Data has become the lifeblood of modern business. However, as the volume of data grows exponentially, managing it has become an increasingly daunting task. The challenge is compounded by data coming from various sources, in different formats, and at different speeds. This is where data architecture provides a roadmap for describing, collecting, storing, processing, and analyzing data to meet business needs. A well-designed data architecture provides an abstract view of data-intensive applications, making it easier to transform data into valuable information. We must take these challenges seriously and invest in robust data architecture to effectively manage and use data to our advantage. To propose a comprehensive data architecture framework to improve data quality monitoring through automated quality checks. The architecture framework utilizes Model Driven Engineering (MDE) techniques. Its support for data-intensive architecture descriptions enables the automated generation of data quality checks. DAT Framework offers a comprehensive solution for data-intensive applications to model their architecture efficiently and monitor the quality of their data. It automates the entire process and improves precision and consistency in data quality monitoring. With DAT, architects and analysts gain access to a tool that simplifies their workflow and empowers them to make informed decisions based on reliable data insights. We have evaluated the DAT in five cases within various industry domains, demonstrating its effectiveness and efficiency. The evaluation demonstrates that the DAT framework achieves around 96% modeling accuracy and reduces modeling time by 63%, enhancing efficiency. Its automated data quality validation ensures reliable and consistent monitoring in data-intensive applications.

Context - The Internet of Things (IoT) refers to a distributed network of smart, connected devices that collaboratively sense, process, and act upon real-world environments. Designing such systems requires managing complex architectural concerns spanning software logic, hardware configuration, and spatial deployment, as well as validating non-functional properties like energy consumption and communication efficiency. Objective - To provide a unified, architecture-centric framework that supports the description, simulation, and automated code generation of IoT applications across software, hardware, and physical space dimensions. Method - We use Model Driven Engineering(MDE) approaches to develop CAPS, a framework that uniquely integrates multi-view architectural modeling, energy- and traffic-aware simulation via CupCarbon, and seamless generation of deployable Arduino code from high-level design models. Result - CAPS enables a traceable and cohesive development process from architectural design to physical deployment. Case studies from diverse domains demonstrate its ability to improve modeling expressiveness, maintain transformation fidelity, and reduce development time through automation. Conclusion - CAPS unifies architectural modeling, simulation, and code generation into a novel, end-to-end toolchain, addressing fragmentation in the IoT development lifecycle and enhancing early validation and traceability.