Engineering Glossary

A single set of stashed changes in Git, which can be reapplied later to the working directory.

Automated tests or processes that run when changes are proposed in a Git repository, ensuring code quality and compatibility.

Notifications or updates a user receives about activity in Git repositories they're interested in or contributing to.

The main Git repository that contains submodules, managing references to specific versions of nested repositories.

A symbolic reference in Git that points to another reference, most commonly used for HEAD pointing to the current branch.

A Git object containing metadata about a tag, including the tagger, date, and optional message.

A group of users on a Git platform with shared access permissions to repositories within an organization.

A user with administrative privileges for managing a team's membership and access rights in a Git organization.

A chronological display of events and activities in a Git repository or user profile on hosting platforms.

A short-lived Git branch created to develop a specific feature or fix a particular issue.

Keywords or categories assigned to Git repositories to help users discover related projects on hosting platforms.

A visual representation of visitor activity and clone statistics for a Git repository on hosting platforms.

The process of moving Git objects between repositories during push, fetch, or clone operations.

A Git object representing a directory structure, containing references to blobs (files) and other trees (subdirectories).

A Git object that stores the hierarchy of files and directories in a repository at a specific point in time.

A Git term referring to an object that resolves to a tree, such as a commit, tag, or tree.

A state of a Git branch that has no commits yet, typically seen when initializing a new repository.

The state of the Git index containing conflicting changes from different branches during a merge operation.

A Git object not accessible from any reference, potentially subject to garbage collection if not recovered.

The remote branch that a local branch is set to track, used as a reference for pull and push operations.

An individual account on a Git platform, associated with personal repositories and contributions.

An authenticated request from a Git client to a server, typically for operations like push or fetch.

The unique identifier for a user account on Git platforms, used for authentication and mention notifications.

A team in a Git organization that is visible to all members of the organization, as opposed to secret teams.

A feature allowing users to receive notifications about activity in Git repositories they're interested in.

Alerts received for all notable events in a watched Git repository, including issues, pull requests, and releases.

Alerts displayed on Git platforms' web interfaces, notifying users of relevant activity or mentions.

The directory on your local machine where you modify files before staging and committing them in Git.

The set of files and directories in your project that are currently checked out and available for editing.

A Git feature allowing multiple working directories to be associated with a single repository.

Permission to make changes to a Git repository, including pushing commits and modifying branches.

High-density memory architecture using vertically stacked chips, enhancing performance and capacity in cloud server hardware.

Cloud infrastructure optimized for 5G networks, supporting high-speed, low-latency services and applications.

Technique to create multiple virtual networks on a shared physical 5G infrastructure, each optimized for specific use cases.

Integration of 5G networks with edge computing to enable low-latency, high-bandwidth applications closer to end-users.

Future convergence of 6G networks with cloud computing, promising ultra-high speeds and advanced capabilities.

Database transaction properties (Atomicity, Consistency, Isolation, Durability) ensuring data integrity in cloud environments.

Software ensuring AI systems adhere to ethical guidelines and regulations in cloud deployments.

Software for identifying and mitigating ethical issues and biases in AI models deployed in cloud environments.

Solutions for managing ethical considerations and regulatory compliance in cloud-based AI systems.

Structured approaches for managing AI development, deployment, and use in cloud environments.

Comprehensive solutions for managing, monitoring, and controlling AI models in cloud-based systems.

Cloud-based tools for explaining and understanding the decision-making processes of AI models.

Software for analyzing and explaining AI model decisions, crucial for transparency in cloud AI services.

Cloud platforms for discovering, sharing, and monetizing pre-trained AI models and algorithms.

Tools for tracking AI model performance and identifying deviations from expected behavior in cloud environments.

Systems for managing different versions of AI models and enforcing governance policies in cloud deployments.

Cloud-based development environments that use AI to assist programmers in writing and optimizing code.

Integration of AI capabilities into data analytics processes in cloud environments for enhanced insights.

Use of AI algorithms to predict and optimize resource allocation in cloud infrastructures.

Application of AI techniques to improve efficiency, performance, and cost-effectiveness of cloud resources.

Automated distribution of cloud resources using AI to optimize performance and cost-efficiency.

AI-based automation of cloud service selection and integration for complex workflows.

Automated creation of source code using AI models, often integrated into cloud development platforms.

Automated categorization and labeling of data using AI algorithms in cloud storage and processing systems.

Use of AI to improve network performance, efficiency, and security in cloud environments.

Intelligent distribution of computing resources in cloud environments using AI algorithms.

Enhanced SIEM systems using AI for improved threat detection and response in cloud environments.

Use of AI algorithms to identify and analyze potential security threats in cloud systems.

Proactive search for hidden threats in cloud environments using AI-powered analytics and automation.

Cloud infrastructure components designed or configured to enhance AI workload performance.

