Data Infrastructure
Your data architecture should enable growth, not block it.
Most data systems break when companies need them most—during migrations, when adding new data sources, or when scaling to 10x users. Teams end up stuck the database can't handle the queries, ETL pipelines fail under load, or adding a new feature requires rewriting everything.
The problem isn't the technology—it's designing systems that only solve today's problem. We build data infrastructure that anticipates how data scientists, analysts, and applications will actually use it tomorrow.
How we think about data infrastructure
We design from the end-user backward. Before touching a database schema, we ask Who will query this? What questions will they need answered? How will this system evolve in 6 months?
This approach leads to simpler, more resilient architectures. A well-designed table structure means analysts can write their own queries instead of waiting for engineering. A smart ETL pipeline handles new data sources without rewrites. A secure multi-tenant architecture serves hundreds of customers from a single table—scalable, maintainable, cost-effective.
We've learned that the best data systems are the ones nobody notices—they just work, even as requirements change.
Practical cases
Challenge
Spanish energy market data arrives daily in I90 files—Excel workbooks with 30+ sheets detailing programming units, energy prices, and market operations. Energy companies and analysts needed to query this data for pricing strategies, forecasting, and regulatory compliance. The Excel format made it nearly impossible to analyze trends or integrate with other systems.
Solution
We designed a relational database that transforms 30 sheets into a single queryable table. Our ETL pipeline runs daily, processing new I90 files automatically. The schema captures the relationships between programming units, time periods, and prices—making it simple to query "show me all prices for Unit X in March" instead of manually searching through spreadsheets.
Outcome
Data scientists can now write SQL queries instead of wrestling with Excel. The system handles new file formats automatically as the market structure evolves. The same architecture powers AI-assisted analysis through MCP servers, enabling advanced forecasting and decision support.
Who this is for
Energy companies stuck analyzing market data in Excel
SaaS platforms needing database architecture that scales
IoT startups requiring real-time data pipelines
Companies preparing for AI/ML integration
Teams migrating legacy systems to modern infrastructure
Anyone whose current database is blocking growth
Ready to build infrastructure that scales?
Let's discuss your data architecture challenges.