Microsoft
Microsoft Fabric Consulting and Implementation
We design and build Microsoft Fabric data platforms on OneLake that bring data engineering, warehousing, real-time analytics, Power BI and AI together under one governed capacity.
One Platform for Data, Analytics and AI
Microsoft Fabric is a software-as-a-service analytics platform that combines Data Factory, Data Engineering with Spark, Fabric Data Warehouse, Fabric Data Science, Real-Time Intelligence, databases and Power BI on a single data lake called OneLake. Data is stored in open Delta Lake format, shortcuts give zero-copy access to Azure Data Lake Storage, Amazon S3 and Google Cloud Storage, and mirroring continuously replicates operational databases and platforms such as Azure SQL Database, Cosmos DB, Snowflake and Azure Databricks. Fabric IQ, currently in preview, adds ontologies, graph and agents that interpret data in business terms.
Hanoi Technology helps organizations adopt Fabric with a clear architecture rather than a collection of experiments. We design workspaces and capacities, implement medallion lakehouses, build pipelines and notebooks with CI/CD, set up governance with Microsoft Purview and the OneLake catalog, and connect Power BI through Direct Lake. We also plan migrations from Azure Synapse, Data Factory, SQL Server and Power BI Premium, and keep capacity costs under control.
We start small and prove value early. A typical first engagement takes one business domain, such as sales or finance, from source systems through lakehouse layers to certified Power BI reports, using the patterns that will later apply across the platform. Capacity consumption is measured from the first week, so you understand real costs before expanding, and every component is deployed through Git and pipelines.
The Challenge
Challenges When Adopting Fabric
Issues we help clients avoid or fix on Microsoft Fabric.
Too Many Choices
Lakehouse or warehouse, Spark or T-SQL, Dataflows or pipelines, import or Direct Lake: Fabric offers several ways to do the same job. Without design principles, teams choose inconsistently and the platform becomes hard to maintain.
Capacity Contention
All workloads share capacity units, so a heavy Spark job or a poorly designed report can throttle others. Teams need to understand smoothing, bursting and monitoring before scaling usage.
Governance Across Workspaces
Fabric makes it easy to create workspaces and items. Without naming standards, domain structure, access rules and endorsement, the same data gets copied and sensitive data spreads.
Migration from Existing Platforms
Synapse, Azure Data Factory, SQL Server warehouses and Power BI Premium all have different paths into Fabric. Rewriting everything at once is risky; leaving it all in place adds cost.
Business Value
What Fabric Delivers
Single Copy of Data
OneLake, shortcuts and mirroring reduce data duplication, so engines and reports work on the same Delta tables.
Unified Capacity and Billing
One capacity model covers data integration, engineering, warehousing, real-time analytics and Power BI.
Faster Power BI on Big Data
Direct Lake mode lets Power BI read Delta tables in OneLake without scheduled imports.
Real-Time Analytics
Eventstreams, Eventhouse and Activator detect patterns in streaming data and trigger actions.
Governance Built In
Microsoft Purview integration, the OneLake catalog, sensitivity labels and domains support discovery and compliance.
AI-Ready Data
Copilot, Fabric data agents and Fabric IQ ontologies work on governed data with business context.
Fabric Rewards Good Architecture
Because everything shares OneLake and capacity, design decisions have platform-wide effects. A small set of agreed patterns for ingestion, layers, naming and security makes Fabric simpler than the tools it replaces; skipping them makes it more complex.
We capture those patterns in a short platform playbook that new teams can follow, covering workspace naming, layer responsibilities, security, CI/CD and when to choose each Fabric engine.
Our Services
Hanoi Technology’s End-to-End Microsoft Fabric Services
Services across the Fabric adoption journey.
Fabric Readiness and Architecture
We assess your current data estate, licensing and skills, then design workspaces, domains, capacities, lakehouse and warehouse patterns and a migration plan.
- Current-state assessment
- Fabric reference architecture
- Adoption roadmap
Lakehouse and Warehouse Implementation
We build bronze, silver and gold layers in lakehouses and warehouses using Spark notebooks, T-SQL and Dataflow Gen2, with tests and documentation.
- Medallion lakehouse
- Warehouse models
- Data quality checks
Data Integration and Mirroring
We implement Data Factory pipelines, configure mirroring for operational databases and use shortcuts to reach existing data in ADLS, S3 or Google Cloud Storage.
- Ingestion pipelines
- Mirrored databases
- Shortcut configuration
Real-Time Intelligence
We build eventstreams, Eventhouse KQL databases, Real-Time Dashboards and Activator rules for IoT, logs and operational events.
- Streaming ingestion
- KQL databases and dashboards
- Activator alerts
Power BI and Semantic Layer
We design semantic models on Direct Lake, set up deployment pipelines and prepare models for Copilot, data agents and Fabric IQ ontologies.
- Direct Lake models
- Report suite
- AI readiness review
Governance, CI/CD and Capacity Management
We configure Purview, sensitivity labels, Git integration, deployment pipelines and capacity monitoring with the Fabric Capacity Metrics app.
- Governance framework
- CI/CD setup
- Capacity monitoring
Our Approach
Our Fabric Adoption Approach
Start with a valuable use case and build the foundation as you go.
- 01
Assess and Plan
We review current platforms, workloads and costs, and choose a first use case with clear business value.
Deliverables: Assessment · Use-case selection
- 02
Design the Foundation
We define workspaces, domains, capacity, security, naming and layer standards.
Deliverables: Architecture and standards · Capacity plan
- 03
Build the First Use Case
We implement ingestion, transformation and reporting end to end, proving patterns with real data.
