Engineering Glossary

A Git reference with a specific meaning or function, such as HEAD or FETCH_HEAD.

A deployment environment used for testing changes before releasing to production in Git-based workflows.

A feature on Git hosting platforms allowing users to bookmark repositories of interest.

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.

Design pattern using specialized containers that run before app containers in a pod, used for setup or dependency management.

Specialized containers that run to completion before other containers in a Kubernetes pod, used for initialization tasks.

Specification of CPU and memory limits for initialization containers in Kubernetes pods, ensuring efficient resource utilization.

Deprecated Kubernetes feature for dynamically modifying resource configurations before they become visible to the system.

Methods and patterns for containers within a pod to exchange data and signals, utilizing shared volumes or localhost networking.

Architectural designs for deploying and managing containerized applications on edge devices in Internet of Things scenarios.

Open-source service mesh providing traffic management, security, and observability for cloud-native applications.

Core services of the Istio service mesh responsible for configuring proxies and implementing policies.

Istio traffic management resource defining policies that apply to traffic intended for a service after routing has occurred.

Istio resources managing inbound and outbound traffic for the service mesh, providing fine-grained control over traffic flow.

Istio design that removes the Mixer component, improving performance and scalability of the service mesh.

Istio resources allowing the addition of entries to the service registry, enabling traffic to external dependencies.

Istio traffic management resources defining a set of traffic routing rules to apply when a host is addressed.

Istio feature providing a sidecar-less service mesh implementation, reducing resource overhead and simplifying deployment.

Open-source, end-to-end distributed tracing system for monitoring and troubleshooting microservices-based distributed systems.

Kubernetes operator for automated deployment and management of Jaeger tracing infrastructure in container environments.

Implementation of distributed tracing using Jaeger to monitor and optimize performance in microservices architectures.

Cloud-native CI/CD solution for Kubernetes, automating the entire software development lifecycle for containerized applications.

Tool for building optimized Docker and OCI images for Java applications without the need for a Docker daemon.

Kubernetes resource that creates one or more pods to perform a specific task and ensures they successfully complete, ideal for batch processes.

Lightweight Kubernetes distribution designed for edge, IoT, and resource-constrained environments, simplifying cluster deployment.

Terminal-based UI for managing and monitoring Kubernetes clusters, providing an intuitive interface for cluster operations.

Kubernetes-based Event Driven Autoscaling, enabling fine-grained autoscaling for event-driven workloads based on event sources.

Kubernetes Universal Declarative Operator, a toolkit for writing operators using a declarative spec, simplifying complex application management.

Tool for building container images from a Dockerfile inside a container or Kubernetes cluster, without requiring a Docker daemon.

Framework for application-level data management in Kubernetes, ensuring consistent backups of stateful apps.

Open-source project for multi-cluster application management in Kubernetes, facilitating resource distribution across clusters.

Lightweight virtual machines that feel and perform like containers, but provide the workload isolation and security of VMs.

Major update to Kata Containers, introducing improved performance, reduced footprint, and enhanced security features.

Cloud-native application lifecycle orchestration tool, automating deployment, testing, and remediation for containerized apps.

Technique allowing network packets to bypass the kernel, improving performance in container networking scenarios.

Linux feature providing isolation for system resources, fundamental to container implementation and resource separation.

CNI plugin using WireGuard to create a mesh overlay network for Kubernetes, enhancing security and simplicity.

Kubernetes-based platform for building, deploying, and managing serverless workloads, simplifying serverless application development.

Knative component providing tools for routing events between publishers and subscribers in cloud-native applications.

Knative component for deploying and managing serverless workloads, handling scaling and routing of containerized applications.

Tool allowing WebAssembly modules to run in Kubernetes clusters, enabling language-agnostic workloads.

Network virtualization solution for Kubernetes integrating the Open vSwitch software switch to provide advanced networking features.

Implementation of Kube-OVN to leverage Open vSwitch for flexible, high-performance container networking.

Tool for checking whether Kubernetes deployments adhere to Center for Internet Security (CIS) best practices.

Use of kube-bench to perform automated security audits of Kubernetes clusters against established benchmarks.

Kubernetes networking solution providing direct container-to-container networking with BGP-based routing.

Tool for static code analysis of Kubernetes object definitions, identifying misconfigurations and potential issues.

Service that listens to the Kubernetes API server and generates metrics about the state of Kubernetes objects.

Open-source system extending native containerized application orchestration capabilities to hosts at Edge.

Tool for bootstrapping a minimum viable Kubernetes cluster, simplifying the process of setting up a production-grade cluster.

Open-source tool for auditing Kubernetes clusters and workloads for potential security issues and best practice violations.

