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.

Systems integrating blockchain technology with traditional cloud services for enhanced security and transparency.

Tools and practices for managing resources across public and private cloud environments.

Tracking performance and health of resources spanning both public and private cloud environments.

Coordinating and automating workflows across multiple cloud environments and on-premises infrastructure.

Data storage solutions that span both on-premises and cloud environments, offering flexible and scalable data management.

Network solutions that connect on-premises infrastructure with cloud services, enabling seamless integration and data flow.

Managing user identities and access across both on-premises systems and cloud services.

Tools for connecting applications, data, and processes across on-premises and cloud environments.

Computational methods combining quantum and classical processing in cloud environments.

Using both quantum and classical computing resources in tandem for cloud-based problem-solving.

Database systems supporting both real-time transactions and analytics in cloud environments.

Integrated compute, storage, and networking in a single cloud-managed system, simplifying data center architecture.

Process of optimizing machine learning model parameters in cloud-based environments.

Software layer that manages virtual machines in cloud computing environments, enabling efficient resource utilization and isolation.

Defined permissions and access controls for users and services in cloud environments.

International standard for information security management in cloud environments.

Security tools that verify user identity before granting access to cloud applications.

Automated process of examining container images for vulnerabilities in cloud environments.

Security approach based on replacing rather than modifying cloud infrastructure components.

Cloud storage that prevents data modification after writing, ensuring data integrity and compliance.

Database system that primarily uses main memory for data storage in cloud environments.

Distributed computing using the collective memory of networked computers in the cloud.

Processing data within network devices to reduce latency and bandwidth use in cloud architectures.

Systems for efficiently updating computation results when input data changes in cloud environments.

Process of organizing data to optimize query performance in cloud databases, enhancing search and retrieval efficiency.

Automated tools for identifying security issues in infrastructure definition files.

Validating infrastructure definitions before deployment in cloud environments, ensuring reliability and consistency.

Cloud-based provision of virtualized computing resources over the internet, including servers, storage, and networking.

Generating cloud infrastructure configurations directly from application code, streamlining development and deployment processes.

Predefined virtual server configurations optimized for different use cases in cloud environments.

Visual tool for creating and managing data integration processes in cloud environments.

Tools for tracking and analyzing the performance of integrated cloud systems and processes.

Automated data storage class selection based on access patterns in cloud environments.

Automatic movement of data between storage tiers based on usage patterns in the cloud.

Network management approach using AI to automate configuration based on business intent.

Network node that connects cloud VPCs to the internet, enabling communication with external resources.

Decentralized storage system for distributing and accessing files in the cloud, enhancing data availability and resilience.

Security tool that monitors network traffic for suspicious activity in cloud environments.

Security system that not only detects but also blocks potential threats in cloud networks.

Processing and analyzing data from Internet of Things devices in cloud environments.

Identifying unusual patterns in data from IoT devices using cloud-based analytics.

Fully managed IoT application platform in the Azure cloud for connecting, monitoring, and managing IoT devices at scale.

Process of collecting and importing data from IoT devices into cloud systems for storage, analysis, and processing.

Centralized repository in the cloud for storing large volumes of IoT device data.

Cloud-based systems for monitoring, updating, and controlling IoT devices, ensuring efficient operation and security at scale.

Process of setting up and configuring IoT devices for secure cloud connectivity.

Software development kit for building applications that connect IoT devices to cloud services.

Deploying cloud intelligence directly to IoT devices for local processing and analytics.

Processing data from IoT devices locally before sending to the cloud, reducing latency and bandwidth usage.

AWS service for running local compute, messaging, and data caching for connected IoT devices.

Central message hub for bi-directional communication between IoT applications and devices.

Tools for connecting and managing diverse IoT devices and data streams in cloud environments.

Cloud-based tools for managing IoT devices from deployment through retirement, ensuring efficient device administration.

Remote software updates for IoT devices managed through cloud platforms, ensuring device security and functionality.

Cloud-based platform for developing, managing, and integrating IoT applications and devices.

Lightweight communication protocols designed for IoT devices in cloud environments.

Cloud service for processing and acting on data from IoT devices based on predefined conditions.

Measures to protect IoT devices, data, and infrastructure in cloud-connected environments.

Real-time processing and analysis of data streams from IoT devices in the cloud.

Cloud service for creating and managing cryptographic keys for data encryption, ensuring secure data protection.

Kubernetes-based platform for building, deploying, and managing serverless workloads.

Kubernetes-native serverless framework enabling function deployment without managing underlying infrastructure.

Rules for controlling network traffic between pods in Kubernetes clusters, enhancing security in cloud-native environments.

Tools for assessing and improving security configurations in Kubernetes environments.

Managed Kubernetes platforms offered by major cloud providers for container orchestration.

Kubernetes native configuration management tool for customizing application configurations.

AWS service for running Lambda functions at edge locations to customize content delivery.

Template for EC2 instance settings used in Auto Scaling groups, defining instance type, AMI, and other parameters.

Security concept of granting only the minimum permissions necessary in cloud environments.

Managing software licenses efficiently to reduce costs in cloud environments, ensuring compliance and cost-effectiveness.

Migration strategy of moving applications to the cloud without significant modifications.

Minimalist virtual machines designed for fast startup and low resource usage in cloud environments.

Lightweight service mesh for Kubernetes, providing observability, reliability, and security for microservices.

Using liquid to dissipate heat in densely packed cloud data centers for improved efficiency.

Service that distributes incoming network traffic across multiple servers to ensure no single server becomes overwhelmed.

Process of collecting, analyzing, and visualizing log data from cloud resources for insights and troubleshooting.

AI-driven analysis of log data to identify patterns, anomalies, and insights in cloud environments.

Tools for building application integrations with minimal hand-coding in cloud environments.

Cloud-based tools for creating applications with little to no traditional programming.

Cloud services enabling app development with minimal coding, using visual interfaces and pre-built components.

Lightweight messaging protocol designed for IoT devices and low-bandwidth networks in cloud ecosystems.

Cloud-based services for building, training, and deploying machine learning models at scale.

Centralized management of critical business data entities in cloud environments.

Tools for developing AI models that can learn how to learn, improving adaptability in cloud environments.

Cloud tools for organizing, integrating, and governing metadata across various data sources.

Advanced antenna designs using engineered materials to enhance cloud network performance.

Gathering and storing quantitative data about cloud resource performance and usage.

Architectural style where frontend apps are composed from independently deployable components in the cloud.

Automating the process of dividing cloud networks into secure zones for enhanced protection.

Connecting and coordinating interactions between independent microservices in cloud architectures.

Strategic process of preparing and scheduling the move of applications and data to cloud environments.

Cloud-based platforms for developing and deploying mixed reality applications, combining virtual and physical environments.

Bringing cloud computing capabilities closer to mobile users to reduce latency and improve performance.

Extending cloud computing capabilities to the edge of mobile networks for lower latency.

Process of making machine learning models available for use in cloud production environments.

Techniques for understanding and interpreting decisions made by AI models in cloud environments.

Tracking the performance and behavior of deployed machine learning models in cloud environments.

Centralized repository for storing and managing machine learning models in cloud environments.

Process of making trained machine learning models available for real-time predictions in cloud environments.

Process of teaching machine learning algorithms to make predictions using data 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.