Engineering Glossary

A visual representation of a hash value, often used as a default avatar in Git platforms.

A service that creates, maintains, and manages identity information for users, often integrated with Git platforms.

A record in Git's index file representing the state of a file in the repository.

A connection between Git and other tools or services to enhance functionality and workflow.

A short sequence of bytes used to identify a longer public key in Git's GPG signing process.

A secure storage system for passwords and keys, often integrated with Git clients for credential management.

A reserved word in Git commands or configurations with a specific meaning or function.

A way to categorize and organize issues and pull requests in GitHub repositories.

A document in a Git repository specifying how the project can be used, modified, and distributed.

A feature in Git platforms allowing users to comment on specific lines of code in pull requests or commits.

Characters marking the end of a line, which can cause issues in Git when collaborating across different operating systems.

A GitHub account that has been restricted due to a violation of terms of service or suspicious activity.

The default name for the primary branch in many Git repositories, replacing 'master' in recent conventions.

An administrative interface for managing Git hosting services, often used in enterprise environments.

A system of annotation used to format text, often used in Git documentation and comments.

A visual representation of an organization's membership on Git hosting platforms.

A way to notify a user on Git platforms by using the @ symbol followed by their username.

A situation in Git where incompatible changes have been made to the same part of a file, requiring manual resolution.

A way to track progress on groups of issues or pull requests in GitHub projects.

A complete copy of a repository, including all branches and history, often used for backup or as a full replica.

A team within a GitHub organization that is a subset of a larger team, inheriting its permissions.

A visual representation of the commit history and branch structure of a Git repository.

A personalized list of recent activity on Git platforms, showing updates from followed users and repositories.

A Git push that introduces new commits to the remote branch that aren't direct descendants of the current remote HEAD.

An alert system on Git platforms to inform users about relevant activities and mentions.

A fundamental unit in Git's data model, representing content, commits, trees, or tags, identified by a unique SHA-1 hash.

The storage system in Git that contains all versions of project files and metadata, organized by SHA-1 hashes.

A unique SHA-1 hash that identifies a Git object, ensuring data integrity and enabling content-addressable storage.

Another term for the SHA-1 hash that uniquely identifies a Git object in the repository.

The classification of Git objects, including blob (file content), tree (directory structure), commit, and tag.

A Git merge involving more than two branches simultaneously, creating a commit with multiple parents.

A shared account on Git hosting platforms where businesses and open-source projects can collaborate across many projects.

A user with administrative privileges for an organization account on Git hosting platforms.

A branch in Git with no parent commit, typically used to start a new history unrelated to existing branches.

A user granted access to a repository but not a member of the organization that owns the repository.

A Git feature that allows files from one branch to temporarily override files in another branch without merging.

The user or organization with administrative rights to a Git repository, controlling access and settings.

A compressed file format used by Git to store multiple objects efficiently, reducing repository size.

A file that provides fast random access to objects stored within a packfile in Git.

A commit from which another commit is derived, establishing the hierarchical relationship in Git history.

A team in a Git platform that grants its permissions to nested sub-teams within an organization.

Alerts received when you're directly involved in repository activities like being assigned or mentioned.

A pattern used in Git commands to limit the scope of operations to specific files or directories.

The process of recursively dereferencing a Git object to reveal its underlying object type.

A reference specific to a particular working tree in a Git repository with multiple worktrees.

A URL that points to a specific version of a file or directory in a Git repository, ensuring consistent reference.

An individual account on Git platforms, used for personal projects and contributions.

A Git feature for searching the repository history to find when a particular change was introduced.

A featured repository prominently displayed on a user's or organization's profile page on their Git hosting platform of choice.

Low-level Git commands that expose internal workings, primarily used for scripting and advanced operations.

High-level Git commands designed for everyday use, providing a user-friendly interface to common version control tasks.

Server-side scripts that run before pushed commits are accepted, used to enforce policies or perform checks.

The main email associated with a Git account, used for communications and commit author identification.