Deliverables: Working data product · Pattern library
- 04
Migrate and Scale
We migrate existing workloads in waves and onboard new teams on the same patterns.
Deliverables: Migrated workloads · Onboarding guide
- 05
Operate and Optimize
We monitor capacity, cost and data quality, and add capabilities such as real-time analytics and data agents.
Deliverables: Operations runbook · Optimization reports
Use Cases
Microsoft Fabric Use Cases
Scenarios we design Fabric platforms for.
Enterprise Data Platform
A company consolidates data from ERP, CRM and SaaS tools into a Fabric lakehouse and serves certified Power BI models across departments.
Synapse and ADF Modernization
A data team moves pipelines from Azure Data Factory and dedicated SQL pools into Fabric Data Factory and Fabric Data Warehouse in phases.
Operational Database Mirroring
Azure SQL and Cosmos DB data is mirrored into OneLake in near real time, removing custom extract jobs.
IoT and Equipment Monitoring
Sensor data streams into Eventhouse, and Activator alerts maintenance teams when readings cross thresholds.
Multicloud Data Access
Shortcuts to Amazon S3 and Google Cloud Storage let analysts query data in other clouds without copying it.
Natural-Language Data Access
A Fabric data agent lets operations managers ask questions about orders and inventory and receive governed answers in Teams.
Technology
Microsoft Fabric Technology Stack
Fabric components and supporting tools we use.
- Microsoft Fabric
- OneLake
- Data Factory
- Dataflow Gen2
- Fabric Data Engineering
- Fabric Data Warehouse
- Lakehouse
- Mirroring
- Real-Time Intelligence
- Eventhouse
- Activator
- Fabric IQ
- Fabric Data Agent
- Power BI
- Microsoft Purview
- Azure DevOps
Platforms
Deliver Microsoft Fabric on Your Preferred Platform
Why Hanoi Technology
Why Organizations Choose Hanoi Technology
Senior, Cross-Functional Teams
Strategy, data, AI, cloud and engineering specialists work as one team, so nothing is lost between hand-offs.
Proven Delivery
More than 50 projects delivered for clients in North America, Europe and Asia-Pacific.
Radical Transparency
A written update every week and a demo of working software every sprint, so you always know where things stand.
Cost-Effective Global Delivery
Experienced engineers from our Hanoi delivery hub, with working-hour overlap for every major time zone.
Success Story · Logistics
Route optimization and fleet visibility for a regional logistics company
A regional delivery company handling thousands of shipments a day was planning routes and dispatching by hand. We built dynamic route optimization, a fleet dashboard and a driver app.
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+58%total ordersAI dispatch and live tracking for a food delivery startup
A fast-growing urban food delivery startup was losing customers to slow deliveries at peak times. We built AI order dispatching and connected restaurants, drivers and customers in real time.
Real estate
90%pricing accuracyTransparent AI property valuation for a multi-city marketplace
A growing real estate marketplace wanted buyers to trust its prices. We built a valuation engine and property pages that compare the estimated value with the asking price.
Resources
Featured Resources
Industry trends and practical guidance to help you get more value from data, AI and technology.
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OpenTelemetry in 2026: Building a Vendor-Neutral Observability Strategy
OpenTelemetry has become the default way to collect telemetry. Here is what changed in 2026 and a practical roadmap for a vendor-neutral observability stack.
Plan Your Microsoft Fabric Platform
Whether you are evaluating Fabric or scaling it, we can help you design it well. Book a free consultation and we will reply within 24 hours.
Microsoft Fabric FAQ
Is Microsoft Fabric a replacement for Azure Synapse?
For many analytics scenarios, Fabric is Microsoft’s strategic direction and covers what teams previously built with Synapse pipelines, Spark pools and SQL. Existing Synapse and Azure Data Factory environments can continue to run while you migrate. We assess each workload and plan a phased move, starting with those where Fabric offers clear benefits such as Direct Lake or simplified operations.
Lakehouse or warehouse: which should we use?
Both store data in Delta format in OneLake. Lakehouses suit Spark-based engineering, semi-structured data and data science, with a SQL analytics endpoint for reading. Warehouses suit teams that prefer full T-SQL with multi-table transactions and familiar SQL tooling. Many platforms use lakehouses for bronze and silver layers and a warehouse or lakehouse for the gold layer, depending on team skills.
How do we size and control Fabric capacity?
We estimate capacity from data volumes, refresh schedules, query patterns and user counts, then monitor actual usage with the Fabric Capacity Metrics app. Separate capacities for development and production, scheduled pausing for non-production, workload optimization and clear ownership help control cost. We review consumption regularly and adjust the SKU when patterns stabilize.
How does Fabric work with Databricks or Snowflake?
Fabric can mirror Azure Databricks Unity Catalog tables and Snowflake data into OneLake, and shortcuts can reference Delta tables stored elsewhere. This lets Power BI and other Fabric workloads use that data without full copies. We help decide where each workload should run, so you avoid paying twice for the same processing.
What is Fabric IQ and should we use it now?
Fabric IQ is a workload, in preview, that adds business semantics through ontologies, graph, planning, data agents and operations agents built on OneLake and Power BI semantic models. It is promising for grounding AI agents in business concepts. We recommend preparing now with clean semantic models and well-documented data, and piloting IQ features on a narrow use case while they mature.
How long does a Fabric implementation take?
A readiness assessment and architecture typically take three to five weeks. A first production use case, including ingestion, lakehouse layers and Power BI reports, often takes six to ten weeks. Migrations from existing platforms are delivered in waves over several months, depending on the number of pipelines, models and reports involved.