your team already works in SQL and wants to keep doing so; skipped where a Lakehouse alone covers the need |
| Data pipelines and Dataflow Gen2 | Orchestrated copy and transformation, with low-code Power Query steps in dataflows | Scheduled loads per source, incremental where the source allows, with retries and failure alerts |
| On-premises data gateway | An outbound connection from your network that lets Fabric read private sources | Installation, registration, a documented patching routine, and a decision on whether a second node is warranted |
| Notebooks | Spark code for transformations too complex or too large for a dataflow | Used sparingly and documented line by line, so the logic is maintainable by someone other than its author |
| Power BI semantic models | The model reports read, able to query Delta tables in OneLake directly in Direct Lake mode | A model on the reporting layer, with refresh or Direct Lake chosen per model and measures that reconcile to source |
Migrating off the systems you run today
Very few implementations start from nothing. The usual starting point is a SQL Server database with scheduled jobs, a handful of SSIS packages, an Excel workbook that three people update by hand, and reports that each connect to the source in their own way. Moving that onto Fabric means rebuilding the load logic as pipelines and dataflows, landing the data in the Lakehouse, and repointing reports at the new reporting layer — then running old and new side by side until the numbers match.
Some legacy paths are big enough to be their own project, and we scope them that way rather than folding them into platform work. If your existing reporting already runs on Power BI Premium or Pro workspaces, our Power BI to Microsoft Fabric migration services handle the workspace moves, capacity sizing and report validation. If the logic you want to retire lives in a departmental Alteryx Designer estate, our Alteryx to Microsoft Fabric migration services are the specialist route: that page covers the workflow-by-workflow rebuild of Alteryx tools, macros and analytic apps in Fabric, while this engagement covers the platform they land on — the Lakehouse, the pipelines and OneLake itself. The two are commonly scoped together, with the platform built first.
When Fabric is the right fit, and when it is not
Fabric fits organizations that want their reporting data in one governed place and are ready to run it on a Microsoft capacity. It is a strong match when Power BI is already the reporting tool, when several systems need combining before anyone can answer a simple question, and when the current process depends on someone exporting files by hand.
It is not always the answer. If all you need is a few reliable loads from existing sources into Azure SQL, standalone pipelines may be the simpler build, and our Azure Data Factory consulting services cover exactly that; pipeline concepts carry across if you move to Fabric later. Fabric also has a running cost, billed by Microsoft for capacity and storage, and our guide to Microsoft Fabric licensing cost explains how capacity and per-user licensing interact before you commit. We will tell you plainly during the consultation if Fabric is more platform than your data needs.
Delivery in three phases
The table shows scope and typical timeline rather than prices, because an honest number requires seeing your source list, legacy logic and reporting expectations first.
| Phase | Scope | Typical timeline | Fee |
|---|---|---|---|
| Phase 1 — foundation | Workspace and domain layout, access roles, OneLake structure, Lakehouse layers, gateway installation, Git connection, and the first source loading end to end into the raw layer | About the first week | Fixed fee — scoped after your free consultation |
| Phase 2 — load and migrate | Pipelines and dataflows for each in-scope source, cleaned and reporting layers, legacy jobs and packages rebuilt, shortcuts where copying is unnecessary, and reconciliation against the source systems | Weeks 2–3 | Fixed fee — scoped after your free consultation |
| Phase 3 — report and hand over | Semantic models on the reporting layer, reports repointed, failure alerting, development-to-production promotion, run-book documentation, and a walkthrough with the people who will operate it | Week 4 and close-out | Fixed fee — scoped after your free consultation |
Everything produced is yours from the first day: the workspaces, the Lakehouse, the pipelines, the semantic models and the Git repository all live in your own tenant, never in ours. Fabric capacity and storage are billed by Microsoft directly to you, separately from our fixed fee. For how scoping and handover work before you speak to anyone, see how our fixed-fee process works.
Related services
Specialist routes onto the platform
Alteryx to Microsoft Fabric Migration
Rebuilding an existing Alteryx workflow estate inside Fabric and reconciling every output against the original.
Power BI to Microsoft Fabric Migration
Moving Power BI workspaces onto Fabric capacity, validating reports, and retiring the old setup.
Azure Data Factory Consulting
Standalone pipelines into Azure for teams that need reliable loads without a full Fabric platform.
FAQ
Fabric questions, answered
You get a working Fabric workspace in your own tenant: a Lakehouse organized into raw, cleaned and reporting layers, pipelines or dataflows that load it on a schedule, OneLake shortcuts where data already lives elsewhere, a Power BI semantic model on top, and written run-book documentation for the people who operate it.
Often a Lakehouse, sometimes both. A Lakehouse suits a mix of files and tables and comes with a read-only SQL analytics endpoint; a Warehouse suits teams who want full T-SQL, including inserts and updates, over structured data. Both store Delta tables in OneLake, so the choice is about how your team works, not lock-in.
Yes. An on-premises data gateway installed on a machine inside your network lets Fabric pipelines and dataflows read SQL Server, file shares and other private sources over an outbound connection, so no inbound firewall ports are opened. We install it, register it, and document how to patch and monitor it.
This engagement stands up the Fabric platform itself: workspaces, OneLake, the Lakehouse, pipelines and the reporting layer. The Alteryx migration is a narrower job that rebuilds an existing estate of Alteryx workflows inside Fabric and reconciles every output. If you run Alteryx today, the two are often scoped together, platform first.
You do, directly to Microsoft, in your own tenant. Fabric runs on a capacity you purchase or pay for as you go, and OneLake storage is billed alongside it, so the running cost stays visible in your own Microsoft bill. Our fee is separate, fixed, and agreed in writing before work starts.
Typical delivery is two to four weeks from kickoff, with the date stated in your written scope. The fixed fee reflects how many sources are connected, how much transformation logic moves into the Lakehouse, whether a gateway is needed, and how much legacy reporting is migrated. Nothing starts until you approve that scope.
Book a free consultation
Get more from the Microsoft tools you already pay for
Tell us what you’re trying to fix — a report, an approval process, an intranet, a Copilot rollout. We scope it as a fixed-fee project, you approve, and a senior engineer delivers in 2–4 weeks.
- Fixed-fee scope agreed before any work starts
- Senior, US-based Microsoft engineers — no handoffs
- Typical 2–4 week delivery
- 30 days of post-delivery hypercare included
The fastest way to reach us is the form — tell us the task and we’ll reply within one business day.