Engineering Glossary

A feature in Git platforms that allows users to notify specific team members or reference issues by using the @ symbol followed by a username.

A feature allowing developers to test and provide feedback on new Git API functionality before it's officially released.

A Git repository containing version control information without a working directory, typically used as a central repository for collaboration.

A Git command used to perform a binary search through a repository's history to identify the commit that introduced a bug or issue.

An integrated CI/CD service in Bitbucket for automating builds, tests, and deployments.

A Git feature that shows which user last modified each line of a file, helping developers trace changes and understand code history.

A collaborative process of analyzing Git blame information to identify the source of bugs or issues in a codebase.

A parallel version of a repository, allowing developers to work on different features or experiments without affecting the main codebase.

Rules enforced on Git branches to ensure code quality, require reviews, or restrict certain actions before merging.

A premium subscription tier offered by Git hosting services, providing advanced features and support for enterprise users.

A digital certificate issued by a Certificate Authority, used in Git to establish secure HTTPS connections and verify server identities.

A temporary storage area that holds frequently accessed data for quick retrieval, improving system performance.

A Git command used to switch between different branches or restore files from a specific commit in a repository's history.

A Git operation that applies the changes from a specific commit to the current branch, useful for selective code integration.

The practice of selecting and applying specific commits from one branch to another in Git, often used for targeted bug fixes.

The process of creating a local copy of a remote Git repository, allowing developers to work on the codebase independently.

A file specifying individuals or teams responsible for code in different parts of a repository.

Operation in version control systems that saves changes to the local repository, creating a new revision with a unique identifier.

A situation in Git where incompatible changes have been made to the same part of a file, requiring manual resolution before merging.

Practice of merging all developer working copies to a shared mainline several times a day.

Directed Acyclic Graph, the underlying data structure used by Git to represent the commit history and relationships between commits.

A Git operation that moves the HEAD pointer to a specific commit, allowing for temporary exploration of historical states.

A Git command or operation that shows the differences between two commits, branches, files, or other Git objects.

Refers to the direction of change propagation in Git, typically from a parent branch to its child branches or forks.

A feature in Git platforms for browsing repository contents, viewing files, and navigating through different branches.

A Git pattern where each new feature is developed in a dedicated branch, then merged back to the main branch.

A Git command that retrieves changes from a remote repository without automatically merging them into the local branch.

A Git push operation that overwrites the remote branch's history with the local branch, potentially causing data loss if used carelessly.

A personal copy of another user's repository on a Git hosting platform, allowing independent development without affecting the original project.

A no-cost tier offered by Git hosting services, typically with limited features and storage for individual users or small projects.

An encryption software used with Git for signing commits and tags, enhancing security and verification.

A simple way to share code snippets, notes, or small files using Git, often integrated into Git platforms.

Distributed version control system for tracking changes in source code during software development.

A custom shortcut command defined by users to simplify complex or frequently used Git operations.

A mechanism in Git allowing a repository to borrow objects from another repository, useful for saving disk space in large projects.

A Git command that shows the author and commit information for each line in a file, similar to the "blame" feature.

A Git feature that tells the system to temporarily ignore changes to a tracked file, useful for local configuration files.

A mechanism in Git for defining attributes on a per-path basis, controlling how Git handles certain files or directories.

A Git command used to perform a binary search through the commit history to find the commit that introduced a bug.

An automated version of Git Bisect that uses a script to determine if each checked-out commit is "good" or "bad".

An enhanced version of Git Blame that detects lines moved or copied from other files, providing better change tracking.

A Git command that creates a single file containing all the data needed to recreate a repository's branch or history.

Commands to create and verify Git bundles, useful for transferring repository data without using a network.

A quick reference guide summarizing common Git commands and their usage, helpful for both beginners and experienced users.

A Git command used to find commits that haven't been merged upstream, useful for identifying changes to be applied to another branch.

A command to remove untracked files from the working directory, helping to maintain a clean repository state.

A command that allows you to modify the most recent commit, adding new changes or updating the commit message.

A command that allows you to modify the most recent commit, changing its message or contents, and update it without creating a new commit.

Scripts that run automatically before or after a commit, allowing you to enforce rules, format code, or perform checks on changes.

