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.

Network architectures allowing customization of data packet processing in cloud infrastructure.

Technique for gradually rolling out new features to subsets of users in cloud applications.

Asynchronous communication pattern for decoupled microservices in cloud architectures.

Infrastructure-as-Code tool for deploying and managing cloud resources using programming languages.

Quantum computing technique for solving optimization problems in cloud environments.

Cloud-based emulation of quantum circuits for algorithm development and testing.

Interface for accessing and controlling quantum computing resources in the cloud.

Cloud-based platforms offering access to quantum computing resources and algorithms.

Experimental database systems leveraging quantum principles for enhanced performance in the cloud.

Techniques for mitigating errors in quantum computations performed in cloud environments.

Experimental cloud services utilizing quantum communication for enhanced security and speed.

Secure communication method using quantum mechanics principles in cloud environments.

Infrastructure for ultra-secure data transmission using quantum principles in cloud systems.

Systems for ultra-secure data transmission using quantum mechanics in cloud environments.

Applying quantum computing techniques to machine learning tasks in cloud environments.

Cloud-based platforms offering quantum-enhanced machine learning algorithms and resources.

Using quantum computing to solve complex optimization problems in cloud environments.

Tools and libraries for developing quantum algorithms in cloud computing environments.

Assessing quantum computational requirements for algorithms in cloud environments.

Tools for creating and testing quantum algorithms in cloud computing platforms, enabling quantum application development.

Demonstrating quantum computers' ability to outperform classical computers in specific tasks.

Metric for measuring the capability and performance of quantum computing systems in the cloud.

Cloud-based access to quantum computing resources and algorithms, enabling exploration of quantum applications.

Algorithms combining quantum and classical computing techniques for enhanced problem-solving in the cloud.

Classical algorithms that emulate quantum computing concepts to solve problems efficiently in the cloud.

Optimization technique inspired by quantum annealing, used for complex problems in cloud computing.

Algorithms based on quantum concepts for identifying unusual patterns in cloud data.

Encryption methods inspired by quantum principles, designed for use in classical cloud systems.

Techniques for data representation inspired by quantum states, used in classical cloud computing.

Optimization methods combining evolutionary principles with quantum concepts in cloud environments.

Data transformation techniques based on quantum principles for machine learning in the cloud.

Applying quantum-like algorithms to financial analysis and prediction in cloud environments.

AI models for data generation based on quantum computing principles, used in classical cloud systems.

Classical ML algorithms incorporating quantum computing concepts for improved performance in the cloud.

NLP techniques leveraging quantum computing principles in classical cloud environments.

AI models mimicking both neural and quantum systems for enhanced learning in cloud computing.

Classical algorithms using quantum-like approaches for solving complex optimization problems in the cloud.

RL techniques incorporating quantum principles for improved decision-making in cloud environments.

Data sampling methods based on quantum principles, used in classical cloud computing tasks.

Data structures and algorithms inspired by quantum tensor networks, used in classical cloud computing.

Encryption methods designed to withstand attacks from future quantum computers in cloud systems.

Database systems designed to maintain security against potential quantum computer attacks in the cloud.

Cloud-based HSMs designed to resist attacks from both classical and quantum computers, ensuring long-term data protection.

Encryption techniques designed to be secure against both quantum and classical attacks in cloud environments.

Classical algorithms mimicking quantum principles to improve AI performance in cloud environments.

Techniques for improving the efficiency and speed of database queries in cloud environments.

Metrics for assessing the usage and financial impact of Reserved Instances in cloud environments.

Process of moving applications to a new cloud platform, often involving some code modifications.

Development paradigm for building responsive, resilient applications in cloud environments.

Copy of a database that allows read-only queries, improving performance and scalability in cloud environments.

Multiple copies of a database for distributing read operations, enhancing performance in cloud systems.

Instantaneous processing and analysis of data as it's generated in cloud environments.

Systems for continuous data processing and analysis in cloud environments with minimal latency.

Process of standardizing diverse data formats in real-time within cloud environments.

Ensuring immediate consistency of data across multiple cloud systems or locations.

Continuous tracking and analysis of business processes in cloud-based systems, enabling rapid decision-making.

Combining data from multiple sources instantly in cloud-based systems, enabling up-to-date analytics and decision-making.

Instantaneous generation and submission of compliance reports in cloud environments.

AI-driven tools suggesting items or actions to users based on data analysis in the cloud.

Generation and submission of compliance-related reports using cloud-based tools and data.

Cloud-based solutions for managing regulatory compliance and reporting, streamlining governance processes.

AI technique where agents learn optimal behaviors through trial and error in cloud environments.

Simulated or real-world scenarios for training AI agents in cloud-based systems.

Managed cloud service for deploying, operating, and scaling relational databases.

Certificates representing clean energy used to power cloud data centers, promoting sustainable cloud operations.

Tracking and optimizing the use of clean energy certificates in cloud operations.

Cloud-based environments ensuring scientific experiments can be replicated precisely.

Optimizing the use of pre-purchased cloud resources to maximize cost savings and ensure resource availability.

Administering pre-purchased cloud instances to optimize costs and resource allocation.

Strategizing the purchase of discounted cloud instances for long-term use to reduce costs.

Pre-purchased cloud compute capacity offered at a discount for a committed term.

Visual representation of cloud resources and their relationships for better management and insight.

Structuring and categorizing cloud resources for efficient management and access control.

Restrictions on the amount or type of cloud resources that can be consumed by a user or account.

Assigning metadata to cloud resources for better organization, management, and cost allocation.

Using metadata tags to track and allocate cloud costs across different projects or departments.

Rules determining how long data is kept in cloud storage before being deleted or archived.

Data privacy principle allowing individuals to request deletion of their personal data from cloud systems.

Adjusting cloud resource allocations to match actual usage, optimizing performance and cost.

Cloud-based systems for coordinating and monitoring multiple robots in industrial or commercial settings.

Cloud-based platform for remotely operating and managing robotic systems, enabling scalable robotics applications.

Using cloud-based software robots to automate repetitive tasks and workflows, improving efficiency and accuracy.

Controlling robots remotely using cloud infrastructure for communication and processing.

AI-driven tools for identifying the source of issues in cloud systems, speeding up troubleshooting and resolution.

Configuration defining network traffic paths between subnets and gateways in cloud environments.

Security technology that detects and prevents attacks on cloud applications in real-time.

Adherence to security and privacy standards for cloud service providers handling customer data.

Digital certificates ensuring secure, encrypted connections to cloud services and applications.

Flexible pricing model offering lower prices on cloud compute usage in exchange for a commitment.

Tools for securely storing, accessing, and managing sensitive information in cloud environments.

Cloud services enabling collaborative computation while keeping individual inputs private.

Proactively testing cloud system resilience by simulating security incidents and failures.

Virtual firewall for controlling inbound and outbound traffic to cloud resources.

Virtual firewalls controlling inbound and outbound traffic for cloud instances within defined security rules.

Platforms integrating security tools and automating incident response in the cloud.

Cloud databases using AI to automate administration, optimization, and security tasks.

Tools allowing non-technical users to perform data analysis in cloud environments without IT assistance.

Standards enabling meaningful data exchange between different cloud systems and services.

AI services that automatically scale and manage underlying infrastructure in cloud environments.

Framework for building and deploying serverless applications in cloud environments.

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.