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A data architect is a practitioner of data architecture, a data management discipline concerned with designing, creating, deploying and managing an organization's data architecture. Data architects define how the data will be stored, consumed, integrated and managed by different data entities and IT systems, as well as any applications using or ...
During the definition of the target state, the data architecture breaks a subject down to the atomic level and then builds it back up to the desired form. The data architect breaks the subject down by going through three traditional architectural stages: Conceptual - represents all business entities.
The Architecture Continuum specifies the structuring of reusable architecture assets and includes rules, representations, and relationships of the information systems available to the enterprise. The Solutions Continuum describes the implementation of the Architecture Continuum by defining reusable Solution Building Blocks (SBBs).
Information architecture (IA) is the structural design of shared information environments; the art and science of organizing and labelling websites, intranets, online communities and software to support usability and findability; and an emerging community of practice focused on bringing principles of design, architecture and information science to the digital landscape. [1]
Data mesh is a sociotechnical approach to building a decentralized data architecture by leveraging a domain-oriented, self-serve design (in a software development perspective), and borrows Eric Evans’ theory of domain-driven design [1] and Manuel Pais’ and Matthew Skelton’s theory of team topologies. [2]
Data Warehouse and Data mart overview, with Data Marts shown in the top right. In computing, a data warehouse (DW or DWH), also known as an enterprise data warehouse (EDW), is a system used for reporting and data analysis and is a core component of business intelligence. [1] Data warehouses are central repositories of data integrated from ...
Data quality – data access is controlled through data services, which tends to improve data quality, as there is a single point for updates. Once those services are tested, only regression testing is needed, if they remain unchanged for the next deployment.
Data engineering refers to the building of systems to enable the collection and usage of data. This data is usually used to enable subsequent analysis and data science, which often involves machine learning. [1] [2] Making the data usable usually involves substantial compute and storage, as well as data processing.