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.

Cloud execution model where providers manage infrastructure, automatically scaling resources as needed.

Container-based applications run on serverless platforms, combining containerization with auto-scaling.

Strategies to minimize expenses in serverless architectures while maintaining performance.

Processing and analyzing data using serverless compute resources in the cloud, providing scalable and cost-effective insights.

Handling and transforming data using auto-scaling, event-driven cloud services, eliminating infrastructure management overhead.

Fully managed database services that scale automatically based on demand, eliminating the need for manual provisioning and management.

Practices combining serverless architectures with DevOps principles for efficient cloud development.

Extract, Transform, Load processes using serverless compute for data integration in the cloud.

Connecting applications and data sources using serverless compute in cloud environments.

Building application components as independent, auto-scaling functions in the cloud.

Network management and routing tailored for serverless architectures in cloud environments.

Techniques for efficient use of compute, storage, and network resources in serverless systems.

Protecting serverless applications from vulnerabilities and attacks in cloud environments.

Tools and practices for implementing security in serverless cloud architectures.

Comprehensive solutions for protecting serverless applications and data in the cloud.

Websites built and hosted using serverless cloud services for automatic scaling.

Orchestrating sequences of serverless functions to create complex, scalable processes.

Rules for managing permissions in AWS Organizations across multiple accounts, enforcing security and compliance standards.

Automated detection of services and their network locations in cloud environments.

Targets for service performance and availability in cloud environments, defining quality standards for cloud services.

Central component managing configuration and policies in a service mesh architecture.

Component handling inter-service communication in a service mesh architecture, managing traffic flow and security.

Standardized APIs for interacting with service mesh technologies in cloud environments.

Monitoring and analyzing traffic and behavior within a service mesh architecture.

Protecting inter-service communication within a service mesh in cloud environments.

Distributing data across multiple database instances to improve performance and scalability in cloud environments.

Systems for tracking and allocating cloud resource costs within an organization.

Deploying components of an application as separate containers alongside the main container in cloud environments.

Helper containers deployed alongside main application containers to provide additional functionality in cloud environments.

Using light-based chips for high-speed data transmission in cloud data centers, improving network performance and efficiency.

Tools for implementing SRE practices in cloud environments, focusing on automation and reliability.

Cloud-based systems for creating, deploying, and managing self-executing digital contracts.

Approach to cloud network management using software-based controllers instead of hardware.

Managing and optimizing power allocation in cloud data centers through software control.

Abstracting storage services from underlying hardware in cloud environments for flexible management.

Technology for efficiently routing traffic between cloud and on-premises resources.

Integrating digital information with the physical world in cloud-based augmented and virtual reality systems.

Cloud databases optimized for storing and querying location-based data, essential for geospatial applications and analytics.

Cloud-based service for converting spoken language into text, enabling voice-controlled interfaces and transcription.

Strategies for efficiently using and monitoring discounted, interruptible cloud compute resources.

Tools for optimizing the use of discounted, interruptible cloud compute resources.

Techniques for maximizing cost savings and availability when using discounted, interruptible cloud resources.

Discounted, interruptible cloud compute resources available for bidding, offering cost savings for flexible workloads.

Techniques for maintaining application data consistency in distributed cloud environments.

Serverless architectures that maintain state between function invocations in the cloud.

Cloud-based functions that don't retain state between invocations, enhancing scalability.

AWS service for coordinating multiple Lambda functions into serverless workflows.

High-performance, low-latency memory technology bridging DRAM and SSDs in cloud infrastructure.

Mechanisms to ensure consistent performance levels for storage resources in cloud environments.

Automatically moving data between storage tiers based on access patterns to balance cost and performance.

Real-time analysis of continuously flowing data in cloud environments, enabling instant insights and rapid decision-making.

Handling and analyzing large volumes of real-time data streams in cloud environments.

Cloud-based systems for querying continuous data streams using SQL-like syntax, enabling real-time data analysis.

