Engineering Glossary

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 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.

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.

Machine learning models deployed and executed on edge devices, often with cloud-based training or updates.

Techniques for reducing the size of AI models to run efficiently on resource-constrained edge devices.

Performing text analysis and language understanding tasks on edge devices with cloud support.

Managing and coordinating distributed edge computing resources and their interaction with the cloud.

Using AI on edge devices to forecast equipment failures, with cloud-based model updates and analysis.

Training AI agents on edge devices through interaction with the environment, supported by cloud resources.

Measures to protect data, devices, and applications at the network edge in cloud-connected systems.

Distributed network layer for managing service-to-service communication in edge computing environments.

Data storage systems designed for edge devices with intermittent connectivity to cloud backends.

Applying knowledge from pre-trained cloud models to new tasks on edge devices with limited data.

Creating applications specifically designed to run on edge devices with cloud integration capabilities.

Software development paradigms optimized for creating applications that run on edge devices.

Seamless integration of edge computing resources with centralized cloud infrastructure and services.

Cloud networking component allowing outbound internet communication while blocking inbound traffic.

Fully managed service for deploying and scaling web applications and services on AWS infrastructure.

Static public IP address that can be dynamically assigned to cloud resources, providing consistent access points for applications.

Virtual network interface that can be attached to instances in a VPC, providing flexible networking capabilities.

Virtual network cards that can be attached, detached, and moved between cloud instances.

Integration of analytical capabilities directly into cloud-based applications and workflows.

Cloud-based platforms for analyzing and responding to human emotions in various applications.

Cloud services using AI to detect and respond to user emotions for enhanced interactions.

Protecting data by encoding it both when stored and while moving between cloud systems.

Techniques for securing data both when stored in cloud systems and while being transmitted.

Cloud resource scaling that considers energy consumption alongside performance metrics.

Allocating cloud resources to tasks with consideration for energy consumption and efficiency.

Computing methods designed to minimize energy consumption in cloud environments.

Allocating cloud resources to optimize energy efficiency while meeting performance requirements.

Documenting cloud providers' sustainability and ethical practices for stakeholder transparency.

Open-source edge and service proxy designed for cloud-native applications, providing features like load balancing and observability.

Adherence to principles ensuring fair and responsible use of AI in cloud environments.

Fully managed event routing service for building event-driven applications in Azure.

Storing the state of a system as a sequence of events in cloud-based applications.

Real-time analysis of data streams in cloud environments for immediate insights.

System design where actions are triggered by events in cloud environments, enabling responsive and scalable applications.

Connecting cloud services and applications based on event occurrences, enabling real-time data flow and processing.

APIs that trigger or respond to events in cloud-native applications, enabling real-time data processing and integration.

Developing cloud applications that respond to and generate events, enabling reactive and scalable architectures.

Serverless event bus for building event-driven applications in AWS, facilitating communication between various services.

Data consistency model where all replicas eventually reach a consistent state in distributed cloud systems.

Cloud systems capable of at least one exaFLOPS, or a billion billion calculations per second.

The runtime context in which cloud functions or applications operate, including libraries, dependencies, and configurations.

Monitoring and recording parameters and results of machine learning experiments in cloud environments.

Cloud services for making AI decision-making processes more transparent and interpretable.

Tools for developing AI models with transparent decision-making processes in cloud environments.

Cloud services offering tools to make AI models' decision-making processes more understandable.

Service for creating private connections between Azure datacenters and on-premises infrastructure.

Data integration process where transformation occurs after loading data into the cloud target system.

Process of extracting data from sources, transforming it to fit operational needs, and loading into a target database in the cloud.

Extending cloud capabilities to devices at the extreme edge of the network, like IoT sensors.

Tools for dynamically enabling or disabling features in cloud applications without code deployments.

Centralized repository for storing, managing, and serving machine learning features in cloud environments.

Analyzing data across multiple cloud or edge locations without centralizing the raw data.

System allowing integrated data access across multiple autonomous database management systems in the cloud.

Machine learning technique that trains algorithms across decentralized devices or servers holding local data samples.

Coordinating federated learning processes across multiple edge devices or cloud nodes.

Cloud-based systems for implementing and managing federated learning across distributed datasets.

Applying federated learning techniques to train models on edge devices connected to cloud systems.

Tools for executing queries across multiple distributed databases or data sources in cloud environments.

Cloud service for storing and accessing file data through shared file systems, providing scalable and manageable data storage.

Cloud services providing scalable file storage accessible from multiple compute instances.

Tools and processes for automating financial operations and optimization in cloud environments.

Framework for managing and optimizing cloud costs across an organization, aligning financial and operational goals.

AI-driven tools for continuously optimizing cloud spend and resource allocation.

Security system that monitors and controls incoming and outgoing network traffic in cloud environments.

Open-source, Kubernetes-native serverless framework for building microservices in the cloud, simplifying application deployment.

Extending cloud computing to the edge of the network, bringing processing closer to data sources.

Using historical data and algorithms to predict future trends or values in cloud-based analytics.

Cloud-based encryption allowing computations on encrypted data without decryption.

