THESIS: From Raw Data to Insights: Comparing Modern Data Engineering Tools

High level descriptionThis thesis focuses on evaluating different predictive maintenance models using provided industrial datasets. The goal is to determine which models are most effective in predicting failures and enabling proactive maintenance. The study will compare approaches such as anomaly detection, time-series forecasting, and classification-based...

High level descriptionThis thesis focuses on evaluating different predictive maintenance models using provided industrial datasets. The goal is to determine which models are most effective in predicting failures and enabling proactive maintenance. The study will compare approaches such as anomaly detection, time-series forecasting, and classification-based...

High level descriptionEffort and time estimation remain among the most challenging aspects of software engineering. Despite decades of research, human estimates are still often uncertain and inconsistent.With the rise of advanced AI assistants and code-generating tools such as GitHub Copilot, ChatGPT, Amazon CodeWhisperer, and similar systems, new...

High level descriptionInfrastructure as Code (IaC) is a cornerstone of modern software development and cloud operations. Terraform is one of the most widely adopted tools for provisioning and managing infrastructure across cloud providers, while is a newer framework with a developer-centric focus, combining infrastructure and application logic in a single...

High level descriptionModern data engineering faces unprecedented challenges as organizations increasingly deploy IoT devices and edge computing infrastructure that generate massive volumes of distributed data.Traditional centralized data processing approaches struggle with the latency, bandwidth, and reliability constraints inherent in edge-to-cloud data...

BackgroundIn today's digital landscape, services and offerings are increasingly composed of multiple interconnected applications and systems. This complexity makes it difficult to trace which services are tied to a specific customer identifier and to detect issues such as failed orders or recurring system errors.Traditionally, support and operations teams...

High level descriptionGenerative AI is increasingly used in software development to write code, tests and suggest solutions. This increases productivity but at the cost of several risks, one being security. The AI tools being used may generate code that contains vulnerabilities, reproduce insecure patterns from training data, or create a false sense of...

High level descriptionIndustries increasingly collect sensor data from equipment, but much of this data is not fully used for real time insights or predictive maintenance. A device-to-cloud prototype can demonstrate how even simple hardware can generate valuable data for health monitoring and failure prediction.Who are we looking for?Bachelor/Master of...

AbstractManaging third-party dependencies is critical for software security, yet existing tools such as Dependabot treat all version updates and reported vulnerabilities as equally urgent. This lack of prioritization leads to alert fatigue and wasted developer effort. This thesis explores how AI can make dependency risk management more contextual and...

High level descriptionWith the rising popularity of AI agents, Anthropic has developed a new protocol for LLM models to communicate with tools. The Model Context Protocol (MCP) is slowly becoming a standard for developing tools for LLMs and LLM Agents. In MCP there are no security or authentication methods, so exposing tools towards end users can be harmful...