Predefined structures for commit messages, helping maintain consistency and include necessary information in commits.

Metadata added to the end of commit messages, often used for things like issue references or sign-offs.

A file format used to store commit graph structure, improving performance for operations that traverse commit history.

A command to check the integrity and validity of the commit-graph file in a Git repository.

A low-level Git command used to create a new commit object from an existing tree object.

A command and system for setting configuration options for Git on a global, local, or repository level.

A diagnostic command that provides statistics about the number and size of objects in a Git repository.

A system that securely stores and retrieves credentials for Git operations, reducing the need for manual authentication.

A lightweight server for the Git protocol, allowing anonymous read access to Git repositories.

Objects in Git that are not reachable from any reference, often created during rebasing or other history-altering operations.

A command that generates a human-readable name for a commit based on the most recent tag and additional commit information.

A command that shows changes between commits, branches, files, or the working directory and the index.

Various methods used by Git to compute differences between versions of files, optimizing for different scenarios.

An external program or interface used to visualize differences between files or commits in a more user-friendly manner.

A command that fetches updates from all remote repositories configured in a Git project.

A powerful tool for rewriting Git history, allowing for large-scale changes across many commits.

A branching model for Git that defines a strict branching structure designed to support multiple parallel developments.

The process of creating a personal copy of someone else's project, allowing for independent development without affecting the original.

A command that verifies the connectivity and validity of objects in the Git database.

Verifies the integrity of objects in the Git database, checking for corruption and inconsistencies.

A security feature in Git that uses GPG to sign and verify commits and tags, ensuring authenticity.

A process that optimizes the Git repository by removing unnecessary files and compressing objects.

Short for Git Garbage Collection, a command to clean up unnecessary files and optimize the repository.

A command for searching files in a Git repository using regular expressions or simple string matching.

A script that Git automatically runs before or after specific events, like commits or merges, to customize workflows and enforce rules.

Same as Git Hook, scripts triggered by various Git actions to automate tasks or enforce policies.

A mechanism to specify intentionally untracked files that Git should ignore, typically defined in a .gitignore file.

The internal format used by Git to store the staging area information, representing the next commit to be made.

A command used to initialize a new Git repository, creating the necessary directory structure for version control.

A feature allowing users to modify commits in their branch history, useful for cleaning up before merging.

The underlying data structures and mechanisms Git uses to store and manage version control information.

A command to parse and manipulate the trailer lines typically found at the end of commit messages.

A system that manages large files by storing them outside the main repository, replacing them with lightweight pointers to improve performance and efficiency.

An extension that replaces large files with text pointers in Git, while storing the file contents on a remote server.

A command to display the commit history of a repository, showing details like author, date, and commit message.

A set of tasks performed to keep a Git repository in good condition, including garbage collection and optimization.

Different algorithms used by Git to combine separate development histories when merging branches.

External programs or interfaces used to resolve merge conflicts in a more visual and user-friendly manner.

A command that finds the best common ancestor between two commits, useful for determining the point where branches diverged.

A feature that improves performance for repositories with many pack files by creating an index across multiple packs.

A feature allowing multiple references (like branches or tags) with the same name to coexist by separating them into different namespaces.

A way to add or inspect object notes, allowing additional information to be attached to Git objects without changing their SHA-1 hash.

The system Git uses to store all the versions of files, commits, and other objects in a content-addressable filesystem.

Compressed files containing Git objects, used to efficiently store and transfer repository data.

The internal format used by Git to store multiple objects efficiently in a single file, reducing disk usage and improving performance.

A feature allowing users to clone a repository without downloading all of its objects, saving bandwidth and storage.

A file format representing changes between versions, used for sharing and applying modifications across different repositories.

Low-level Git commands that expose the internal workings of Git, primarily used for scripting and advanced operations.

User-friendly commands for everyday tasks, like git status and git commit, designed for ease of use and clear output.

The native network protocol used by Git for efficient data transfer between repositories.

A command that removes objects that are no longer reachable from any references in the repository.

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.

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

Custom, cluster-level resources in Kubernetes that can be allocated to containers, such as GPUs or FPGAs.

Kubernetes component allowing custom logic to be added to the scheduling process for advanced pod placement strategies.

