We architect and build modern data platforms on Microsoft Fabric, Databricks and cloud-native services, with governance and semantic models that turn raw data into decisions and into fuel for AI.
Most companies do not lack data. They lack a platform where data is reliable, governed and reachable by the people and models that need it.
Each department builds its own extracts and dashboards. Numbers differ by source and nobody owns the definition of revenue.
Years of ETL jobs, stored procedures and spreadsheets keep the business running but block any change.
Generative AI and machine learning expose every gap in quality, lineage and access control that traditional BI tolerated.
Warehouses and licenses grow every year while decision makers still rely on manual reconciliation.
One governed foundation serves operational reporting, self-service analytics, machine learning and generative AI.
Semantic models and a data catalog give finance, operations and sales the same numbers with the same meaning.
Medallion-style layers and reusable pipelines turn a new dashboard from a project into a task.
Clean, documented, access-controlled data is the prerequisite for RAG, forecasting and agents that act on it.
Lakehouse storage, compute separation and workload governance keep cloud spend proportional to use.
From data strategy and architecture to engineering, governance and enablement.
Target architecture, platform selection, domain and ownership model, and a roadmap from current systems to a governed lakehouse.
OneLake, lakehouses and warehouses, Data Factory pipelines, Direct Lake semantic models and Power BI, designed as one integrated platform.
Delta Lake, Unity Catalog, streaming and batch pipelines, and ML-ready feature layers on AWS, Azure or Google Cloud.
Ingestion from ERPs, CRMs, APIs and events, change data capture, data quality checks and orchestration, built on our integration practice.
Power BI models, measures and certified datasets that make self-service safe, plus migration from legacy BI tools.
Catalog, lineage, ownership, quality rules, access policies and LGPD and GDPR controls, implemented in the platform rather than in slides.
Architecture first, then incremental delivery by business domain.
We inventory sources, consumers, pipelines and pain points, and quantify where trust and time are being lost.
We define the target platform, layers, governance model and migration strategy, with a clear decision on Fabric, Databricks or both.
We deliver one business domain end to end, from ingestion to certified semantic model, then repeat with the patterns established.
We set up monitoring, cost control and data ownership routines, and train your team to run the platform.
A lakehouse with clear layers, governed access and a semantic layer shared by BI and AI.
Microsoft Fabric and Databricks both fit this design. The right choice depends on your existing Microsoft footprint, engineering skills and multi-cloud requirements.
Cloud-native and open formats first, so your data stays portable.
Where we usually start with data and analytics engagements.
Moving SQL Server, Oracle or on-premise warehouse workloads to a governed lakehouse without losing the reports the business relies on.
Designing workspaces, capacities, OneLake domains and Direct Lake models for an organization already invested in Microsoft 365 and Power BI.
Multi-cloud data engineering with Delta Lake, Unity Catalog and ML pipelines.
A single semantic model for revenue, margin and operational KPIs across ERP and CRM.
Preparing documents, master data and metrics so RAG and agents work on governed, current information.
Catalog, ownership, quality rules and privacy controls implemented across the platform.
Fabric is a strong fit for organizations centered on Microsoft 365, Power BI and Azure that want an integrated SaaS platform. Databricks suits engineering-heavy teams, multi-cloud strategies and advanced machine learning. Many enterprises run both, with Databricks for engineering and Fabric for the semantic layer and reporting. We evaluate this against your skills, workloads and contracts before recommending.
A lakehouse stores data in open formats on cloud object storage while providing warehouse-style capabilities such as transactions, schema enforcement and SQL performance. It replaces the separate data lake plus data warehouse pattern with one governed platform.
In most cases yes. We migrate or reconnect existing reports to certified semantic models, often improving performance with Direct Lake, and retire duplicates gradually.
As part of the platform, not as a separate program. Catalog, lineage, ownership, quality rules and access policies are implemented in Unity Catalog, Purview or OneLake security while the first domains are being built.
Generative AI depends on data that is current, documented and access-controlled. We frequently design the data platform and the AI retrieval layer together so that the same governance applies to both.
Put governed data to work in RAG, copilots and agents.
Reliable ingestion starts with APIs, events and CDC.
Landing zones and cost control for data workloads.
Both platforms implement the lakehouse on open formats. The differences are in who they are built for, how governance works and where Power BI sits. A decision framework from projects that used one, the other and both.
RAG is simple to demo and hard to run. This guide walks through the components of a production retrieval-augmented generation architecture and the decisions that separate a reliable system from a chatbot that hallucinates politely.
The difference between a successful pilot and a production AI capability is rarely the model. It is the architecture around it. Here is what that architecture contains and which decisions matter.
An architecture assessment shows where trust, time and money are being lost and what a governed platform would take.