Guest author contribution
by Jörg Kremer
mip Management Informationspartner GmbH
Head of Consulting / Delivery Manager
Guest author contribution
by Jörg Kremer
mip Management Informationspartner GmbH
Head of Consulting / Delivery Manager
The cloud is regarded as a forward-looking IT infrastructure - but its introduction is not a sure-fire success. Companies must weigh up technological potential such as scalability and efficiency gains against risks and complexity. This article provides an overview of the strategic questions that should be answered before a migration.
Scalability meets reality
The cloud is regarded as a forward-looking IT infrastructure - but its introduction is not a sure-fire success. Companies must weigh up technological potential such as scalability and efficiency gains against risks and complexity. This article provides an overview of the strategic questions that should be answered before a migration.
The migration of analytical infrastructures to the cloud is more than just an infrastructure project - it is a strategic decision with enormous implications. For IT architects in the field of data analytics, the opportunity lies above all in the elasticity and modularity of modern cloud services. They offer the technical prerequisites for scalable data processing, flexible integration of AI tools and agile project development.
But how can this potential be tapped securely, efficiently and economically?
Agility through cloud infrastructures - a paradigm shift
The typical development paths of classic analytics systems - monolithic, cumbersome, static - quickly reach their limits in the age of data-driven innovation. Cloud technologies support modern architecture paradigms such as:
- Containerization (Kubernetes, Docker)
- CI/CD pipelines for analytics models
- Automated scaling of ETL jobs
For data scientists and DevOps teams in particular, this creates enormous freedom in their daily work. Prototypes can be implemented and iterated quickly - without long lead times or resource conflicts.
Flexibility that does not come without complexity
But with freedom comes responsibility. Cloud-based analytics environments are highly complex: they require well thought-out networks, governance guidelines, identity management and FinOps processes. Too loose an architectural decision can be expensive in the long term - both technologically and economically.
Key questions before migration:
- How do I/O-intensive workloads behave in the cloud?
- How can performance be guaranteed with massive amounts of data?
- How can costs be realistically predicted and controlled?
A well-optimized Hadoop cluster or an in-memory database such as SAP HANA only performs well in the cloud if it is tailored to the respective platform.
For data analytics architects the cloud is a tool - not a goal. Those who plan and evaluate in a structured manner and take governance seriously will gain scalability, speed and a strategic advantage. Those who migrate without reflection risk uncontrollability and cost escalation. So see the cloud as an Architectural opportunity - with maturity.
Jörg Krämer
Decision criteria for workloads in typical Analytice infrastructures
Not all clouds are the same - and not every analytics component belongs there. Some decision-making aids:
| Component | Recommendation | Cloud-native pipelines | Ideally suited |
|---|---|
| Legacy DWH systems | Critical, high customization effort |
| Real-time analyses | With optimized infrastructure |
| Sensitive data (e.g. DSG) | Only with hybrid models |
Learn more?
mip-Whitepaper
Migration to the cloud
In the white paper from our partner mip, you can find out how to transform your analytics architecture into the cloud in a targeted manner - with a view to performance, security and cost-effectiveness.
Wilhelm Hardering
Senior Executive Manager
