Enterprise Technology Glossary

6,173 results · page 79 of 309

• Data Sharding

  Data sharding is a database partitioning technique that distributes data across multiple independent shards to support scalability, performance, and availability objectives in large enterprise systems, while enabling capacity expansion through additional nodes instead of scaling a single database instance.

• Data Sharding Strategy

  Data sharding strategy is the structured plan for horizontally partitioning data across multiple databases or storage nodes in an enterprise environment, enabling controlled scalability, performance management, and alignment with operational, governance, and service-level requirements.

• Data Sharing Agreement

  Data sharing agreement is a formal contract that governs how organizations exchange, access, and use data under defined legal, security, and privacy conditions. It matters in enterprise contexts because it documents roles, safeguards, and permitted purposes for inter-organizational data flows.

• Data SLA Enforcement

  Data SLA enforcement is the implementation of monitoring, controls, and operational processes that ensure data services meet defined service-level agreements for availability, performance, quality, security, and recovery, providing predictable and auditable behavior of enterprise data platforms and data products.

• Data Source Monitor

  Data source monitor is a software component that tracks the health, performance, availability, and security-relevant activity of enterprise data sources, providing metrics and alerts that support observability, incident response, risk management, and compliance for database and data platform environments.

• Data Sovereignty

  Data sovereignty is the principle that data is governed by the laws and regulatory requirements of the jurisdiction where it is collected, stored, or processed, which matters to enterprises for compliance, cloud architecture design, vendor selection, and cross-border data management.

• Data Sovereignty Policy

  Data sovereignty policy is an organization’s formal rule set for managing where data is stored and processed under specific national or regional laws, guiding architecture, cloud deployment, and controls to maintain compliance with jurisdictional and cross-border data requirements.

• Data Staging

  Data staging is the controlled intermediate environment and process where enterprises land, validate, cleanse, and transform data from multiple sources before loading it into data warehouses, data lakes, or other analytic platforms, supporting governance, quality assurance, and reliable reporting.

• Data Stewardship

  Data stewardship is the role-based discipline for managing and overseeing data assets so they remain accurate, secure, compliant, and usable across the enterprise. It assigns accountable stewards to enforce data policies, standards, and controls across systems, processes, and business domains.

• Data Stewardship Program

  Data stewardship program is an organizational framework that assigns roles, processes, and controls for managing enterprise data assets, ensuring defined accountability for data quality, protection, compliant use, and consistency across systems for governance, security, analytics, and regulatory obligations.

• Data Stewardship Role

  Data stewardship role is a formally defined position that manages data quality, governance, and authorized use for specific data domains or datasets, supporting consistent definitions, regulatory compliance, and dependable analytics in enterprise environments.

• Data Storage

  Data storage is the process and technology stack that records, preserves, and retrieves digital data on physical or virtual media for use by enterprise applications and users, forming a core dependency for operations, analytics, compliance, and resilience strategies.

• Data Stream

  Data stream is a continuous, ordered flow of data records generated and transmitted over time for real-time processing or analysis. It matters in enterprises because it underpins event-driven architectures, monitoring, and analytics that depend on current, time-ordered information flows.

• Data Streaming Fabric

  Data streaming fabric is an architectural layer that provides a unified, governed substrate for publishing, routing, and consuming real-time data streams across distributed systems, supporting event-driven workloads, real-time analytics, and integration between operational and analytical platforms in enterprise environments.

• Data Striping

  Data striping is a storage technique that splits data into units and distributes them across multiple disks or devices to increase aggregate throughput and balance I/O load, which supports enterprise performance objectives for databases, analytics, virtualization, and other data-intensive workloads.

• Data Subject Access Request

  Data Subject Access Request is a formal request under data protection law by an individual to an organization for confirmation and access to personal data processed about them, which requires structured discovery, review, and disclosure workflows in enterprise environments.

• Data Synchronization

  Data synchronization is the governed process of keeping data values aligned across multiple systems, databases, or applications, ensuring consistency and currency for shared datasets so enterprises can maintain coherent records and predictable behavior across distributed architectures and environments.

• Data Synchronization Service

  Data Synchronization Service is a software capability that maintains consistent data across multiple systems or replicas by detecting changes and propagating updates under defined rules, which matters in enterprises that operate distributed applications, hybrid environments, and regulated data management practices.

• Data Taxonomy

  Data taxonomy is a structured classification scheme that organizes enterprise data into defined categories and hierarchies to support consistent labeling, governance, access control, and reuse across systems, enabling alignment between business domains, security policies, and metadata-driven data management practices.

• Data Telemetry Pipeline

  Data Telemetry Pipeline is a structured process that collects, transports, and prepares machine-generated telemetry data, such as logs, metrics, and traces, for monitoring, analytics, and governance in enterprises, enabling observability, incident analysis, capacity planning, and compliance-related investigations.