Data Platform
Data Platform: lake, warehouse and lakehouse on AWS
The data platform your analytics and your AI stand on: ingestion, storage, transformation and modelling on S3, Glue, Athena and Redshift, with reliable, reproducible pipelines.
The foundation of your analytics and AI
The problem
- Your data is scattered across silos and nobody fully trusts the numbers.
- Every report is a manual effort that arrives late and out of sync.
- You want to do analytics or AI, but the data foundation isn't ready for it.
- You have no governance: you don't know who accesses what, or where each number comes from.
What's included
- Data lake / lakehouse architecture on AWS (S3, Glue, Athena, Redshift).
- Ingestion and transformation pipelines: versioned, monitored and reproducible.
- Data modeling for analytics and for AI use cases (including RAG).
- Data governance: catalog, quality, lineage and access control.
- Pipeline orchestration and observability, with alerts on failures.
Good AI starts with good data
A GenAI assistant or an ML model is only as good as its data. That's why data engineering isn't optional: it's the foundation on which we build GenAI and analytics you can actually trust.
See GenAI Consulting & AssessmentHow we work
Discovery
We map your sources, your current state and the business questions your data needs to answer.
Plan & Quote
We design the data architecture and the pipeline roadmap, with clear scope and price.
Execution
We build the pipelines and the lakehouse with IaC, validating data quality at every step.
Hand-off & MSP
We leave the platform documented and monitored. We can keep operating and evolving it with you.
Stack & technologies
Reliable pipelines · data ready for analytics and AI
FAQ
Why do I need data engineering before doing AI?
Because a model or an assistant is only as good as its data. Siloed, dirty or ungoverned data gives unreliable results. The right data foundation is what makes AI actually work.
What is a lakehouse?
An architecture that combines the flexibility of a data lake with the performance of a data warehouse, on AWS. It gives you a single place for analytics and AI without duplicating data everywhere.
Do you work with my existing sources?
Yes: transactional databases, SaaS, events, files. We build the ingestion into AWS and leave it monitored.
How do you ensure data quality and governance?
With a catalog, quality checks, lineage and access control. You know where each number comes from and who can see it.
