Engineering Glossary

A method of submitting contributions to a Git project, proposing changes from a forked repository or branch.

Customizable files that automatically populate the body of new pull requests, guiding contributors.

Alternative terms for the same concept of proposing and reviewing changes in Git-based collaboration.

A Git command used to upload local repository changes to a remote repository, sharing updates with collaborators.

A file in a Git repository that provides information about the project, its purpose, and how to use or contribute to it.

A Git operation that moves or combines a sequence of commits to a new base commit, often used to maintain a linear history.

A Git rebase mode allowing users to modify, reorder, or squash commits before applying them to the target branch.

A Git mechanism that records updates to branch tips and other references in a repository.

A string that specifies the mapping between remote and local references when fetching or pushing in Git.

A strategy where a separate branch is created for release preparation, allowing continued development on the main branch.

A Git repository hosted on a server, allowing collaboration and synchronization between multiple developers.

Centralized location where software packages or modules are stored and managed, facilitating version control and collaboration.

A data structure containing all the files, history, and metadata for a Git-managed project.

Source Control Management, a system for tracking and managing changes to code, of which Git is an example.

The cryptographic hash function used by Git to generate unique identifiers for objects in the repository.

A secure authentication method using a pair of cryptographic keys for accessing remote systems or services.

A Git clone that only fetches a limited number of recent commits, reducing download size for large repositories.

The process of combining multiple commits into a single commit in Git, often used to simplify history.

The process of preparing changes to be committed in Git, also known as "adding to the index."

A Git feature for temporarily storing uncommitted changes, allowing you to switch branches without committing work in progress.

A Git feature for temporarily saving uncommitted changes, allowing you to switch contexts without committing incomplete work.

A Git command that shows the state of the working directory and staging area, indicating which files are modified or staged.

A Git repository nested within another repository, allowing you to include external projects in your own project.

A named reference to a specific point in Git history, typically used to mark release points or important milestones.

A subscription level for GitHub designed for collaborative groups or small organizations.

The original repository from which a fork is created, or the branch that a local branch is tracking.

Terms describing the flow of changes in Git, with upstream being the source and downstream the destination.

A temporary commit indicating unfinished work, often used to save progress or facilitate code reviews.

A secure alternative to passwords, used for authentication in Git operations, providing limited access to specific resources or actions.

A secondary location where Git stores object files, useful for optimizing storage and performance in large repositories.

A dedicated hardware or virtual machine optimized for running Git services, often used in enterprise environments for improved performance.

The person responsible for addressing an issue or pull request in a Git repository, helping to organize and prioritize tasks.

A temporary code used in two-factor authentication to verify a user's identity when accessing Git repositories or services.

The primary branch in a Git repository from which feature branches are created and into which they are merged after development.

A simple authentication method in Git using a username and password, often discouraged in favor of more secure methods.

The recurring period for which Git hosting services charge users or organizations for their subscriptions and usage.

The email address associated with a Git account or organization for receiving invoices and billing-related communications.

A user role in Git hosting platforms responsible for managing billing information, subscriptions, and payments for an organization.

A subscription package offered by Git hosting services, defining features, storage limits, and pricing for users or organizations.

A brief description of a user's profile on Git platforms, typically including their role, interests, or expertise in software development.

A Git object type representing the contents of a file, stored as a sequence of bytes without any metadata or file attributes.

An action on Git platforms to restrict a user's ability to interact with a repository, often used to manage disruptive behavior.

A set of rules applied to Git branches to control who can push changes, merge, or perform certain actions, enhancing code quality.

A command-line tool often used with Git to transfer data and interact with APIs, supporting various protocols including HTTPS.

A visual representation of an issue or pull request in Git project management tools, used for organizing and prioritizing work.

A series of connected commits in Git, representing a sequence of changes that build upon each other in a repository's history.

A group of changes made to files in a Git repository, typically represented by a commit and including additions, modifications, and deletions.

An automated test or verification process run on Git repositories to ensure code quality, security, or compliance with project standards.

A nested team within a Git organization's hierarchy, inheriting permissions and access rights from its parent team.

