Hi all,
Data sovereignty is becoming a core design criterion in modern analytics stacks – especially as organizations face stricter regulations (EU Data Act, AI Act) and increasing pressure to localize data handling.
In analytics environments (Data Warehouses, BI, ML platforms), we see three main architectural shifts:
- Adoption of EU-based infrastructure like StackIT, Exoscale or Ionos
- Use of analytics engines like Exasol that support flexible deployment (on-prem, private or public cloud)
- Move toward hybrid and multi-cloud architectures that separate sensitive workloads from globally distributed ones
Relevant questions:
- How do you balance performance and compliance in your BI/ML stack?
- Are you using region-specific deployments or tools with fine-grained control over compute & storage layers?
- What’s your approach to AI model training and inference, considering data locality and legal boundaries?
We’re curious:
Which tools and platforms are you working with?
What works well in practice?
Any best practices to share around data residency, security, and sovereignty in analytical use cases?
Looking forward to your input and examples!