Tools for building complex serverless applications by combining multiple cloud functions.

Cloud computing model where developers run and manage application functionalities without complex infrastructure.

Adhering to EU data protection regulations in cloud-based data processing and storage.

Cloud compute instances equipped with Graphics Processing Units for parallel processing tasks.

Cloud-based services for creating AI models that can generate new content or data.

Cloud-based systems for analyzing large volumes of location-based data efficiently.

User interfaces that interpret hand or body movements to interact with cloud services.

Methodology using Git repositories as the source of truth for declarative infrastructure and applications in the cloud.

Service that improves availability and performance of applications by optimizing traffic routing across cloud regions.

Database systems that replicate and distribute data across multiple geographic locations in the cloud.

Rules and constraints applied to resources within a Google Cloud organization, enforcing governance and compliance.

Cloud services for analyzing and visualizing complex relationships in large datasets using graph structures.

NoSQL databases designed to store and query data with complex relationships in cloud environments.

Practices and technologies aimed at reducing the environmental impact of cloud data centers and operations.

Practices for developing cloud software that minimizes energy consumption and environmental impact.

Techniques for developing cloud applications that optimize resource usage and reduce environmental impact.

Adhering to US healthcare data protection regulations in cloud-based systems and applications.

Transmitting touch and motion sensations through cloud-connected devices for immersive experiences.

Automated tests to verify the availability and proper functioning of cloud resources and services.

Utilizing excess heat generated by cloud data centers for other purposes, improving energy efficiency.

Packages of pre-configured Kubernetes resources for deploying applications in cloud environments.

Systems managing data across diverse database types and locations in cloud environments.

Using cloud resources for solving complex computational problems requiring significant processing power.

Advanced 3D visualization interfaces for interacting with cloud services and data.

Conceptual cloud data centers using holographic technology for data storage and processing.

Experimental technology using holograms to store data in cloud environments, potentially offering high density.

Experimental data storage technique using holograms in cloud systems for high-density, long-term preservation.

Cloud-based offerings that enable computations on encrypted data without decryption.

Cloud-based service offering encryption that allows computations on encrypted data.

Encryption technique allowing computations on encrypted data without decryption in cloud environments.

Adding more machines to a cloud system to distribute load and increase capacity.

Cloud-based systems facilitating communication between humans and robots or AI agents.

Design pattern using DaemonSets to run system daemons or agents on every node in a cluster.

Processes and tools for preserving and restoring data in containerized environments, ensuring data durability.

Techniques ensuring data integrity across distributed container-based applications, maintaining reliability.

Distributed data management architecture providing consistent data access across containerized applications.

Unified data architecture combining data lake and data warehouse features in containerized environments.

Process of moving data between containers or from traditional systems to containerized applications.

Techniques for maintaining data across container lifecycles, including volume mounts and persistent volumes.

Network layer responsible for forwarding container traffic based on control plane decisions.

Techniques for creating and maintaining copies of data across multiple containerized instances.

Partitioning data across multiple database containers for improved scalability and performance.

Process of running database systems within containers for improved portability and resource utilization.

Microservices design pattern where each service has its own dedicated database, ensuring data isolation.

Platform for observability and monitoring of containerized environments and microservices.

Design pattern for handling failed message processing in containerized message-driven systems.

Approach to deploying containers by specifying desired state rather than imperative commands.

Techniques for rolling out container updates, including rolling updates, blue-green, and canary deployments.

Kubernetes component that evicts pods from nodes based on specific policies to optimize cluster resource usage.

Standardized format defining the structure and metadata of Docker container images, ensuring portability.

Kubernetes feature allowing nodes to advertise system hardware resources to the cluster, enabling specialized hardware use.

Network optimization technique in container load balancing for improved performance by bypassing the load balancer for responses.

Implementation of tracing in containerized microservices for end-to-end request visibility.

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

Implementation of OpenTelemetry for standardized observability in containerized environments.

Minimal container images containing only the application and its runtime dependencies, reducing attack surface.

Command to attach local standard input, output, and error streams to a running container for interaction and debugging.

Set of files located in the specified PATH or URL, used during the docker build process to create images.

Docker CLI plugin for extended build capabilities, including multi-platform builds and enhanced build options.

Component enabling Docker to be used as a container runtime in Kubernetes via the Container Runtime Interface.

Command-line tool for interacting with Docker daemon and managing containers, images, and other Docker objects.

Command creating a new image from a container's changes, useful for saving modifications made to a running container.

Tool for defining and running multi-container Docker applications, simplifying the process of managing complex application stacks.

Process of viewing detailed information about a container's configuration and state, aiding in troubleshooting.

Feature providing digital signing and verification of Docker images, ensuring image authenticity and integrity.

Process of validating the authenticity and integrity of signed Docker images before use.

Feature allowing management of multiple Docker endpoints from a single Docker client, simplifying multi-environment management.

Background service managing Docker objects like images, containers, networks, and volumes.

Command allowing users to run a new process inside a running container, useful for debugging and maintenance tasks.

