Data mesh is a decentralized approach to managing data that covers different layers of architecture: data, application, and technology. related to domain ownership, data as a product, self-service infrastructure, and federated computational governance. However, according to James Serra, many concerns regarding data mesh exist, such as “no standard definition of data mesh, huge investments required for its implementation, performance problems of combining data from different domains, etc.”
Efrain Rodriguez demonstrated the progress cambodia whatsapp number data the U.S. Department has made in applying the data mesh concept to its practices.
According to John O’Brien, the choices regarding the required data integration architecture must be made based on aligning data architecture principles and best practices, business needs in analytics solutions, and technology and vendor products. As James Serra said, “No one-size-fits-all architecture” exists.
Data architecture for operational systems differs from data integration architecture.
According to Dave Wells, the problem with the current approach is that all data integration architectures discussed above focus on analytical data, while operational systems have complex, many-to-many connections. Examples of operational systems architecture are operational data stores and hubs.