Data Engineering with Cloud Platforms: Cloud Storage, Data Flows, and Scaling

Course overview

The training course "Data Engineering with Cloud Platforms: Cloud Storage, Data Flows, and Scaling" gradually introduces the topics of "Cloud Storage", "Data Flows", and "Scaling" and translates the content into understandable work situations. The learning progress in Cloud Computing is made visible through a documented transfer task. The focus is on structured work tasks, technical implementation steps, and a comprehensible quality assurance process. The transfer part deepens Cloud Computing and translates the insights into Data Engineering into a coherent task. The guiding theme is the task "Data Engineering with Cloud Platforms"; it is addressed from a technical, methodological, and quality-related perspective. Commonalities, differences, and sensible transitions between Cloud Computing and Data Engineering are elaborated. Participants work on understandable practical cases from data-related analysis and platform tasks, compare solution paths, and document assumptions, decisions, and results. The goal is a structured understanding of the task areas described in the title and their application in different work situations. Program At the end, work results from Cloud Computing are reflected upon and compared with the learning objectives for Data Engineering. The educational content is divided into seven interrelated learning sections: - Data Paths and Professional Goals: The focus "Data Engineering with Cloud Platforms" is categorized in terms of task framework, typical areas of application, and the necessary work steps. The orientation is specified by the path from sources and structures through processing steps to usable and verified results, as well as through interfaces, dependencies, and controlled transitions between multiple work or system areas. - Cloud Storage: Participants categorize requirements, choose an appropriate approach, and document the resulting outcomes. A dedicated learning step places Cloud Computing within the professional task framework. - Data Flows: Terms, processes, and decision points are interconnected and applied in a practical task. - Scaling: This subtopic is addressed using central terms, typical work steps, and a comprehensible application case. The learning tasks connect Cloud Computing with Data Engineering and make the interfaces between both topics visible. - Implementation, Integration, and Testing: Components are set up or developed, interconnected, and secured through comprehensible tests. - Quality of Evaluation: Sources, assumptions, calculations, and statements are checked and documented clearly. The technical focus is on the competence field of Cloud Computing and the application focus of Data Engineering. - Practical Case and Transfer: Participants work on a coherent case, justify their approach, and transfer the insights gained to a modified task. In the results control, both Cloud Computing and Data Engineering are examined based on understandable criteria. A practical example shows how Cloud Com…

Upcoming dates

• 2026-09-27T22:00:00.000Z · 2026-12-20T23:00:00.000Z · München · Combined Learning
• 2026-09-27T22:00:00.000Z · 2027-03-14T23:00:00.000Z · München · Combined Learning
• 2026-10-25T23:00:00.000Z · 2027-04-11T22:00:00.000Z · München · Combined Learning