HTTP callbacks that receive admission requests and can modify or reject objects before persistence in Kubernetes.

Incorporation of external secret management systems like HashiCorp Vault with container platforms.

Kubernetes service type that maps a service to a DNS name, useful for representing external services within a cluster.

Kubernetes services that reference external resources by DNS name, facilitating access to external dependencies.

Allocation and management of Field-Programmable Gate Arrays as resources in container orchestration platforms.

Implementation of Function-as-a-Service platforms on Kubernetes, enabling serverless architectures in container environments.

Open-source cloud-native runtime security project, providing real-time threat detection for containerized environments.

Use of Falco to monitor and alert on unexpected behavior in running containers and Kubernetes clusters.

Technique of intentionally introducing failures in containerized systems to test resilience and error handling capabilities.

Flags in Kubernetes used to enable or disable specific features, allowing for fine-grained control over cluster functionality.

Implementation of service mesh across multiple Kubernetes clusters, enabling cross-cluster service communication.

Technique for managing multiple Kubernetes clusters from a single control plane, useful for multi-cloud or hybrid deployments.

Kubernetes feature allowing controllers to implement asynchronous pre-delete hooks, ensuring proper resource cleanup.

Use of finalizers in Kubernetes operators to perform cleanup operations before custom resource deletion.

Lightweight virtualization technology used for creating and managing secure, multi-tenant container environments.

Minimal virtual machines used by Firecracker to provide strong isolation for containers or functions.

Open-source, Kubernetes-native serverless framework for running functions, supporting multiple languages and event triggers.

Progressive delivery tool for Kubernetes, automating canary releases and A/B testing of containerized applications.

Network fabric for containers designed to give a consistent, easily configured layer 3 network across multiple hosts for Kubernetes.

CNI plugin providing a simple overlay network for container communication across hosts, enabling basic networking.

Out-of-tree plugin mechanism in Kubernetes for interfacing with third-party storage systems, predating CSI.

Open-source data collector for unified logging layer, enabling efficient data collection and consumption for better use of data.

Kubernetes deployment ensuring Fluentd log collectors run on every node for comprehensive log aggregation.

Modular components in Fluentd for ingesting logs from various sources in containerized environments.

Modular components in Fluentd for sending processed logs to various destinations from containerized environments.

GitOps toolkit for deploying applications to Kubernetes, automating the deployment pipeline from Git repositories.

Next generation of Flux, providing a set of continuous delivery solutions for Kubernetes with improved modularity and features.

Automatic adjustment of function instances based on demand in serverless container environments.

Standardized way of building function containers, abstracting away infrastructure concerns for developers.

Process of allocating and managing GPU resources for containers running machine learning workloads.

Tool facilitating development and testing of Kubernetes applications in remote clusters.

Kubernetes feature allowing for dynamic provisioning of short-lived volumes for containers.

Network virtualization technology used in some container networking solutions for multi-tenant environments.

Integrated CI/CD platform with native container and Kubernetes support for building, testing, and deploying applications.

Set of composable APIs and specialized tools for building continuous delivery systems on top of Kubernetes.

Operational model where the desired state of a Kubernetes cluster is version controlled and automated from a Git repository.

Distributed file system that can be used for providing persistent storage to containerized applications in Kubernetes.

Kubernetes operators written in Go, leveraging the client-go library for interacting with the Kubernetes API.

Tool that provides recommendations for resource requests and limits for Kubernetes deployments.

Managed compute platform for deploying containerized applications in a serverless environment.

Managed Docker registry service by Google for storing, managing, and securing container images.

Customizable visualization panels for monitoring containerized environments and applications.

Configurable backends in Grafana for retrieving metrics and logs from various container monitoring systems.

Horizontally-scalable, multi-tenant log aggregation system designed for use in containerized environments.

Automated setup and configuration of Grafana dashboards and data sources in container deployments.

Implementation of GraphQL APIs in microservices architectures for flexible data querying and manipulation.

Kubernetes Quality of Service class ensuring pods receive the exact amount of requested resources, ideal for critical workloads.

Open-source container registry providing content trust, vulnerability scanning, and RBAC for storing and distributing container images.

Continuous Delivery platform supporting various deployment strategies for containerized applications in Kubernetes environments.

