Engineering Glossary

A variation of Git pull that fetches remote changes and rebases local commits on top of them instead of merging.

A safer alternative to force push, which only updates the branch if it hasn't been modified since your last fetch.

A command that compares the changes between two commit ranges, useful for reviewing rebased branches.

A low-level command that reads tree objects into the index, used in complex merging scenarios.

A log of all reference updates in a repository, useful for recovering lost commits or branches.

Same as Git Reflog, logs that record when the tips of branches and other references were updated in the repository.

The system Git uses to store references (like branches and tags) as files in the .git directory.

A string that specifies the mapping between remote and local references when fetching or pushing.

Adds a new remote repository to your local Git configuration, associating a name with a URL.

A command that removes remote-tracking branches that no longer exist on the remote repository.

Removes a remote repository from your local Git configuration, deleting its associated references.

A feature allowing one Git object to be transparently used in place of another, useful for history rewriting.

A feature that remembers how you resolved merge conflicts and automatically reapplies the resolution.

A command used to undo changes by moving the current branch head to a specific commit.

A Git reset option that updates the working directory and staging area to match the specified commit.

A Git reset option that moves the HEAD to a specified commit without changing the working directory or staging area.

A command that lists commit objects in reverse chronological order, often used as a basis for other commands.

A command used to parse Git revision syntax and return the corresponding object name.

A command that creates a new commit that undoes the changes made by a previous commit.

A comparison of two Git commands used to undo changes, with revert creating new commits and reset modifying history.

A command to remove files from both the working directory and the Git repository.

A clone that only fetches a limited number of commits, reducing download size and time for large repositories.

A command that summarizes git log output, typically used to create release announcements.

Commits that are cryptographically signed to verify the identity of the committer and ensure integrity.

Tags that are cryptographically signed to verify the identity of the tagger and ensure the tag's integrity.

The process of combining multiple commits into a single commit, often used to simplify history before merging.

A command that applies a stored stash to the working directory without removing it from the stash list.

A command that applies a stored stash to the working directory and removes it from the stash list.

A command that displays the state of the working directory and the staging area.

A repository embedded within another repository, allowing you to keep a Git repository as a subdirectory of another Git repository.

A command that executes a specified shell command for each initialized submodule in a Git repository.

A comparison of two Git features used to include external repositories in a project, each with different use cases.

A feature for managing nested repositories as subdirectories of a parent repository, an alternative to submodules.

A low-level Git command that manipulates the staging area, updating file information and preparing changes for commit.

The communication protocol used between Git clients and servers for efficient data transfer.

A feature allowing multiple working trees to be attached to the same repository, useful for working on different branches simultaneously.

Creates a new working tree associated with the repository, allowing work on multiple branches simultaneously.

A command to list details of each working tree associated with the repository, showing paths and branch information.

A low-level Git command that creates a tree object from the current index, representing a directory structure.

A Git command used to create an archive (zip or tar) of files from a specified commit, useful for creating release packages.

Agreed-upon patterns for naming branches to improve organization and clarity in collaborative projects.

A command to examine the contents and type of Git objects, useful for inspecting repository internals.

A command to resume the cherry-pick process after resolving conflicts in a paused cherry-pick operation.

Levels at which Git configuration can be set: system (all users), global (user), and local (repository).

A command providing detailed statistics about the number and size of objects in a Git repository.

Mechanisms to securely store and retrieve credentials for Git operations, reducing manual authentication.

Custom scripts that determine how Git should generate diffs for specific file types.

A versatile tool for rewriting Git repository history, offering more power and flexibility than git filter-branch.

A more thorough garbage collection process in Git, optimizing repository storage and performance.

A low-level Git command that computes the object ID (SHA-1 hash) for a given file or string, used in Git's internal storage.

Scripts triggered by specific events in Git workflows, used to automate tasks or enforce policies.

The intermediate area in Git where changes are prepared before committing, also known as the staging area.

Individual, uncompressed Git objects stored separately in the object database before being packed.

A command to list the contents of a tree object in Git, showing files and subdirectories.

Custom scripts defining how Git should handle merges for specific file types or patterns.

A low-level Git command that creates a tree object from a specific index or tree listing.

The four fundamental object types used by Git to store repository data and history.

A command to pack references into a single file for improved performance in repositories with many references.

Compressed files containing multiple Git objects, optimizing storage and transfer efficiency in repositories.

Server-side scripts that run after commits have been accepted, often used for notifications or deployments.

Server-side scripts that run before pushed commits are accepted, used to enforce policies or checks.

An improved version of the Git transfer protocol, offering better performance and feature negotiation.

A command to move a series of commits to a new base commit, useful for changing the parent of a branch.

A command to remove old entries from the reflog, useful for cleaning up Git history and freeing space.

A command to combine all loose objects in a repository into packfiles, improving storage efficiency.

Commands to undo changes in Git, with varying impacts on the working directory and staging area.

Techniques and tools for securely storing and accessing sensitive information in Git repositories.

A command that lists references in a Git repository along with their associated commit hashes.

A feature allowing users to check out only a subset of files from a repository, saving space and time.

A command to validate the contents of Git pack files, ensuring data integrity in the object database.

An application that can be installed directly on organizations and user accounts to extend GitHub's functionality.

A version of Markdown with additional features specifically supported on GitHub.

A tool for importing repositories to GitHub from other version control systems or source code hosts.

A feature in GitHub for tracking tasks, enhancements, and bugs for a project.

A platform for posting and finding software development jobs, integrated with GitHub profiles, that was discontinued in 2021.

A platform for developers to sell and buy integrations for GitHub, enhancing workflow and productivity.

A static site hosting service that takes files directly from a GitHub repository to publish websites.

A collaborative documentation feature in GitHub repositories, allowing easy creation and editing of project documentation.

A lightweight, branch-based workflow that supports teams and projects where deployments are made regularly.

A version control workflow that combines feature-driven development and feature branches with issue tracking.

Branching model for Git that defines a strict branching model designed around project releases.

A graphical interface for viewing Git repositories, providing a visual representation of commit history.

Query language for APIs and a runtime for executing those queries with existing data.

A pointer in Git that refers to the latest commit in the current checked-out branch.

A simple program often used as an introduction to a new programming language or environment, including Git tutorials.

A method for quickly patching production releases by branching directly from the main branch or a tag.

Also known as the staging area, where changes are prepared before committing in Git.

Predefined structures for creating new issues in Git repositories, ensuring consistent and complete information.

A static site generator often used with GitHub Pages to create websites from Markdown files in a Git repository.

A tool in GitHub Pages that allows users to easily select and apply pre-designed themes to their Jekyll sites.

Git Large File Storage, an extension for versioning large files in Git repositories.

A library used by GitHub to detect the programming languages used in repositories.

A lightweight markup language commonly used for documentation in Git repositories.

Formerly the default name for the primary branch in Git repositories, now often replaced by 'main'.

The process of combining changes from different branches into a single branch, integrating multiple lines of development.

An application that uses the OAuth protocol to access Git platform APIs on behalf of users.

A secure string used for authentication in Git operations, allowing access to resources without sharing passwords.

The default name for the remote repository from which a local repository was cloned.

A file describing changes between versions, used to share and apply modifications across different Git repositories.

A Git command that fetches changes from a remote repository and merges them into the current branch.

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.