Database systems tailored for AI workloads, often featuring in-memory processing and distributed architectures.

Use of AI algorithms to identify unusual patterns or behaviors in cloud systems and data.

Intelligent automation of data and application integration processes in cloud environments using AI.

Cloud-based offering of AI capabilities, allowing businesses to leverage AI without extensive in-house expertise.

Cloud-based AI capabilities offered as a service, enabling easy integration of AI into applications.

Application of AI for automating and enhancing IT operations management in cloud environments.

Integrated solutions leveraging AI for automated monitoring, analysis, and management of cloud IT operations.

Use of AI-driven analytics to forecast and prevent system failures in cloud infrastructure.

Service that protects APIs in cloud environments by managing access, monitoring traffic, and preventing attacks.

Dedicated services for securing and managing APIs in cloud environments, including access control and threat protection.

Design approach prioritizing API creation before implementation, common in cloud-native application development.

Integration strategy using purpose-built APIs to connect data, devices, and applications in cloud ecosystems.

Persistent 3D digital content overlaid on the physical world, accessible via cloud infrastructure for augmented reality applications.

Cloud-based tools for analyzing user behavior and performance in augmented and virtual reality environments.

Cloud-hosted services enabling multi-user interaction in shared augmented or virtual reality spaces.

Specialized CDN optimized for delivering AR/VR content with low latency and high bandwidth.

Cloud-based tools and services for creating, testing, and deploying augmented and virtual reality applications.

JSON-based files defining infrastructure and configuration for Azure resource deployment.

Dynamic UIs that adjust based on user behavior, device capabilities, and context in cloud applications.

Techniques to identify and mitigate attacks on ML models in cloud-based AI systems.

Systems for informing administrators or users about important events or issues in cloud environments.

Process of examining AI algorithms for bias, errors, or unintended consequences in cloud-based systems.

Seamless, context-aware interaction methods for accessing cloud services in IoT environments.

Tools for identifying unusual patterns or behaviors in cloud systems, often using machine learning techniques.

Network management approach that optimizes performance based on specific application requirements in cloud environments.

Technique for quickly estimating query results in large-scale cloud databases, trading accuracy for speed.

Low-cost cloud storage for infrequently accessed data with longer retrieval times, e.g., Amazon Glacier, Azure Archive Storage.

Cloud-based storage for software build outputs, dependencies, and related metadata.

Hypothetical cloud offering of human-level AI capabilities across various domains.

Cloud-based environments for developing and testing advanced AI systems approaching human-level intelligence.

Systematic recording of actions and events in cloud systems for security and compliance purposes.

Chronological records of system activities for reconstructing and examining the sequence of events in cloud environments.

Integration of machine learning and natural language processing in cloud-based data analytics workflows.

Shared, persistent digital content overlaid on the physical world, accessible via cloud infrastructure.

Cloud-based systems for managing and delivering AR content and experiences, enabling scalable AR applications.

Cloud-powered generation of AR graphics and content, offloading processing from end-user devices.

Collections of EC2 instances that automatically adjust capacity based on defined conditions.

Automatic adjustment of cloud resources to match workload demands, ensuring optimal performance and cost-efficiency.

Automated machine learning processes for model selection, hyperparameter tuning, and feature engineering in cloud environments.

Cloud-based services automating the machine learning pipeline from data preparation to model deployment and monitoring.

AI-driven tools for improving efficiency and performance of machine learning workflows in the cloud.

Systems that automatically implement and maintain cloud resource policies and compliance.

Continuous, AI-driven assessment of cloud systems against regulatory and security standards.

AI-powered tools for identifying, categorizing, and mapping data assets in cloud environments.

AI-driven systems for managing data quality, security, and compliance in cloud environments.

Cloud-based tools using AI to clean, transform, and prepare data for analysis, streamlining data preparation processes.

AI-powered generation and selection of features for machine learning models in cloud environments.

AI-driven coordination of security incident detection and resolution in cloud systems, automating response workflows.

Cloud services that automate the process of creating and optimizing machine learning models, from data prep to deployment.

Cloud-based systems that automate the end-to-end machine learning model development process, including feature engineering and model selection.

AI-driven tools for simulating cyberattacks to identify vulnerabilities in cloud systems and applications.

AI-powered analysis of cloud architectures to identify potential security risks and attack vectors.

Self-managing, self-healing cloud services requiring minimal human intervention, leveraging AI for operations.

Ultra-low latency edge computing extensions of Azure for 5G networks, bringing cloud resources closer to users.

Service for creating, assigning, and managing policies to control Azure resources and ensure compliance.

Cloud-based services facilitating data exchange and process integration between business partners.

Architectural approach creating backend services tailored to specific frontend application needs in cloud environments.

Physical server dedicated to a single tenant, offering direct hardware access in cloud environments.