A Git command used to remove untracked files and directories from a working directory, helping to maintain a tidy repository.

A technique used in Git hosting services to distribute repository data across multiple servers for improved performance and reliability.

A visual representation of code additions and deletions over time in a Git repository, useful for tracking project activity.

A document outlining expected behavior and guidelines for contributors in a Git repository, promoting a positive community.

A designated person or team responsible for reviewing and approving changes to specific files or directories in a Git repository.

A user with read and write access to a Git repository, able to contribute code and participate in project discussions.

A unique identifier (SHA-1 hash) assigned to each commit in Git, used to reference specific points in a repository's history.

The person who originally created a commit in Git, distinct from the committer who may have applied the changes on behalf of the author.

A visual representation of the commit history in a Git repository, showing the relationships between different branches and merges.

The underlying structure and visual representations of Git's commit history, used for understanding project evolution.

A descriptive text associated with a Git commit, explaining the purpose and details of the changes made in that commit.

A Git object type containing metadata about a commit, including the author, committer, date, and a pointer to the tree object.

A file used by Git to store commit graph information, optimizing performance for operations that traverse the commit history.

A term referring to any Git object that can be resolved to a commit, including commit IDs, branch names, and tags.

A Git feature allowing users to view differences between two branches, often used before merging to review changes.

A visual representation of a user's Git activity over time, showing the frequency and distribution of their contributions to repositories.

A document in a Git repository outlining best practices, coding standards, and procedures for contributors to follow when submitting changes.

The collective changes, commits, issues, and pull requests made by a user to Git repositories, showcasing their involvement in projects.

Any person who has made contributions to a Git repository, whether through code, documentation, or other forms of project support.

A visual representation of all contributors to a Git repository, showing their relative contributions over time and by type.

The fundamental set of Git commands and functionality, excluding third-party extensions or hosting platform-specific features.

A promotional code offering discounts or credits for Git hosting services, often used to attract new users or upgrade existing accounts.

A time-based job scheduler used in Git hosting platforms to automate tasks such as backups, notifications, or periodic code checks.

A Git object not reachable from any reference (branch, tag, etc.), often created during rebasing or other history-altering operations.

A central interface in Git platforms providing an overview of a user's repositories, activities, and notifications.

The primary branch in a Git repository, typically named "main" or "master," serving as the base for new branches and deployments.

A visual representation of a project's dependencies, showing relationships between different components or external libraries.

A visualization of other projects or repositories that depend on a given Git repository, useful for understanding its impact.

An SSH key granting read-only access to a specific Git repository, commonly used for automated deployments or CI/CD processes.

The process of resolving a Git reference (like a branch or tag) to its corresponding commit object or SHA-1 hash.

A state in Git where the HEAD pointer references a specific commit rather than a branch, often used for temporary work.

Tools and features in Git used to identify and troubleshoot issues with repositories, commits, or configuration settings.

A folder within a Git repository containing files and potentially other subdirectories, organized hierarchically.

A term describing a Git working directory that contains uncommitted changes, indicating modifications that haven't been saved to the repository.

Alerts sent via email by Git platforms to inform users about repository activities, mentions, or updates.

A high-tier account type offered by Git hosting services, providing advanced features and support for large organizations.

A problematic merge in Git where conflicts are resolved incorrectly, potentially introducing subtle bugs or inconsistencies.

A type of Git merge where the target branch's pointer is simply moved to the latest commit of the source branch, without creating a new commit.

A temporary branch in Git created to develop a specific feature or fix, typically merged back into the main branch upon completion.

A markdown syntax used in Git documentation and comments to display formatted code snippets with syntax highlighting.

The underlying structure used by Git to store repository data, including objects, refs, and configuration files.

A feature in Git platforms allowing users to receive updates and notifications about other users' activities.

A file containing a Git repository's object database, used in certain Git operations.

A text file specifying intentionally untracked files that Git should ignore, customizable for different project types.

A technique in Git to join two unrelated commit histories, typically used for repository repair or history modification.

A unique identifier (SHA-1) for Git objects, used to ensure data integrity and create content-addressable storage.

