Topic 6

Lesson 6. Cloud Ecosystems, Collaborative Processing, and No-Code Automation. Google Sheets, Microsoft 365. Foundations of Data Transfer Automation Between Services (Power Automate / Make / n8n)

Objective: formation of students with a complex of theoretical knowledge, architectural thinking and practical skills in the field of organizing collective information processing in modern cloud ecosystems, configuring collaboration tools and designing inter-service integration pipelines based on No-Code / Low-Code platforms. The student must master the prerequisites of evolution from local systems to cloud workspaces, understand the architectural specifics of the Cloud-Native platform of Google Workspace and the hybrid corporate ecosystem of Microsoft 365, as well as grasp the principles of event-driven architecture (Event-Driven Architecture) and the concept of overcoming the problem of "isolated data islands" (Data Silos). During the training, special attention is paid to the practical mastering of collective security and integrity control mechanisms in cloud spreadsheets (separation of Viewer/Commenter/Editor access roles, protection of ranges with calculation formulas, version audit according to the PITR principle and input data validation rules), as well as the use of dynamic cloud functions (IMPORTRANGE, FILTER, UNIQUE, SORT and the SQL-like QUERY function). Students must learn to construct automated data transfer chains, distinguish between the mechanics of polling (Polling) and instant triggers (Webhooks), perform field mapping (Data Mapping), filtering and routing of flows between sources and target actions (Actions). An important analytical and comparative result is the formation of the ability to reasonably choose iPaaS platforms in accordance with the technical and security requirements of the organization (differentiating between the corporate ecosystem with RPA support Microsoft Power Automate, the interactive visual canvas of the Make cloud service, and the Open-Source/Self-Hosted platform n8n with the ability to execute custom code in JavaScript/Python) to create reliable, scalable, and automated analytical pipelines in real time.