GCP – Engineering Deutsche Telekom’s sovereign data platform

Imagine transforming a sprawling, 20-year-old telecommunications data ecosystem, laden with sensitive customer information and bound by stringent European regulations, into a nimble, cloud-native powerhouse. That’s precisely the challenge Deutsche Telekom tackled head-on, explains Ashutosh Mishra. By using Google Cloud’s Sovereign Cloud offerings, they’ve built a groundbreaking “One Data Ecosystem.”

When we decided to modernize our telecommunications data ecosystem at Deutsche Telekom, we faced a daunting task. Over 40 legacy systems, each an ecosystem (data warehouse or data lake), held terabytes of customer, network, and operational data. Each system had 5,000 to 10,000 users who had built their workflows around these isolated silos over decades of use. 

The result? What I lovingly call a “spaghetti mess” of data distribution and an undetermined cost of value creation.

The technical challenge of building our One Data Ecosystem (ODE) was one thing — consolidating disparate systems always is. It’s the regulatory puzzle that made it genuinely interesting. 

As a telecommunications company in Germany, we handle some of the most sensitive data imaginable: call data records (CDRs), network telemetry, and customer location data. Under GDPR and Germany’s Telecommunications and Telemedia Data Protection Act regulations, this data simply can not leave sovereign borders or risk exposure to foreign legal frameworks.

Here’s where it gets technically fascinating: Traditionally, regulated industries solve this by building expensive on-premises encryption and pseudonymization infrastructure. You process your sensitive data locally, strip it of identifying characteristics, and then send sanitized versions to the cloud for analytics. 

This approach costs millions in dedicated hardware and creates a fundamental innovation bottleneck. We wanted something radically different: cloud-native processing of sensitive data, without compromise.

Engineering sovereignty at cloud scale

The breakthrough came with Google Cloud’s approach to digital sovereignty and their Germany Data Boundary by T-Systems offering (formerly known as Sovereign Controls by T-Systems). The architecture is elegant in its simplicity: Deutsche Telekom maintains complete cryptographic control through external key management (EKM) while using cloud-native data services.

Here’s how the technical magic works. T-Systems manages our encryption keys entirely outside Google’s infrastructure. This creates sovereign protection against foreign legal frameworks, and ensures they are able to control access to their data and deny access for any reason. 

Meanwhile, we use format-preserving encryption (FPE) algorithms that maintain data utility for analytics while ensuring privacy protection.

The core innovation is our custom pseudonymization layer, which comprises C++ modules with Java wrappers that handle real-time data transformation during ingestion. This eliminates the traditional need for separate preprocessing infrastructure while maintaining analytical value.

Choosing our data format was crucial. After extensive POCs, we settled on Apache Iceberg, and here’s why that matters for anyone building similar platforms: Iceberg solves the polyglot analytics problem beautifully. Our data scientists prefer working in Python notebooks, our engineers use Spark, and our business analysts work with SQL.

While traditional approaches force you to pick sides or maintain multiple data copies, Iceberg provides us with a single source of truth that speaks every language fluently.

The three-layer architecture we built around Iceberg is worth replicating: Raw data lands directly in Cloud Storage, flows through an Atomic layer for normalization and schema evolution, and then surfaces in an Analytic layer optimized for specific use cases. BigQuery, Spanner, BigTable, and Cloud SQL each serve their optimal workloads while sharing the same underlying Iceberg foundation.

Performance and scale in production

We are migrating from more than 40 legacy systems to reinvigorate our business demands. We have ingested over 200 source systems in just six months. However, the real validation came recently when one of our use cases, running live on the new platform, achieved a 22x performance improvement over its legacy predecessor. 

That number represents the compound effect of eliminating data silos, reducing ETL complexity, and using cloud-native autoscaling. When you can process overnight analytics jobs in minutes instead of hours, you fundamentally change how business decisions get made.

What makes this platform genuinely scalable isn’t just the technical architecture; it’s the operational model. We’ve implemented a GitOps approach with policy-as-code onboarding through GitLab CI/CD pipelines, where infrastructure and governance policies are defined declaratively and deployed automatically. This means onboarding a new system takes hours instead of months, and compliance becomes automatic rather than manual.

Additionally, we’re already running agentic AI use cases on the public side of our platform. The unified data model we’ve built positions us perfectly for the next wave of AI innovation. As more AI services become available with sovereign controls, we’ll be ready to expand our deployment at scale.

The key insight: Build your data foundation with AI in mind, even if you can’t implement every AI capability immediately. Clean, unified, well-governed data is the prerequisite for everything that’s coming.

A blueprint for the future

This is one of the largest and most comprehensive data platforms built on Google Cloud’s Data Boundary — but it won’t be the last. The architectural patterns we’ve developed, external key management, format-preserving encryption, unified data formats, policy-as-code governance, are replicable across any regulated industry.

The business case is also compelling: Eliminate expensive on-premises preprocessing infrastructure while gaining cloud-scale analytics capabilities. The technical implementation is proven. What’s needed now is the willingness to engineer sovereignty, rather than simply accept traditional trade-offs.

For my fellow data architects in regulated industries, you don’t have to choose between innovation and compliance. With the right technical approach, you can achieve both and build platforms that position your organization for the AI-driven future that’s rapidly approaching.

The maturity and integration of Google Cloud’s data and AI capabilities, combined with our intensive collaboration between engineering teams, has made this transformation possible. We’re not just customers: We’re co-creating the future of sovereign cloud platforms.