Framework for building Kubernetes APIs using custom resource definitions (CRDs), simplifying the creation of operators and controllers.

Tool providing real-time cost visibility and insights for running applications on Kubernetes, optimizing resource allocation.

Kubernetes Cluster Federation, enabling resource management across multiple Kubernetes clusters from a single control plane.

Kubernetes-native serverless framework, allowing deployment of small bits of code (functions) without managing underlying infrastructure.

System for evolving the Kubernetes API over time, allowing for backwards compatibility and gradual feature deprecation.

Plugins intercepting requests to the Kubernetes API server before object persistence, enforcing policies.

Feature allowing the Kubernetes API to be extended with additional APIs, enabling custom resources and controllers.

Key-value pairs for attaching non-identifying metadata to objects, used for tooling and client retrieval.

Mechanisms (HPA, VPA, Cluster Autoscaler) for automatically adjusting resources based on demand and metrics.

CNCF initiative ensuring consistency and portability across Kubernetes distributions and installations.

API objects automatically created when other objects change state, providing a cluster activity audit trail.

Mechanism for coordinating multiple Kubernetes clusters, enabling multi-cluster application deployment and management.

Field on resources preventing accidental deletion, ensuring proper cleanup of related resources or external dependencies.

Process of automatically deleting objects that no longer have owners, preventing resource leaks.

Evolved ingress and service networking, providing a more expressive, extensible way to configure traffic routing.

Key-value pairs attached to objects for identification and selection, crucial for organizing and selecting object subsets.

Cluster-wide aggregator of resource usage data, providing CPU and memory metrics for Horizontal Pod Autoscaler.

Software extensions to Kubernetes that use custom resources to manage applications and their components.

Relationship between API objects where one object owns another, used for cascading deletion and orphan handling.

Label query over a set of resources, used for identifying and grouping API objects based on their labels.

Policies and timelines for Kubernetes version releases, support, and deprecation, guiding upgrade planning.

Fully functional Kubernetes clusters running on top of other Kubernetes clusters, enabling multi-tenancy.

Web-based tool for security risk analysis of Kubernetes resource manifests, identifying vulnerabilities.

Kubernetes native configuration management tool that allows customization of YAML manifests without templating.

Technique in Kustomize for creating variations of a common base of Kubernetes resources, enabling environment-specific configs.

Method in Kustomize for modifying existing Kubernetes resources without directly editing the original YAML files.

Cloud security platform providing compliance monitoring and threat detection for containerized environments.

Unit of a container image representing a set of files or changes, crucial for image building and optimization.

Storage mechanism used by container runtimes, allowing efficient storage and quick updates of container images.

Design pattern ensuring only one instance of a distributed application component is active at a time.

Process where multiple instances of an operator elect a leader to prevent conflicts in resource management.

Security concept of giving a user or process only the minimum levels of access necessary to perform its function.

User-friendly, open-source IDE for managing Kubernetes clusters, providing a unified interface for multiple clusters.

Observability platform providing insights into the behavior of highly distributed software systems and microservices.

Kubernetes policy for constraining resource allocations (CPU and memory) for pods or containers in a namespace.

Kubernetes object defining the min, max, and default compute resources for pods and containers in a namespace.

Lightweight service mesh for Kubernetes, providing observability, reliability, and security for microservices.

Component of Linkerd responsible for proxying traffic between services, implementing service mesh functionality.

Linkerd resource providing service-specific traffic metrics and control, enhancing observability and management.

High-performance, minimal-footprint proxy used in Linkerd's data plane for efficient service mesh operations.

Framework allowing various security models to be plugged into the Linux kernel, enhancing container isolation.

Process of connecting external load balancers with Kubernetes services for distributing incoming traffic.

Methods used by load balancers to distribute traffic across multiple container instances or pods.

Kubernetes service type that provisions an external load balancer in cloud environments for routing external traffic to services.

Kubernetes volume type for locally attached storage, providing high-performance storage options for stateful workloads.

Technique for routing traffic to geographically or topologically closer endpoints in distributed container deployments.

Techniques for collecting, processing, and storing logs from distributed containerized applications for centralized analysis.

Design of systems for capturing, storing, and analyzing logs from containers and orchestration platforms.

Horizontally-scalable, multi-tenant log aggregation system designed for use with Prometheus and Kubernetes.

Cloud-native distributed block storage system for Kubernetes, providing persistent storage for stateful workloads.

Implementation of Longhorn for providing resilient, replicated block storage in Kubernetes clusters.

Comparison of container runtimes operating at different abstraction levels, balancing performance and features.

Technique for precomputing and storing query results, improving performance in microservices architectures.