The default branch that is checked out when a repository is cloned, typically 'main' or 'master'.

A reference to the tip of a branch, representing the most recent commit in that branch's history.

A characteristic of Git hosting systems designed to ensure continuous operation and minimal downtime.

A script that Git executes before or after events such as commit, push, and receive, used for automating or controlling Git processes.

The unique name assigned to a device on a network, used for identification and communication purposes.

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.

Object storage gateway for Ceph, providing S3-compatible API for container storage needs in Kubernetes.

Linux kernel feature for limiting, prioritizing, and isolating resource usage of container groups.

Technique for tracking and capturing changes in databases, useful in microservices architectures.

Practice of intentionally injecting failures in CI/CD pipelines to improve system resilience.

Platform for simulating various abnormal scenarios in Kubernetes environments to test reliability.

Static code analysis tool for detecting misconfigurations in Infrastructure as Code deployments.

Open-source networking, observability, and security solution for container workloads based on eBPF.

eBPF-powered service mesh offering high-performance traffic management and security for microservices.

Observability platform for Kubernetes providing deep visibility into container network flows.

High-performance networking mode in Cilium using extended Berkeley Packet Filter for packet processing and security.

Continuous integration and delivery platform supporting containerized application testing and deployment.

Fault tolerance pattern preventing cascading failures in distributed containerized systems.

Technique to detect failures and prevent system overload in microservices architectures, enhancing resilience.

Design pattern for handling large payloads in microservices by storing data externally, improving efficiency.

Open-source tool for static analysis of vulnerabilities in container images, enhancing security.

Kubernetes component managing interactions between cluster and cloud provider APIs, enabling cloud integration.

Specification for packaging and managing distributed applications across different environments.

Tooling for building container images from source code without Dockerfiles, adhering to best practices.

Architectural patterns specifically tailored for distributed, containerized applications in cloud environments.

Specification for describing event data in common formats, facilitating event-driven architectures in cloud environments.

Isolation technique in containerized microservices to prevent failure propagation across the system.

Group of servers or other resources that work together as a single system to provide high availability.

Kubernetes project for declarative management and provisioning of clusters using Kubernetes-style APIs.

Implementation of Cluster API for specific infrastructure providers, enabling cluster lifecycle management.

Kubernetes component that automatically adjusts the size of a cluster based on resource demands.

Process of estimating and allocating resources needed for container workloads in a cluster.

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

Interconnected network of container clusters, enabling cross-cluster communication and resource sharing.

Network configuration and management within and between container clusters, enabling inter-container communication.

Procedure for updating the Kubernetes version and components of a cluster while minimizing disruptions.

Centralized logging solution capturing logs from all containers and nodes in a cluster, aiding in troubleshooting.

Kubernetes component that automatically adjusts the number of nodes in a cluster based on resource utilization.

Kubernetes service type that exposes the service on an internal IP within the cluster, facilitating internal communication.

Kubernetes services accessible only within the cluster, used for internal communication between pods.

Kubernetes RBAC resource defining permissions across an entire cluster, not limited to a single namespace.

Kubernetes RBAC resource that binds a ClusterRole to users, groups, or service accounts across all namespaces.

Custom resource in Operator Lifecycle Manager describing a specific version of an Operator.

GitOps platform for managing and automating containerized application deployments, streamlining CD workflows.

Techniques to reduce initialization time of containers, particularly important in serverless environments.

Design pattern for handling failures in distributed container-based systems by reversing or compensating actions.

Kubernetes API object used to store non-confidential data in key-value pairs, accessible by pods.

Service mesh feature of Consul providing secure service-to-service communication with automatic TLS encryption.

Standardized unit of software that packages code and all its dependencies for quick, reliable deployment.

Methods used by attackers to escape container isolation and access the host system, posing security risks.

Software responsible for managing container lifecycle, including creation, execution, and destruction.

Security vulnerability allowing a process to break out of container isolation and access the host system.

Security measures and best practices to prevent containers from breaking out of their isolation.

Weaknesses in container runtimes or configurations that could allow processes to escape isolation.

Mechanisms to verify the health and readiness of containerized applications, ensuring service availability.

