Enterprise Technology Terminology

5,405 results · page 67 of 271

• Data Ontology

  Data ontology is a formal semantic model that defines enterprise data concepts, attributes, and relationships in a machine-interpretable way, enabling consistent meaning, interoperability, and reasoning across heterogeneous systems for integration, analytics, governance, and regulatory or policy-aligned data use.

• DataOps

  DataOps is an organizational practice that applies agile, DevOps, and process control principles to how enterprises build and operate data pipelines and analytics, enabling more reliable, automated, and governed delivery of data needed for reporting, decision support, and machine learning.

• Data Orchestration

  Data orchestration is the automated coordination and control layer for data movement and processing tasks across enterprise systems, enabling consistent, policy-governed data workflows that support analytics, applications, and compliance requirements in complex, hybrid, and cloud data environments.

• Data Orchestration Framework

  Data orchestration framework is a structured software layer that coordinates, schedules, and monitors automated data workflows and dependencies across enterprise data systems, enabling repeatable, auditable delivery of data for analytics, governance, compliance, and operational use cases in heterogeneous technology environments.

• Data Orchestration Layer

  Data orchestration layer is a software control layer that defines, schedules, and coordinates end-to-end data workflows across multiple platforms, enabling centralized management, monitoring, and policy enforcement for data movement and processing in enterprise data and analytics environments.

• Data over Cable Service Interface Specifications

  Data over Cable Service Interface Specifications (DOCSIS) is a cable industry standard that defines how broadband IP data services operate over hybrid fiber-coaxial networks, which matters for enterprises that rely on cable-based last-mile connectivity, performance planning, and security design.

• Data Ownership Model

  Data ownership model is a formal construct that allocates legal, governance, and operational rights and responsibilities over data assets to defined roles, enabling organizations to manage accountability for access, use, quality, security, and compliance across distributed data environments.

• Data Parallelism

  Data parallelism is a parallel computing approach in which the same computation runs concurrently on different partitions of a data set across processors or nodes, enabling enterprises to scale analytics, simulations, and machine learning workloads across available infrastructure.

• Data Parallel Library

  Data parallel library is a programming library that enables the same operation to run concurrently across many data elements, helping enterprises exploit multicore, GPU, or distributed hardware for analytics, simulation, and AI workloads while supporting performance portability and maintainable parallel code.

• Data Persistence

  Data persistence is the property of data to remain stored and retrievable on durable, nonvolatile media beyond the lifetime of the process that created it, which supports continuity, compliance, and reliable recovery in enterprise information systems.

• Data Pipeline

  Data pipeline is a controlled, automated set of processes that moves and transforms data from source systems to target environments. It matters in enterprises because it provides reliable data delivery for analytics, reporting, governance, and regulatory or operational requirements.

• Data Pipeline Monitoring

  Data pipeline monitoring is the continuous tracking of data flows, jobs, and infrastructure across a data pipeline to observe reliability, performance, and data quality, enabling enterprises to maintain data availability, meet service objectives, and support governance and compliance requirements.

• Data Pipeline Orchestrator

  Data pipeline orchestrator is software that defines, schedules, and coordinates data workflows and tasks across systems in enterprises, providing dependency management, monitoring, and failure handling for pipelines that support analytics, reporting, and machine learning workloads in complex data environments.

• Data Pipelines

  Data pipelines are automated processes that move and transform data from source systems to target platforms under defined rules and schedules, enabling enterprises to deliver governed, observable, and reusable data flows for analytics, compliance, and operational decision support.

• Data Plane

  Data plane is the part of a network or distributed system that performs real-time forwarding and processing of data traffic based on policies from the control plane, which matters for enterprise performance, security enforcement, and consistent operations across environments.

• Data Plane Acceleration

  Data plane acceleration is the use of hardware and software techniques to increase the performance and efficiency of packet and data processing in a network or compute data plane, which supports enterprise-scale traffic volumes and service-level objectives.

• Data Plane Development Kit

  Data Plane Development Kit (DPDK) is an open source collection of user space libraries and drivers for high-throughput, low-latency packet processing on commodity servers, used in virtual network functions, cloud networking, and telecom data planes to accelerate software-based networking.

• Data Plane Monitoring Agent

  Data Plane Monitoring Agent is a component that runs on network, cloud, or data processing infrastructure to collect telemetry from the data plane for performance, reliability, and security monitoring, enabling enterprises to observe actual traffic and data flow behavior in operation.

• Data Plane Optimization

  Data plane optimization is the engineering and tuning of how networks and distributed systems process live traffic, used by enterprises to increase throughput, reduce latency, and improve resource utilization while maintaining required reliability, security controls, and service-level objectives.

• Data Poisoning

  Data poisoning is an attack on machine learning and AI systems in which adversaries corrupt training or input data, affecting model accuracy or behavior. It matters in enterprises because it compromises predictions, business processes, security controls, and regulatory compliance.