Guest author contribution
by Jörg Kremer
mip Management Informationspartner GmbH
Head of Consulting / Delivery Manager
Cloud migration: full of opportunities, but also risks
The cloud offers analytics teams enormous opportunities: horizontal scaling, pay-per-use, automation and continuous integration of new services. However, this versatility also conceals pitfalls. Especially for IT architects responsible for data analytics infrastructures, without sound architectural decisions and proactive risk management, the cloud opportunity quickly becomes a strategic problem.
Table of contents
Data sovereignty and regulatory pressure
Many analytics projects process sensitive data - such as personal information, transaction-based events or log data relevant to data protection. Migration to the cloud shifts the physical and legal control over this data. It leaves the secure company networks and is stored in external data centers, which are often operated across national borders.
Problems arise if the storage locations are not clearly defined, access options are not fully documented or regulatory requirements such as the GDPR have not been fully taken into account. In practice, this can have serious consequences.
For example, a healthcare company migrated its analysis reports to a public cloud environment - only later did it turn out that the underlying servers were located in the USA. Migrating back to an EU region was technically feasible, but organizationally and contractually complex.
Vendor lock-in in the analytics world
Another often underestimated risk lies in the creeping dependence on specific providers and their proprietary services. Cloud-native tools such as BigQuery, AWS Glue or AzureWhat is Azure? Azure is a cloud computing platform from Microsoft. It... More Synapse offer enormous performance and ease of integration, but are also strongly tied to the respective platform.
The more an analytics stack relies on such services, the more difficult and costly a subsequent migration will be - whether for economic, technical or strategic reasons. To prevent this dependency, it is advisable to consciously rely on open standards. Technologies such as Apache Airflow or Kubernetes enable greater portability. APIs should be designed in such a modular and abstract way that a change of provider remains realistic, at least in perspective. The choice of data formats can also set the course for the future: Those who rely on open formats such as Parquet or ORC increase their independence and minimize conversion costs in the event of a migration.
The hidden costs of data processing
Analytics platforms often involve a high level of data movement - not only internally, but also across network boundaries. While such processes hardly incur any costs in traditional on-premises environments, the same process can quickly become an expensive affair in the cloud.
Many teams underestimate how high egress costs for data exports can be, for example, or how much always-on clusters - such as for Spark processing or data warehousing - can hit the budget. Redundant calculations or poorly planned queries in pipelines can also lead to cost spikes.
The answer to these challenges lies in early cost awareness - and in structured FinOps processes. Responsibility for cloud expenditure must not lie solely with controlling, but must be part of architecture and deployment responsibility. This is the only way to optimize processes and avoid cost traps.
Availability and fault susceptibility of pipelines
If central ETL or streaming pipelines fail, this usually has a direct impact on downstream processes: Reports remain empty, dashboards provide outdated data, machine learning models are no longer reliably trained.
New sources of error occur in the cloud, for example due to network delays, service disruptions or changing API behavior. This makes it all the more important to build resilience into the architecture. This means implementing retry logics, defining fault tolerances, using monitoring solutions such as Prometheus or cloud-native tools and establishing service level indicators (SLIs) or service level objectives (SLOs). This is the only way to identify risks at an early stage and systematically limit them.
Hybrid operation - opportunities with side effects
Many analytics systems are operated in hybrid mode today: On-premises databases feed cloud dashboards, local Hadoop clusters provide input for cloud-based visualizations or model training. This flexibility seems sensible at first glance - but it creates complexity.
The challenges start with data synchronization: Real-time or near-real-time synchronization between two infrastructures requires high-precision orchestration. Different security models quickly lead to access problems, especially if role and rights concepts are not standardized. Monitoring becomes more difficult because metrics and logs are distributed across multiple platforms.
Unplanned continuous operation in hybrid mode is therefore risky. From the outset, there should be a target picture that defines which systems will be moved to the cloud in the future, which will remain on-prem - and how the transition will be structured in concrete terms. Without such a target picture, there is a risk of a state of permanent provisional arrangements that creates technical debt.
Emergency planning: the underestimated discipline
Rarely is the failure of a cloud service the biggest problem for the company - rather, it is the lack of preparation for precisely this eventuality. What happens if the region in which the cloud DWH is operated is temporarily unavailable? How long will systems remain available if no data pipeline is running? Which data statuses can be restored?
A professional emergency concept must define recovery targets - both in terms of time (RTO) and data integrity (RPO). Georedundant deployments, tested restore procedures and escalation protocols are just as necessary as a communication plan for internal and external stakeholders.
Analytics architects are particularly in demand here: they have to define which reports and KPIs are business-critical in an emergency - and which can be tolerated with a delay.
Organizational weaknesses - cloud without cultural change
Technically cleanly migrated systems are of little use if the organization does not follow suit. There is often a lack of responsibility: Who is responsible for FinOps? Who ensures compliance? Who trains analysts for the new tools?
Further training, clear governance models and communication strategies are essential not only to enable migration, but also to successfully shape lasting change.
The introduction of a cloud analytics platform is a cultural turning point. Roles shift, processes change and new skills are required. If there is no structured change management, there is a risk of inefficiencies, frustration or shadow IT.
Jörg Kremer
Conclusion
Analytics architectures are highly sensitive to cloud risks - even more so than traditional IT systems. This is because they process business-critical data, are directly linked to operational decisions and are highly dynamic. Anyone designing cloud architecture today must not only demonstrate technical excellence, but also master economic, security-related and organizational risks.
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.
Click here for the first part of the blog article series:
What data analytics architects need to consider when migrating their infrastructure to the cloud
Guest author contribution by Jörg Kremer mip Management Informationspartner GmbH Head of Consulting / Delivery Manager Guest author contribution ...
Wilhelm Hardering
Senior Executive Manager
