Data
Data Mesh
Data mesh is an organizational and architectural approach to data management that distributes data ownership to the business domains that produce it, rather than centralizing all data in a single platform team. Each domain owns, documents, and serves its data as a product — a 'data product' — accessible to other domains through a self-serve infrastructure.
Data mesh was introduced by Zhamak Dehghani as a response to the bottlenecks created by centralized data lake and warehouse teams. In AI contexts, data mesh enables faster AI development by giving AI teams direct, trusted access to high-quality domain data without waiting for a central team. The four pillars are: domain ownership, data as a product, self-serve infrastructure, and federated computational governance.
Related terms
- Data Lake — A data lake is a centralized repository that stores structured, semi-structured, and unstructured data in its raw format at any scale.
- Feature Engineering — Feature engineering is the process of transforming raw data into the input representations that machine learning models use to make predictions.
- MLOps — MLOps (Machine Learning Operations) is the set of practices that operationalize ML and AI models in production — covering CI/CD pipelines for model updates, automated testing, performance monitoring, data versioning, and rollback procedures.