Physical or virtual machine running the container runtime and hosting containers, providing the execution environment.

Process of moving container images through different environments (e.g., dev, test, prod) in a controlled manner.

Cryptographic process of digitally signing container images to ensure their integrity and authenticity during distribution.

Stages a container goes through from creation to termination, including running, paused, and stopped states.

Processes and tools for managing containers throughout their lifecycle, from creation to deletion.

Process of capturing and managing log output from containerized applications for monitoring and troubleshooting.

Configuration file specifying how to build a container image, including base image, commands, and environment setup.

Quantitative measures of container performance, resource usage, and health, used for monitoring and optimization.

Specification and libraries for configuring network interfaces in Linux containers, standardizing networking.

Standard for developing plugins to configure network interfaces in Linux containers, ensuring interoperability.

Architecture and principles governing how containers communicate within and across hosts in orchestrated environments.

Automated arrangement, coordination, and management of software containers, crucial in large-scale deployments.

Kubernetes mechanisms (liveness, readiness, startup) for checking container health and controlling lifecycle.

Technique for capturing and analyzing real user interactions with containerized applications.

Rules defining how containers should be restarted in case of failures or exits, enhancing application resilience.

Software responsible for running containers, managing their lifecycle from creation to deletion.

Measures and tools to protect containers during execution, preventing unauthorized access or malicious activities.

Lightweight processes that manage the lifecycle of containers and provide abstraction between runtimes.

Standardized interface for container orchestration platforms to interact with storage systems.

Standard for developing storage plugins for container orchestration platforms, enabling storage extensibility.

Process of identifying security vulnerabilities in container images and their dependencies.

Mechanism for verifying the integrity and authenticity of container images before deployment.

Automated process of releasing container images to production environments after passing tests.

Ongoing process of validating deployed containers against defined policies and expectations.

Container networking solution using VPP (Vector Packet Processing) for optimized performance.

Approach prioritizing API definition before implementation, crucial for microservices architecture.

Linux kernel feature for limiting, prioritizing, and isolating resource usage of container groups.

Components managing the overall state of a container cluster, including API server, scheduler, and controllers.

Process of updating the core components of a container orchestration system while maintaining cluster stability.

Kubernetes component running controller processes to regulate the state of the cluster, maintaining desired state.

Software framework for building Kubernetes controllers and operators, simplifying custom resource management.

Kubernetes feature allowing custom resource API version conversions via external webhooks.

Storage optimization technique used in container filesystems to reduce duplication and improve efficiency.

Process of marking a node as unschedulable in Kubernetes, preventing new pods from being assigned to it.

Flexible, extensible DNS server used for service discovery in Kubernetes clusters, replacing kube-dns.

Horizontally scalable, multi-tenant, long-term storage for Prometheus metrics, enhancing monitoring capabilities.

Use of Cortex to provide isolated Prometheus monitoring for multiple users or teams in shared environments.

Kubernetes object for creating time-based jobs, running containers on a schedule for automated tasks.

Kubernetes patterns for running batch processes and scheduled tasks in containerized environments.

Kubernetes operators capable of managing resources across multiple namespaces, enabling broader automation.

Kubernetes API extension allowing pods to consume custom metrics for autoscaling and monitoring.

Extension of the Kubernetes API that defines new, custom resource types specific to a cluster's needs.

Process of converting custom resources between different API versions in Kubernetes, enabling API evolution.

Kubernetes API extension allowing the definition of custom resources, enabling cluster customization.

Kubernetes feature for extending the API with custom resources, enabling cluster customization.

User-defined API objects in Kubernetes, extending the core functionality of the platform for specific use cases.

Alternative or additional scheduler implementation for Kubernetes, allowing custom pod placement logic.

Alternative scheduling implementations in Kubernetes for custom pod placement strategies, enabling specialized scheduling logic.

Kubernetes feature providing DNS-based service discovery for containers within the cluster.

Use of Data Plane Development Kit for high-performance packet processing in container networks.

Kubernetes object ensuring that all (or some) nodes run a copy of a specific pod, useful for cluster-wide services.