OneLake and Lakehouse
Eventhouse and KQL
Spark
Data Factory
Direct Lake and Power BI
Purview
Capacity management
CI/CD and IaC
START WHERE YOU ARE
REPORTING
We have Power BI everywhere and the reports argue with each other.
Power BI is how people see the answer. Fabric is where the answer gets built, governed and kept current. We start upstream of the dashboard.
MODERNIZING
We have years of ADF, SSIS, Synapse or Data Explorer and somebody still babysits it.
Most of our work, and the genuinely hard part. We assess what migrates, what gets redesigned, and what stays.
EVALUATING OR SCALING
We are not sure Fabric is the right move, or it is in and the bill keeps climbing.
We look at your estate and licensing and say whether Fabric is the shortest path. Sometimes it is not. If it is in, sizing and isolation so cost tracks usage.
We design, implement, and operate Fabric as an enterprise data platform rather than as a new place to build Power BI reports. Our architects work hands-on with OneLake and Lakehouse, Eventhouse and KQL, Spark, Data Factory pipelines, Direct Lake, medallion architectures, Purview governance, CI/CD, and production capacity management.
Capacity and Workload Isolation
Capacity is a shared resource. Critical workloads separated, workspace and environment boundaries set, utilization monitored, and transformations built to consume resources efficiently.
Governance and Security by Design
Microsoft Entra ID, role-based access, workspace security, service principals, managed identities, Key Vault, private networking, Purview, and regional data residency, built in rather than bolted on.
CI/CD and Infrastructure as Code
Source control, deployment pipelines, automated testing, Infrastructure as Code, and development, test and production separation, so the platform can be safely changed after it reaches production.
Real-Time and IoT Workloads
Eventhouse and KQL with streaming ingestion, Lakehouse storage, Spark processing, and Direct Lake, for telemetry that does not wait for a nightly batch.
DESIGNED AROUND HOW DATA MOVES THROUGH THE ORGANIZATION
SOURCES
SaaS applications
SAP via gateway
Files and feeds
Device telemetry
Raw source data
Landed as it arrived. Nothing trusted yet, nothing thrown away.
Lakehouse · OneLake
Validated and conformed
Cleaned and joined into consistent business entities. Where the arguments about the number end.
Spark · Data Factory pipelines
Business-ready
Semantic models and Direct Lake serving Power BI, dashboards, alerting, and feature engineering.
Direct Lake · Semantic models
CONSUMERS
Executive reporting
Self-serve Power BI
AI and agents
Operational apps
A governed foundation that supports multiple analytical use cases without rebuilding the same transformations inside individual reports. Depending on the workload, the architecture draws on OneLake, Lakehouse, Eventhouse and KQL databases, Spark notebooks, Data Factory pipelines, semantic models, Direct Lake, Power BI, and real-time streaming.
FABRIC SERVICES
Most engagements use three or four of these. None use all twelve at once.
Strategy, readiness, and architecture
Data platform and medallion design
Implementation and migration
Real-time analytics and IoT
Lakehouse, Eventhouse, and data engineering
Data ingestion and transformation
Power BI and semantic-model modernization
Capacity planning and workload isolation
Security, identity, networking, and governance
Data residency and multinational architecture
CI/CD and Infrastructure as Code
Monitoring, optimization, and production support
Our Process
Fabric projects follow a consistent structure at Smart Data. The phases are not rigid. They adjust to where your organization is starting from. But the sequence is deliberate, and the first phase exists because the longest-lead item is not engineering.
1–2 weeks • Low commitment • High clarity
We settle the decisions that are expensive to reverse: capacity topology and SKU, data residency and region, which SAP and source systems land where, and whether production data is permitted in non-production. We submit the Azure capacity quota request in the first week, because quota is granted per region and per SKU and approval can take days to weeks. Nothing can be provisioned until it clears.
Phase 2: Build and Parallel Run
4–8 weeks • Working solution • Measurable outcome
We build the medallion architecture in Fabric, move ADF, Synapse, SSIS and Data Explorer workloads across, and rebuild the semantic models reporting reads from. For migrations we run the new environment in parallel with the legacy stack until the numbers match. Parallel run exists so that when something is wrong you find out on a Tuesday instead of at quarter close.
Phase 3: Ongoing Support and Optimization
Ongoing • Expand what works • Embed into operations
After go-live, most organizations have more domains to bring in, capacity to right-size, and reporting to extend. We offer managed services for teams that want a long-term delivery partner, with named support tiers and response commitments. We also enable your team on the patterns and Fabric features we used, so they can maintain and extend the environment independently.
MIGRATING TO FABRIC
We assess what should migrate, what should be redesigned, and what should stay where it is. Sometimes the right answer is to leave a workload alone.
WHERE YOU ARE
Azure Data Factory
Synapse and Data Explorer
SSIS, SSAS and SSRS
SAP through the on-prem gateway
Power BI on extracts
01
Inventory
02
Dependency map
03
Rebuild
04
Parallel run
05
Cutover
Parallel run exists so that when something is wrong you find out on a Tuesday instead of at quarter close.
WHERE YOU LAND
Microsoft Fabric
OneLake
Lakehouse
Eventhouse
Direct Lake
Data Factory pipelines
Semantic models
Purview
Does Fabric replace Synapse?
For most estates, over time, yes. Synapse pipelines, dedicated SQL pools, and Data Explorer clusters have direct landing places in Data Factory, the Fabric warehouse, and Eventhouse.
What about SAP?
It arrives the way it does today, through the on-premises data gateway, and lands raw in a Lakehouse before anything is conformed.
Our Services
Our Value
Why Organizations Choose Smart Data for Microsoft Fabric
The Power BI On-Ramp
Most Fabric journeys start in Power BI. We work upstream of the dashboard, on the semantic models, pipelines and governance that decide whether two reports agree.
Migration Discipline
Almost nobody arrives at Fabric from nothing. Inventory, dependency mapping, rebuild, parallel run and cutover is the same discipline we used to automate 78% of a legacy ETL conversion on Databricks.
More Than One Platform, On Purpose
The right engine depends on your estate rather than on our specialization. We build on Fabric and Databricks, and we score both against your workloads, so the recommendation is not a sales position.
Same Team Throughout
The architects who design your Fabric environment are the engineers who build it. Senior practitioners do the work throughout the engagement, not entry-level staff following a playbook.