Data Analytics
We modernize your Data Warehouse by migrating it to Microsoft Fabric or Databricks, incorporating Lakehouse architectures, Delta Lake, and data governance to enable advanced analytics and AI.
Bridge to modernization
Every improvement made to your current DWH generates knowledge and documentation that will support a future migration to Fabric or Databricks. When you decide to evolve, you will not be starting from scratch.
- High operational costs: Infrastructures with limited adaptability.
- Slow processing: Limitations in accessing up-to-date information.
- Limited flexibility: Complex changes in data sources and reporting.
- Accumulated technical debt: Environments that are difficult to maintain and evolve.
- New AI requirements: Data prepared for advanced analytics and automation.
Your current Data Warehouse supports daily operations, but modernizing it is key to reducing costs, improving performance, and accelerating access to AI capabilities.
As a result of our assessment, we provide a clear and structured report divided into four action phases.
- Unified platform: Data engineering, analytics, AI, and Power BI on a single data foundation.
- Direct Lake: Data consumption in Power BI without intermediate import processes.
- Flexible scalability: Resources adjusted to current business needs.
- Integrated governance: Lineage, cataloging, and access control managed centrally.
- High-performance engine: Optimized processing for analytics and data engineering
- Governance with Unity Catalog: Centralized management of access, lineage, and data assets
- Integrated MLOps: Native tools to develop, deploy, and manage AI models
- Flexible analytics: Scalable SQL environments without infrastructure management.
- Lakehouse/Medallion architecture: Trusted layered data using the open Delta Lake format.
- AI-ready: Curated and accessible data for models, Copilot, and Azure AI Foundry.
We follow a structured approach that provides a solid foundation for the future implementation of Purview or Unity Catalog.
Architecture, workloads, costs, performance, dependencies, and risks.
Fabric, Databricks, or coexistence, based on your real context, not commercial partnerships.
Lakehouse/Medallion, security, governance, and migration strategy.
Lakehouse, notebooks, pipelines, orchestration, and pattern standardization.
From SSIS to Fabric Pipelines/Dataflows Gen2 or Databricks Jobs/Workflows.
Metric standardization, Direct Lake, and optimized consumption layer.
Purview or Unity Catalog depending on the platform.
Sizing, automated pause, environment separation, and cost optimization from day one.
Training, handover, and post-migration support.
Concrete results from day one
Actionable deliverables
- Modernization roadmap with phases, timelines, dependencies, and quick wins.
- Architecture implemented in Fabric or Databricks with security and governance.
- Production-ready pipelines with monitoring, alerts, and traceability.
- Curated data model (Medallion) and datasets ready for consumption.
- Cost baseline and verified optimization plan.
- Technical and operational documentation.
We migrate to Fabric or Databricks in phases, without disrupting the business and keeping costs under control.
Fabric is ideal when your technology ecosystem is fully based on Microsoft products (Microsoft 365, Power BI, and Purview). Databricks is the preferred choice for advanced Data Science, MLOps, large-scale Spark workloads, or multi-cloud environments. In many cases, both platforms coexist. We help you choose the best option based on your specific requirements.
Yes. We design coexistence strategies that allow domains to be migrated gradually, validating results while maintaining business continuity.