Engineering Glossary

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-hosted version of ROS, providing robotics middleware and tools as a service.

Platforms for simulating and testing robotic systems using cloud computing resources.

Services for processing and rendering 3D audio experiences using cloud computing resources.

Factory automation systems leveraging cloud resources for data processing, analytics, and remote management.

Systems using cloud computing to enhance navigation capabilities of autonomous vehicles or robots.

Systems using cloud computing to enhance navigation capabilities of autonomous vehicles or robots.

Implementations of ACID properties for database transactions in distributed cloud environments.

Integrated security solutions designed to protect cloud-native applications throughout their lifecycle.

Specialized software for identifying and resolving issues in cloud-native applications and microservices.

Frameworks and services optimized for training and deploying deep learning models in cloud environments.

Specialized hardware accelerators optimized for cloud-native workloads and accessible as cloud services.

Network security tools designed specifically for protecting cloud-native applications and infrastructure.

Cloud-based services providing hardware-level encryption key management and cryptographic operations.

Integrated development environments optimized for building and deploying cloud-native applications.

Interactive computing environments for data analysis and machine learning, designed for cloud deployment.

Traffic distribution services designed specifically for cloud-native applications and microservices architectures.

Virtualized network services designed to run in containerized environments, optimized for cloud infrastructure.

Network services built as microservices, optimized for deployment in cloud-native environments.

Scalable platforms for training AI agents using reinforcement learning algorithms in cloud environments.

SIEM solutions designed for collecting and analyzing security data from cloud-native applications.

Automated coordination of security tools and processes in cloud-native environments.

Process of redesigning applications and infrastructure to fully leverage cloud-native technologies and practices.

BCI systems that use cloud computing for data processing and AI-driven interpretation of brain signals.

Design approach for building applications that can run on multiple cloud platforms without significant modifications.

Technology enabling virtual access to specialized AI hardware accelerators in the cloud.

Services for processing and delivering AR experiences using cloud computing resources.

Frameworks for building AI systems that mimic human cognitive processes using cloud resources.

Using cloud computing resources to perform complex calculations required for mining digital currencies.

Managed services for connecting, monitoring, and controlling Internet of Things devices using cloud infrastructure.

Services for automating repetitive tasks and workflows using software robots hosted in the cloud.

Platforms offering drag-and-drop interfaces for creating applications without traditional coding, hosted in the cloud.

Practices and tools for building, training, and deploying AI models optimized for cloud environments.

Techniques and tools for leveraging cloud-based GPUs to accelerate computational tasks in cloud-native applications.

Process of moving applications and data from one cloud provider to another, optimizing for cost, performance, or features.

Unified computing paradigm combining cloud, edge, and fog computing for optimal data processing and latency.

Infrastructure-as-Code service for defining and provisioning AWS infrastructure using declarative templates.

Visual tool for creating, viewing, and modifying AWS CloudFormation templates, simplifying infrastructure-as-code development.

Small-scale cloud data centers located at the edge of the network to support low-latency applications.

Lightweight application layer protocol designed for constrained devices in Internet of Things (IoT) environments.

Delay in executing serverless functions when they haven't been used recently and need to be initialized.

Multiple robots working together, leveraging cloud resources for coordination and shared learning.

Cloud-based tools enabling teams to jointly design and refine data models for applications.

Cloud platforms supporting team-based data analysis, model development, and knowledge sharing.

Cloud-based recommendation systems processing large datasets to suggest items based on user preferences.

Database storage format optimized for analytical queries in cloud data warehouses.

Architectural pattern separating read and write operations in cloud-based applications.

Real-time analysis of data streams to identify and respond to specific patterns or events in cloud environments.

Standardized guidelines for ensuring cloud systems meet regulatory requirements (e.g., GDPR, HIPAA, PCI DSS).

Regulatory standards guiding data protection and security practices in cloud computing.

Continuous assessment of cloud systems and practices to ensure adherence to regulatory standards.

Cloud service allowing dynamic assembly of virtualized compute, storage, and networking resources.

Storage devices with built-in processing capabilities, enabling data processing within the storage subsystem.

Storage hardware with integrated computing resources for in-situ data processing in cloud environments.

AI technology that enables machines to interpret and understand visual information from images or videos.

Cloud services offering pre-built models and tools for image and video analysis tasks.

Simultaneous execution of multiple computations or processes in cloud environments.

Technology protecting data in use by performing computation in a hardware-based trusted execution environment.

Isolated environments in cloud infrastructure for secure processing of sensitive data.

Monitoring and identifying unintended changes in cloud resource configurations over time.

Permissioned blockchain network operated by a group of organizations in a cloud environment.

Serverless compute platforms for running containers without managing the underlying infrastructure.

Optimizing container resource allocations to improve efficiency and reduce costs in cloud environments.

Technologies and practices for managing network connectivity between containerized applications in the cloud.

Tools and practices for protecting containerized applications during execution in cloud environments.

Measures and tools for protecting containerized applications and their data in cloud environments.

User interfaces that adapt based on user context, device capabilities, and environmental factors.

Dynamic approach to security that continuously analyzes and adapts to changing risks in cloud environments.

Ongoing process of assessing and improving security risks in cloud infrastructure configurations.

Automated, real-time tracking and reporting of compliance status for cloud resources and applications.

Ongoing validation of data quality, integrity, and consistency in cloud-based data pipelines and storage systems.

Automated process of releasing code changes to production environments in cloud infrastructure.

Automated pipeline for building, testing, and deploying applications in cloud environments.

Ongoing process of improving cloud resource utilization, performance, and cost-efficiency.

Constant monitoring and analysis of application performance in cloud environments to identify bottlenecks.

Ongoing process of adjusting cloud resource allocations to match actual usage and demand.

Automated process of regularly updating machine learning models with new data in cloud environments.

Ongoing process of validating cloud infrastructure and applications against defined policies and configurations.

Process of distributing cloud expenses across different departments, projects, or cost centers within an organization.

Automated identification of unusual or unexpected patterns in cloud spending, helping optimize resource allocation and budgeting.

Associating cloud expenses with specific resources, services, or business units for accurate accounting.

Tool for visualizing and analyzing cloud spending patterns and trends, helping optimize resource allocation and budgeting.

Automated suggestions for reducing cloud expenses while maintaining performance and reliability.

Intelligent scaling of cloud resources considering both performance requirements and cost implications.

Ensuring cloud data transfers comply with international data protection regulations.

Synchronizing data across multiple cloud providers for redundancy and disaster recovery.

Blockchain-based agreements executable across different cloud platforms, enabling multi-cloud decentralized applications.

Execution environments for cloud-native applications that work across multiple cloud providers.

Services for securely generating, storing, and rotating encryption keys in cloud environments.

Services defending against Distributed Denial of Service attacks on cloud-based applications and infrastructure.

Experimental technology using DNA molecules to store digital data, potentially leveraging cloud for processing.

Experimental technique using DNA molecules to store digital data, potentially integrated with cloud for processing and retrieval.

Managed Domain Name System services provided by cloud platforms for translating domain names to IP addresses.

Portable runtime for building microservices-based applications across cloud and edge environments.

Tools for creating interactive, visual representations of cloud data and metrics.

Centralized repository for metadata about an organization's data assets in the cloud.

Tools for organizing, indexing, and searching data assets across cloud environments.

Categorizing data based on sensitivity and importance for appropriate handling in cloud storage.

Monitoring changes in data distributions that may affect machine learning model performance in cloud environments.

Integrated architecture for managing and accessing data across diverse cloud and on-premises environments.

Unified data management framework spanning multiple cloud and on-premises 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.