Contributions made to private repositories, typically only visible to repository collaborators.

A Git repository with restricted access, visible only to the owner and explicitly granted collaborators.

A Git branch that represents the stable, deployed version of a project, often protected from direct changes.

A user's public page on a Git platform, displaying information about their activity and repositories.

An image representing a user on a Git platform, displayed alongside their contributions and activities.

A kanban-style board in Git platforms for organizing and prioritizing project issues and pull requests.

A branch with rules enforcing certain conditions before changes can be pushed, merged, or deleted.

A Git reference that doesn't follow the standard refs format but is treated similarly, like HEAD or MERGE_HEAD.

Contributions made to public repositories, visible on a user's profile and activity feed.

A Git repository that is openly accessible to anyone, allowing viewing and often cloning without authentication.

Permission level in Git allowing a user to clone a repository and fetch updates, but not push changes.

The process of examining proposed changes in a pull request before they are merged into the main codebase.

A visual representation of a GitHub repository's recent activity, including commits, issues, and pull requests.

A visualization of commit frequency over time, often displayed as a heatmap on Git platforms.

The act of uploading commits from a local branch to a corresponding branch in a remote Git repository.

Permission to upload local changes to a remote Git repository, allowing direct modification of shared branches.

Data structures used to optimize Git operations by quickly determining which objects are reachable from a given commit.

Git objects that can be accessed by following the commit history from a given reference point.

Permission to view and clone a Git repository without the ability to make changes or push commits.

A backup method for accessing a Git account if two-factor authentication is enabled but unavailable.

A name that points to a specific Git object, typically a commit, such as branches or tags.

The collection of references in a Git repository, including branches, tags, and other named pointers to commits.

The address of a remote Git repository, used for fetching and pushing changes between local and remote repositories.

A version of a Git project hosted on a server, serving as a central point for collaboration and backup.

A local reference that represents the state of a corresponding branch in a remote repository.

A copy of a Git repository, often used in distributed systems for redundancy and load balancing.

A local storage of repository data used to improve performance in Git operations.

A visual representation of the commit history and branch structure in a Git repository.

A person responsible for managing a Git repository, reviewing contributions, and maintaining project quality.

A protection rule requiring one or more reviewers to approve changes before merging in Git platforms.

A condition that must be met, such as passing tests, before changes can be merged into a protected branch.

The process of addressing and fixing merge conflicts in Git, typically done manually by the developer.

A Git operation that creates a new commit that undoes the changes made by a previous commit.

The process of examining proposed changes in a pull request before merging them into the main codebase.

A specific version of a file, directory, or entire repository in Git, often referenced by a commit hash.

The act of moving a branch pointer to an earlier commit in Git, effectively undoing more recent commits.

The top-level directory of a Git repository, containing all project files and the .git subdirectory.

The base filesystem of a Git repository, from which all other directories and files are organized.

A pre-written response that can be quickly inserted into issues or pull request comments on Git platforms.

The range of access or permissions granted to a token or application in Git-based systems.

A licensed user in enterprise Git hosting plans, typically representing a single user account.

A team in a Git organization that is hidden from non-members and other teams within the organization.

A record of security-related events and actions in a system, used for monitoring and auditing purposes.

An authenticated request between Git servers, often used in integrations or automated processes.

A mechanism for Git platforms to send event notifications to external services or applications.

A Git repository with an intentionally truncated history, often created through shallow cloning.

An authentication process allowing users to access multiple Git-related services with one set of credentials.

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.

Kubernetes component that automatically adjusts the CPU and memory reservations for pods to optimize resource utilization.

Resource utilization data used by VPA to make decisions on adjusting pod resource allocations.

Extended VPA functionality allowing scaling decisions based on application-specific metrics.

Kubernetes resource defining the behavior for automatically adjusting pod resource requests and limits.

Isolated environments within a physical Kubernetes cluster, providing multi-tenancy and resource isolation.

Single IP address representing a service, used for load balancing in container networking.

