Data Platform Services enable faster access to trusted data across distributed environments by utilizing active metadata, knowledge graphs, semantics and machine learning (ML) capabilities of data integration (as well as other data management tools, including data catalogs and data governance). The Data Platform Services must be able to deliver data in various styles (not just batch, but a combination of batch with data virtualization, streaming, messaging or API-based delivery styles). Data Platform Services integrate data from different sources to provide consistency across multiple environments or systems, or technologies leveraged in an enterprise. Data Platform Services aim to provide an abstraction layer above all of the different services and systems that it touches to create more fluidity across data environments, by accessing data in place or support its consolidation where appropriate. Data Platform Services abstract away the technological complexities engaged for data movement, transformation and integration, making all data available across the enterprise. Data Platform Services utilize continuous analytics over existing, discoverable and inferenced metadata assets to support the design, deployment and utilization of integrated and reusable data across different data storage technologies and multiple environments, including hybrid and multi-cloud platforms. Data Platform Services continuously identify, connect and analyze data and metadata from disparate sources to discover unique, operationally-relevant relationships between the available data points. A data fabric architecture provides flexible, reusable and augmented data management (i.e. better semantics, integration and organization of data) through metadata. Metadata drives the fabric design. Compared to traditional approaches, active metadata and semantic inference are key new aspects of a data fabric to discover new insights. A data fabric utilizes continuous analytics of existing, discoverable and inferenced metadata assets to support the design, deployment and utilization of integrated and reusable data objects regardless of deployment platform. It can include automated orchestration for data access, data integration, data quality, use of knowledge graphs, and even data utilization and usage recommendations. A data fabric utilizes as much metadata as is available from any other contributing data management platform or tools. Fabric designs evolves over time. Initially, existing systems can passively participate in the fabric design by sharing their metadata. A metadata-driven data fabric has significant potential to reduce data management efforts, including design, deployment and operations, supporting the DevSecOps approach for modern software development. When the fabric design matures, participating systems actively adapt to the alerts and recommendations generated by the fabric through data analytics, data and AI orchestration.