Dedicated physical servers without virtualization, offered as a cloud service for high-performance workloads.

Execution of series of jobs without user interaction, often for large-scale data processing in the cloud (e.g., AWS Batch, Azure Batch).

Distributed computing environments for processing and analyzing massive datasets in the cloud.

Interface for monitoring and managing cloud service usage and costs, providing detailed breakdowns and forecasts.

Use of cloud resources for computational biology and genomics research, enabling large-scale analysis and modeling.

Evaluation of cloud data center effects on local ecosystems and wildlife to ensure sustainable operations.

Cloud storage that provides fixed-size raw storage volumes, typically used for databases or file systems.

Cloud services providing persistent block-level storage volumes for use with compute instances.

Cloud-based tools for analyzing and visualizing blockchain data and transactions, offering insights into network activity.

Distributed databases using blockchain technology for enhanced security and immutability in cloud environments.

Software for managing and enforcing rules, permissions, and consensus mechanisms in blockchain networks deployed on cloud platforms.

Cloud-based solutions for connecting blockchain networks with existing enterprise systems and applications.

System for collecting, processing, and storing performance metrics from containerized applications and infrastructure.

Kubernetes add-on that collects resource metrics from kubelets for use in autoscaling and monitoring.

Techniques for improving the performance and efficiency of the Kubernetes Metrics Server for better monitoring.

Lightweight, CNCF-certified Kubernetes distribution designed for developer workstations, IoT devices, and CI/CD.

Reusable base structure for building microservices, providing common functionalities like logging and monitoring.

High-performance, S3-compatible object storage system, often used for persistent storage in container environments.

Feature allowing containers to share mounted volumes, enabling advanced storage scenarios in Kubernetes.

Design patterns for deploying multiple containers within a single pod, such as sidecar, ambassador, or adapter patterns.

Container images supporting multiple CPU architectures, enabling deployment flexibility across diverse hardware.

Tools and practices for administering and orchestrating multiple Kubernetes clusters as a unified system.

Technologies and configurations enabling network connectivity and service discovery across multiple Kubernetes clusters.

Coordinated management and deployment of containerized applications across multiple Kubernetes clusters.

Mechanisms for locating and accessing services deployed across multiple Kubernetes clusters.

Implementation of service mesh functionalities spanning multiple Kubernetes clusters for unified traffic management.

Dockerfile technique using multiple stages to create optimized, smaller container images by separating build and runtime environments.

Performance and isolation tests for evaluating the effectiveness of multi-tenant Kubernetes cluster configurations.

CNI plugin enabling attachment of multiple network interfaces to pods in Kubernetes, supporting advanced networking scenarios.

Container Network Interface plugin allowing multiple CNI plugins to be used simultaneously, enabling multi-network support for pods.

Use of Multus to attach pods to multiple networks, enabling complex networking scenarios in Kubernetes.

Admission controller webhooks that can modify resource requests before they are persisted, enabling dynamic resource manipulation.

Security protocol ensuring both client and server authenticate each other, commonly used in service mesh implementations.

Observability platform offering performance monitoring and analytics for containerized applications and infrastructure.

Cloud-native approach to solving L2/L3 use cases and service chain provisioning in Kubernetes.

Kubernetes feature considering Non-Uniform Memory Access topology for optimal pod placement.

Kubernetes abstraction for creating multiple virtual clusters within a physical cluster, providing a scope for names and resources.

Kubernetes feature for partitioning clusters into virtual sub-clusters, enabling resource isolation and access control.

Process of modifying network address information in packet headers for routing in container networks.

Concept of replacing dedicated network appliances with virtualized functions, often containerized.

Security technique dividing networks into isolated zones, enhancing container and microservices security.

Kubernetes resources specifying how groups of pods are allowed to communicate with each other and other network endpoints.

Specification of how groups of pods are allowed to communicate with each other and other network endpoints.

Feature enabling logging of allowed and denied network connections based on Kubernetes Network Policies.

Practice of dividing a network into smaller segments or subnets, improving security and performance in container environments.

Cloud-native solution providing layer 2/3 network services to Kubernetes workloads, enhancing networking capabilities.

Kubernetes resource defining rules for pod-to-pod and pod-to-external communication, implementing network segmentation.

Security platform providing real-time container firewall, vulnerability management, and compliance for Kubernetes.

Observability platform offering performance monitoring and analytics for containerized applications and infrastructure.

Basic unit of a distributed application in many systems, often referring to individual machines or processes.

Kubernetes feature allowing pods to be scheduled on nodes with specific characteristics based on labels.

Kubernetes authorization mode controlling kubelet access to the API server, enhancing cluster security.

Feature automatically adjusting the number of nodes in a Kubernetes cluster based on workload demands.

