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Built on Fabric.Ready for AI.

Microsoft Fabric, data, and AI

Most AI projects stall on the data underneath them. We design, build, and govern Microsoft Fabric platforms so the numbers are trusted, the cost is understood, and Copilot and data agents have something solid to stand on.

See what we do

What we do

Fabric. Data. AI.

Seven practices, each a defined body of work with named deliverables. Most engagements use two or three. Open any card for the detail.

  • Microsoft Fabric

    Fabric architecture and strategy

    Microsoft Fabric

    Fabric architecture and strategy

    Workspace topology, capacity model, and medallion design decided before anything is built on top of them.

    • Workspace and domain topology
    • Capacity sizing against real workload profiles
    • Medallion contract with ownership at every layer
    • OneLake shortcuts and mirroring strategy
    • Security and tenancy model
    • Development to production promotion path
  • Microsoft Fabric

    Platform migration

    Microsoft Fabric

    Platform migration

    Synapse, Tableau, and legacy SQL estates moved onto Fabric with the business logic intact and the numbers reconciled.

    • Estate inventory and dependency mapping
    • Wave planning by dependency and business risk
    • Synapse SQL pools, Spark, and ADF conversion
    • Tableau to Power BI semantic modeling
    • Parallel-run reconciliation harness
    • Cutover runbook with a tested rollback path
  • Microsoft Fabric

    Capacity and cost optimization

    Microsoft Fabric

    Capacity and cost optimization

    Where every capacity unit goes, who should pay for it, and what the SKU should actually be.

    • Consumption traced to workspace, item, and owner
    • Right-sizing against a full business cycle
    • Chargeback by domain and cost center
    • Throttle diagnosis and remediation
    • Alerting on utilization and overage
    • A quarterly review your team runs without us
  • Data

    Data engineering

    Data

    Data engineering

    Pipelines, lakehouses, and real-time ingestion built to be operated by your team rather than babysat by ours.

    • Medallion lakehouse with enforced schema and grain
    • Metadata-driven ingestion frameworks
    • Spark and notebook development under source control
    • Eventstream and real-time ingestion
    • Orchestration, structured logging, and alerting
    • CI/CD with Git integration and deployment pipelines
  • Data

    Power BI and semantic modeling

    Data

    Power BI and semantic modeling

    Star schemas, DAX that holds up under concurrency, and a semantic layer the business trusts.

    • Semantic model design and refactoring
    • DAX performance work, measured before and after
    • Direct Lake and storage mode strategy
    • Shared measure library and business definitions
    • Executive reporting designed for decisions
    • Governed self-service enablement
  • Data

    Governance and security

    Data

    Governance and security

    Purview cataloging, sensitivity labeling, and access control built into the platform rather than bolted on afterwards.

    • Purview catalog, glossary, and end-to-end lineage
    • Sensitivity labels applied by inheritance
    • Entra groups, row and object level security
    • Tenant setting baseline and drift detection
    • Access review cadence and audit evidence
    • Quality gates at every medallion boundary
  • AI

    AI readiness, Copilot, and data agents

    AI

    AI readiness, Copilot, and data agents

    Copilot in Fabric and Power BI, Fabric data agents, and the model and governance work that decides whether their answers can be trusted.

    • AI readiness review of models and lakehouses
    • Semantic model descriptions and Prep data for AI
    • Fabric data agent design and configuration
    • Testing against a bank of questions with known answers
    • Sensitivity labels and access in place before AI is enabled
    • Copilot tenant settings and capacity planning

Our product

Lakewarden

Lakewarden

The command center for Microsoft Fabric.

Access, capacity, refreshes, connections, and tenant change — a whole Fabric estate in one console, signed in with your own Microsoft identity.

Retention
Years of audit, metrics, and daily snapshots — against 14 to 30 days natively
Compute
Runs outside your capacities, so monitoring Fabric costs no CU
Identity
Delegated Entra sign-in. Calls go straight to Microsoft's APIs and data stays in your tenant
Visit lakewarden.com

Built for the practices on this page. Each module covers the ongoing operating side of one of them.

  • Governance and security

    Access Review

    Every principal, every workspace, every role — guests flagged, effective access resolved through group membership, and role changes previewed before a write.

    Explore
  • Capacity and cost optimization

    Capacity and CU usage

    Per-item CU attribution, hour-by-weekday load, spike forensics, and SKU what-if — so a capacity conversation runs on evidence instead of instinct.

    Explore
  • Data engineering

    Refresh history

    The last fifty runs of every model with Fabric's own error text and a diagnosed cause, so a failing refresh is a known problem rather than an investigation.

    Explore

Microsoft Entra verified publisher.

Lakewarden is an independent product. It is not affiliated with or endorsed by Microsoft.

Reference architecture

What we build, and what wraps around it.

Source systems through to the semantic layer and the agents that read it, with governance, security, operations, and delivery treated as part of the architecture.

GovernancePurview catalog, lineage, glossary, sensitivity labels, and data quality gates at every medallion boundary.

Security

Entra identity, workspace roles, row and object level security, private networking, and tenant policy baseline.

Operations

Orchestration, structured run logging, alerting, capacity monitoring, chargeback, and quarterly right-sizing.

Delivery

Git integration, deployment pipelines, environment parameterization, and reviewable promotion to production.

03OneLake

A single logical lake in open Delta Parquet. Every Fabric engine reads and writes the same tables, which removes the copy-per-tool pattern that makes reconciliation impossible and storage costs compound.

How we work

Review, architect, build, harden, transfer.

The sequence is deliberate. Each step is only as good as the one before it, and the last one is the point of the other four: your team running the platform without us.

Start with the free review
  1. 01

    Review

    A free thirty-minute conversation about your current estate, what you are trying to reach, and whether Fabric is genuinely the right answer. No deck, no pitch.

  2. 02

    Architect

    Workspace and domain topology, medallion structure, security model, and capacity plan get decided while they are still cheap to change. Documented and reviewed before anything is built on top of them.

  3. 03

    Build

    Delivery in two-week increments against a defined backlog. Pipelines are idempotent, models are version controlled, and every increment ends with something your team can see running.

  4. 04

    Harden

    Logging, alerting, access review, cost controls, and a validated recovery path. Migrations are reconciled against the source system before anything is retired.

  5. 05

    Transfer

    Knowledge transfer is scheduled work, not a document dropped on the last day. The engagement ends with your team operating the platform without us.

Free consultation

Thirty minutes. No cost, no deck.

We look at the architecture you have today, what you are trying to reach, and whether Microsoft Fabric is the right platform for it. If it is not, we will say so.

What we cover

  1. 01Your current estate, and where the friction actually is
  2. 02Where Fabric fits in it, and where it does not
  3. 03Whether your data is ready for Copilot and data agents
  4. 04What a realistic first ninety days would involve

Based in

Minneapolis, Minnesota

Working with teams across the United States

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Book your free consultation.

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