**Data architecture** is a discipline within [[Enterprise Architecture (ServiceNow)|enterprise architecture]] that defines the models, policies, rules, and standards governing how [[Data|data]] is collected, stored, integrated, and used within an organization. It serves as a master plan for managing [[Data asset|data assets]], providing the blueprint for [[Data pipeline|data pipelines]], [[Cloud computing|cloud storage]] infrastructure, [[Data governance|data governance]] tools, and specifications for [[Data integration|data integration]] and transformation. The practice involves designing multilayer infrastructures that align with organizational goals and business requirements. A [[Data architect|data architect]] develops and coordinates the architectural blueprint, ensuring that [[Data management|data management]] systems support collaboration across departments and stakeholders. Core components of modern data architectures include [[Application programming interface|APIs]], [[Artificial intelligence|AI]] and [[Machine learning|machine learning]] models, [[Data streaming|data streaming]] capabilities, [[Real-time computing|real-time analytics]], and container orchestration platforms such as [[Kubernetes|Kubernetes]]. Contemporary data architecture has evolved significantly with the widespread adoption of cloud computing. Emerging trends include the shift from centralized [[Data lake|data lakes]] toward domain-based designs, the rise of modular architectures using [[Open-source software|open-source]] components, and the growth of [[Data fabric|data fabric]] approaches for hybrid and [[Multi-cloud|multi-cloud]] environments. Organizations increasingly require architectures capable of handling [[Big data|big data]] volumes while supporting advanced applications such as [[Predictive analytics|predictive analytics]] and [[Applied artificial intelligence|applied AI]]. # Highlights and notes > Data Architecture 101 > > According to the [Data Management Body of Knowledge](https://www.dataversity.net/what-is-the-data-management-body-of-knowledge-dmbok/) (DMBoK 2), data architecture is akin to a “master plan” to manage data assets. Internal organizational policies and business policies often guide the data architecture design. Data architecture deliverables include multilayer infrastructures for data platforms and Data Governance tools, and specifications and standards for data collection, integration, transformation, and storage. > > While [both data architecture and data modeling](https://analyticsindiamag.com/understanding-the-difference-between-data-architecture-data-model/) strive to align data technologies with business goals, data architecture is solely focused on maintaining relationships between business functions, technologies, and data. In short, data architecture sets standards across data systems, acting as a vision or a pattern for how data systems interact. Data architects develop a vision and blueprint of an organization’s data infrastructure, and the [data engineers](https://www.dataversity.net/data-architect-vs-data-modeler-vs-data-engineer/) are in charge of creating this vision.