Prometheus exporter for hardware and OS metrics exposed by *NIX kernels, used for node-level monitoring in Kubernetes.

Kubernetes add-on detecting hardware features and system configuration, facilitating hardware-aware scheduling.

Unique identifier for a node in a Kubernetes cluster, used in various operations and configurations.

Process of removing pods from a node when it experiences resource pressure, ensuring stability.

Kubernetes add-on monitoring and reporting node health issues, enhancing cluster reliability.

Kubernetes plugin limiting node and pod modifications to increase security and prevent privilege escalation.

Kubernetes feature for constraining pods to nodes with specific labels, enabling basic scheduling control.

Procedure for updating the software stack on Kubernetes nodes while minimizing disruption to running workloads.

Capture and management of system and container logs at the node level in a Kubernetes cluster.

Kubernetes controller managing the lifecycle of nodes, handling node registration and decommissioning.

Kubernetes add-on running a dns caching agent on cluster nodes to improve DNS performance.

Kubernetes service type exposing the service on each node's IP at a static port, enabling external access.

Kubernetes services that expose applications on a static port on each node, facilitating external access.

Security practice of running containers with non-privileged users to reduce potential attack surface.

Network communication between external clients and services within a Kubernetes cluster, typically through ingress.

Open-source project providing secure content distribution and verification for container registries.

Implementation of Notary to ensure the integrity and authenticity of container images, enhancing supply chain security.

Open Container Initiative specification defining an API protocol for distributing container images.

Open Container Initiative specification defining the format of container images, ensuring interoperability.

Detailed standard for container image format, ensuring interoperability between different container technologies.

Standard defining the configuration, execution environment, and lifecycle of containers, ensuring compatibility.

Linux kernel mechanism for terminating processes when the system runs out of memory, affecting container reliability.

Integration of Open Virtual Network with Kubernetes for advanced software-defined networking.

Open-source developer-centric web interface for visualizing Kubernetes cluster state and workloads.

Industry standard for container runtime implementations, ensuring compatibility across platforms.

General-purpose policy engine for cloud-native environments, often used for Kubernetes admission control.

OPA-based system for declaring, managing, and enforcing policies in Kubernetes environments.

Container-attached storage solution providing persistent storage for stateful workloads in Kubernetes.

OpenEBS feature for provisioning local persistent volumes, optimizing performance for I/O intensive workloads.

Framework for building serverless functions with Docker and Kubernetes, simplifying the deployment of event-driven workloads.

JSON Web Tokens used for authentication in Kubernetes, often integrated with external identity providers.

Vendor-agnostic way to receive, process, and export telemetry data in various formats for observability.

Vendor-neutral API for distributed tracing, facilitating the instrumentation of microservices for observability.

Kubernetes extension designed for edge computing scenarios, enabling efficient management of edge nodes.

Kubernetes extension that oversees installation, upgrades, and management of Operators in a cluster.

Feature for usage reporting and billing in Kubernetes clusters, often used with Operators.

Performance and health indicators exposed by Kubernetes Operators for monitoring and alerting.

Kubernetes extension pattern for automating application-specific operational knowledge and tasks.

Framework for building Kubernetes Operators, simplifying the development process with high-level APIs.

Tool for validating Operators against best practices and providing suggestions for improvements.

Public registry for finding and sharing Kubernetes Operators, facilitating discovery and distribution.

Kubernetes plugins developed and maintained outside the main Kubernetes repository, enabling extensibility.

Design pattern ensuring consistency between database and message broker in distributed systems.

Process of identifying abnormal behavior in containerized applications or infrastructure for troubleshooting.

Additional resource consumption introduced by container runtimes and orchestration systems.

Layered filesystem used by container runtimes to efficiently manage image layers and container writes.

Virtual networks created on top of existing networks, enabling container-to-container communication across hosts.

Base container image upon which other images are built, providing foundational layers for child images.

Service mesh feature ensuring mutual authentication between services using mTLS, enhancing security.

Kubernetes resource representing a request for storage by a user, abstracting storage implementation details.

Kubernetes abstraction for a piece of storage in the cluster, managed independently of individual pods.

Kubernetes resource representing a piece of storage in the cluster that has been provisioned by an administrator.

Kubernetes resource representing a request for storage by a user, abstracting the details of the underlying storage implementation.

Practice of defining CI/CD pipelines using code, often stored alongside application code for version control and review.

Open-source observability tool for Kubernetes, providing automatic data collection and real-time debugging capabilities.

Use of Pixie to gain deep insights into Kubernetes cluster performance and application behavior without code changes.

Smallest deployable unit in Kubernetes, consisting of one or more containers sharing storage and network resources.

Kubernetes scheduling constraint that allows pods to be co-located with other pods based on labels.

Kubernetes features for influencing pod scheduling based on the topology of the cluster and other pods.