Commands for exporting a container's filesystem as a tarball and importing it as an image.

Cloud-based repository service for finding and sharing container images, central to the Docker ecosystem.

Command showing the history of an image's layers, providing insight into how the image was built and modified.

Process of examining Docker image metadata, including layers, environment variables, and exposed ports.

Standardized format defining the structure and metadata of Docker container images, ensuring portability.

Virtualized network providing connectivity between Docker containers, isolating container traffic from the host network.

Different networking modes in Docker, including bridge, host, overlay, and macvlan, offering varied connectivity options.

Command for removing unused Docker objects like containers, networks, images, and volumes.

Commands for exporting Docker images to tar archives and importing them back, useful for image transfer and backup.

Command-line tool for scanning Docker images for vulnerabilities, providing detailed reports on potential security issues.

Deprecated component that provided CRI compatibility for Docker in Kubernetes, bridging Docker and CRI.

Unix socket or named pipe used for communication between Docker client and daemon, enabling Docker API access.

Command displaying a live stream of container resource usage statistics, including CPU, memory, and network I/O.

Native clustering and orchestration solution for Docker, allowing you to create and manage a swarm of Docker nodes as a single virtual system.

Real-time stream of Docker daemon events, providing insights into container, image, volume, and network activities.

Command displaying the running processes of a container, similar to the Unix top command but container-specific.

Persistent data storage mechanism for containers, independent of container lifecycle, enabling data persistence.

Different storage backends for Docker volumes, including local, NFS, and cloud-specific drivers.

Text document containing all the commands a user could call on the command line to assemble a Docker image.

Instruction defining build-time variables that users can pass at build-time to the builder.

Guidelines for creating efficient, secure, and maintainable Dockerfiles, optimizing image builds and runtime performance.

Instruction telling Docker how to test a container to check its health status, improving reliability.

Set of commands used in Dockerfiles to build images, including ADD, COPY, RUN, CMD, and ENTRYPOINT.

Set of commands used in Dockerfiles to build images, including ADD, COPY, RUN, CMD, and ENTRYPOINT.

Instruction setting the system call signal that will be sent to the container to exit, customizing shutdown behavior.

File specifying which files and directories should be excluded when building a Docker image.

Approach to software design focusing on the core domain in containerized microservices architectures.

P2P-based image and file distribution system to improve efficiency of image downloads in container environments.

Process of cordoning a node and evicting its pods in preparation for maintenance or decommissioning.

Networking configuration supporting both IPv4 and IPv6 in container environments, enabling broader connectivity options.

Kubernetes feature allowing custom logic to be applied to object creation or modification requests.

Kubernetes feature enabling runtime configuration of audit policies without API server restart.

Automatic creation of storage when persistent volume claims are made in Kubernetes, simplifying storage management.

Monitoring solution for containerized environments, providing deep visibility into application performance.

Combination of Elasticsearch, Logstash, and Kibana for logging and monitoring containerized environments.

Network communication between containers or services within the same cluster or data center.

Deployment of artificial intelligence and machine learning models on edge devices using containers.

Processing and analyzing data at the network edge using containerized analytics applications.

Tools and practices for managing containerized workloads on edge devices, ensuring consistent deployment and updates.

Management and coordination of containerized applications across distributed edge locations.

Security measures and practices specific to containerized edge computing environments, protecting distributed workloads.

Techniques for efficiently distributing containerized workloads across edge devices, optimizing resource usage.

Synchronization of data and state between edge containers and cloud-based systems, ensuring consistency.

Component controlling outbound traffic from a container cluster to external services, enhancing security and traffic management.

Service mesh components managing outbound traffic from the mesh to external services, providing fine-grained control.

Management and policies for outbound network traffic from containers, ensuring security and compliance.

Application Performance Monitoring tool for containerized applications in the Elastic Stack.

Sensitive data stored in an encrypted form in container orchestration platforms, enhancing security.

Comprehensive testing of containerized applications from start to finish, validating entire system functionality.

Kubernetes API resource providing a more scalable alternative to Endpoints for service discovery.

Key-value pairs passed to containers at runtime, used for configuration and runtime behavior control.

High-performance proxy often used in service mesh implementations for containerized environments.

Temporary containers used for troubleshooting or debugging in Kubernetes pods, without affecting the main containers.

Short-lived storage volumes in Kubernetes that share the lifecycle of a pod, useful for temporary data storage.

Routing strategy for load balancing network traffic across multiple paths in container networks.

Distributed key-value store that provides a reliable way to store data across a cluster of machines.

Destinations for cluster-level events in container orchestration platforms, used for monitoring and alerting.

Components or services generating events in containerized environments, often used in event-driven architectures.

Pattern of storing changes to application state as a sequence of events, often used in microservices architectures.

Design pattern capturing all changes to application state as a sequence of events, useful in distributed systems.

Implementation of event sourcing pattern in containerized microservices architectures, ensuring data consistency.

Design approach where containerized services produce, detect, and react to events, enabling loose coupling and scalability.

Rules determining which pods to terminate when a node is under resource pressure in Kubernetes clusters.