Segmented portion of a cloud network with its own IP address range, allowing for improved security and traffic management.

Tools for tracking and reporting environmental metrics of cloud resource usage, promoting eco-friendly cloud computing.

Documenting environmental impact and energy efficiency of cloud operations, promoting transparency and accountability.

Allocating cloud resources with consideration for environmental impact, optimizing for energy efficiency.

Improving cloud resource efficiency to minimize environmental impact while maintaining performance and reliability.

Practices and technologies minimizing the environmental impact of cloud operations.

Moving to the cloud with strategies to minimize environmental impact, optimizing resource usage and energy efficiency.

Creating cloud facilities with minimal environmental impact and maximum energy efficiency.

Using collective behavior algorithms to optimize cloud resource allocation, improving efficiency and performance.

Coordinating multiple robots using cloud-based swarm intelligence algorithms, enabling scalable and adaptive robotic systems.

Creating artificial datasets for testing and development in cloud environments, preserving privacy and expanding data availability.

Cloud services for creating AI-generated images, videos, and audio, enabling content creation at scale.

Ultra-low latency network interfaces for real-time tactile feedback in cloud applications.

Methods for labeling cloud resources to improve organization, management, and cost allocation.

Cloud infrastructure specifically designed for telecommunications service providers.

Cloud-based platforms for remotely operating robots with real-time feedback, enabling distant manipulation and interaction.

Cloud service converting written text into spoken words, often used in accessibility and voice interface applications.

Cloud-based predictive analytics for time-dependent data, used for trend analysis and future projections.

Cloud databases optimized for storing and querying time-stamped data, essential for IoT and monitoring applications.

Deploying machine learning models on resource-constrained IoT devices connected to the cloud.

Cloud-based tools for replacing sensitive data with non-sensitive tokens to enhance security.

Comprehensive assessment of direct and indirect costs associated with cloud adoption and usage.

Technique of using pre-trained AI models for new tasks, reducing training time and data requirements in the cloud.

Cloud networking service that connects VPCs, on-premises networks, and other cloud services.

Set of best practices for building scalable, maintainable cloud-native applications.

Lightweight, single-purpose machine images for running cloud applications with minimal overhead.

Analysis of costs and revenues associated with individual cloud service units or transactions.

Efforts to create standardized interfaces for interacting with various cloud services.

Secure, encrypted link between a local network and cloud resources, enabling private access to cloud services over public networks.

Cloud-based delivery of virtual reality content to end-user devices, enabling immersive experiences without local processing.

Cloud databases optimized for storing and querying high-dimensional vector data, often used in AI applications.

Managing and tracking changes to code and configurations in cloud-based repositories.

Increasing the capacity of a single cloud resource by adding more power (CPU, RAM, etc.).

Software-defined network providing connectivity for cloud resources, enabling secure and flexible communication.

Cloud-based platforms offering customizable, isolated development and testing environments.

Emulations of computer systems providing scalable, isolated environments in the cloud.

Software-based network devices deployed in cloud environments for various networking functions.

Software implementations of network functions running on cloud infrastructure, replacing traditional hardware appliances.

Cloud-based services for creating, rendering, and delivering virtual reality experiences.

Cloud-hosted VR environments for collaborative work and data visualization, enabling immersive remote collaboration.

Speech-based interfaces for interacting with cloud applications and services, enhancing accessibility and user experience.

Cloud-based processing for generating 3D holographic or volumetric images, enabling immersive visualization applications.

Cloud services for delivering 3D video content for immersive viewing experiences.

Metric measuring water consumption efficiency in cloud data centers, promoting sustainable resource management.

Tracking water consumption and efficiency in cloud data center operations, promoting sustainable resource management.

Cloud-based security service that filters and monitors HTTP traffic between web applications and the Internet.

Low-level language for running high-performance applications in cloud-based web browsers.

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.