Kubernetes kubelet implementation that masquerades as a node and schedules pods on non-Kubernetes platforms.

Kubernetes volume properties (ReadWriteOnce, ReadOnlyMany, ReadWriteMany) defining how a volume can be mounted.

Process of creating a new volume populated with the contents of an existing volume in Kubernetes.

Feature allowing the size of a persistent volume to be increased without recreating the volume.

Kubernetes feature for detecting and reporting issues with persistent volumes to ensure data integrity.

Kubernetes volume properties (Block, Filesystem) defining how the storage is presented to the container.

Process of making a storage volume accessible within a container's filesystem, enabling data persistence.

Specifications in Kubernetes pod definitions declaring how volumes should be mounted into containers.

Kubernetes components enabling integration with various storage systems for persistent storage.

Point-in-time copies of volumes in Kubernetes, used for backup, restoration, or cloning of persistent data.

Incorporation of security scanning tools into the container build and deployment pipeline.

Set of tools for implementing GitOps workflows in Kubernetes environments, automating deployment and management.

Software-defined networking solution for containerized applications, providing a virtual network across hosts.

Use of Weave Net to create a virtual network connecting Docker containers across multiple hosts.

Use of WebAssembly runtimes in containers for portable, secure, and high-performance applications.

Integration of WebAssembly modules in service mesh proxies for customizable traffic management.

External services invoked during the Kubernetes admission process to validate or mutate API requests.

Kubernetes authorization mode delegating access decisions to an external REST service, enabling custom auth logic.

Kubernetes authentication method verifying bearer tokens against an external webhook service.

CNI IPAM plugin for dynamically allocating IP addresses across multiple nodes in Kubernetes.

Use of the Wireguard VPN protocol to secure container-to-container communication across hosts.

Digital certificates used for authenticating clients in Kubernetes API server communications.

Linux kernel technology for high-performance packet processing, useful in container networking scenarios.

Container advisor that collects, aggregates, and exports resource usage and performance data from running containers.

Second version of control groups, offering a unified hierarchy and improved resource management for containers.

Industry-standard container runtime used by many container and orchestration platforms, providing core container operations.

Core components and architecture of the containerd container runtime, including its image management and execution features.

Intermediary process between containerd and runc, managing container lifecycle and I/O, enhancing isolation.

Use of extended Berkeley Packet Filter for advanced container networking capabilities, enhancing performance and security.

Use of extended Berkeley Packet Filter for deep insights into container behavior and performance.

Integration of eBPF technology in service mesh implementations for improved performance and observability.

Use of eBPF for efficient and detailed monitoring of containerized applications and infrastructure.

Feature ensuring at-rest encryption of data stored in etcd, Kubernetes' distributed key-value store.

Optimization techniques for improving etcd performance in large-scale container orchestration.

Point-in-time copies of etcd's state, used for backup and recovery in Kubernetes clusters.

Process of updating the etcd cluster to a newer version in a Kubernetes environment, ensuring cluster data store reliability.

High-performance, open-source RPC framework used for efficient communication between containerized microservices.

Use of gRPC, a high-performance RPC framework, for communication between containerized microservices.

Container runtime sandbox that provides an additional layer of isolation between containerized applications and the host kernel.

Use of gVisor to enhance security in multi-tenant container environments by isolating containers from the host kernel.

Framework for securing software supply chains, applicable to container image build and distribution processes.

Open-source tool for discovering security weaknesses in Kubernetes clusters through simulated attacks.

Kubernetes network proxy maintaining network rules on nodes, implementing part of the Kubernetes Service concept.

Different implementations of kube-proxy for managing service networking, each with unique performance characteristics.

Kubernetes component responsible for assigning newly created pods to nodes based on resource requirements and constraints.

Primary node agent running on each node in a Kubernetes cluster, ensuring containers are running in a pod as expected.

Linux Containers, a userspace interface for the Linux kernel containment features, providing operating system-level virtualization.

Container runtime focusing on security and composability, offering an alternative to Docker (now deprecated).