Kubernetes services that don't allocate a cluster IP, used for direct pod-to-pod communication in stateful applications.

Package manager for Kubernetes that helps you define, install, and upgrade even the most complex Kubernetes applications.

Packages of pre-configured Kubernetes resources, facilitating the deployment and management of complex applications.

Helm feature allowing custom actions to be performed at specific points in a release's lifecycle, enhancing deployment flexibility.

Kubernetes operator that manages Helm releases, automating the deployment and lifecycle of Helm-based applications.

Storage locations for packaged Helm charts, enabling version control and distribution of containerized application configurations.

Mechanism in Helm for parameterizing chart templates, allowing for customization of deployments across different environments.

Kubernetes operators leveraging Helm charts for managing the lifecycle of complex, stateful applications.

Kubernetes feature allowing nested namespaces, providing finer-grained resource isolation and multi-tenancy capabilities.

Kubernetes controller that automatically adjusts the number of pods in a deployment based on observed metrics.

Metrics used by HPA to determine when to scale containerized applications, including CPU, memory, and custom metrics.

Extended HPA functionality allowing scaling decisions based on application-specific or external metrics.

Kubernetes resource defining the behavior for automatically scaling the number of pods in a replication controller or deployment.

Container networking mode where pods use the host's network namespace, bypassing virtual networks for improved performance.

Linux kernel feature for managing large memory pages, improving performance for memory-intensive containerized applications.

IP Virtual Server, a transport-layer load balancing technology used in Kubernetes for efficient service proxying and load distribution.

Implementation of IPsec protocols to secure container-to-container communications across hosts and clusters.

Kubernetes feature enabling pods and services to be assigned both IPv4 and IPv6 addresses, facilitating transition to IPv6.

Support and configuration for using IPv6 addressing in Kubernetes clusters, enabling large-scale container deployments.

Approach to designing APIs that can be called multiple times without changing the result, crucial for reliable microservices.

Lightweight, standalone, executable package that includes everything needed to run a piece of software.

Unique identifiers for container images, ensuring consistency and integrity across different environments and registries.

OCI specification for multi-architecture container images, allowing a single image to support multiple platforms.

Technique for reusing unchanged layers when building or pulling container images, improving build and deployment speed.

Strategies for reducing the size and number of layers in container images, improving storage efficiency and pull times.

Metadata file describing the contents and configuration of a container image, crucial for image distribution and deployment.

Standardized format for container image manifests, supporting multi-architecture images and content-addressable layers.

Process of moving container images through different environments (e.g., dev, staging, production) in a controlled manner.

Process of downloading container images from a registry to a local environment or container runtime.

Process of uploading locally built container images to a remote registry for distribution and deployment.

Rules defining how long container images are kept in a registry, balancing storage costs with availability needs.

Automated process of analyzing container images for vulnerabilities, misconfigurations, and compliance issues.

Cryptographic process of digitally signing container images to ensure their integrity and authenticity during distribution.

End-to-end process of cryptographically signing container images and verifying their authenticity before deployment.

Technique of combining multiple layers of a container image into a single layer, potentially reducing image size and complexity.

Process of assigning human-readable labels to container images, facilitating version management and deployment workflows.

Automated security analysis of container images to identify known vulnerabilities in installed packages and dependencies.

Component in container runtimes responsible for managing image-related operations like pulling, pushing, and local storage.

Practice of replacing entire container instances instead of modifying existing ones, enhancing consistency and reliability.

Approach to deploying containerized applications by directly specifying the desired actions, as opposed to declarative methods.

Distributed data management systems optimized for high-performance data processing in containerized environments.

Technique for updating containerized applications or infrastructure components without full redeployment, minimizing downtime.

Built-in plugins in Kubernetes for various functionalities, as opposed to out-of-tree or external plugins.

Technique for optimizing container image distribution by allowing partial and on-demand loading of image layers.

Automated testing of infrastructure definitions, ensuring consistency and reliability in container deployments.

Kubernetes API object managing external access to services in a cluster, typically HTTP, providing load balancing and SSL termination.

Components implementing the Ingress resource in Kubernetes, managing the routing of external traffic to internal services.

Entry point for external traffic in service mesh architectures, providing routing, security, and observability for incoming requests.

Design pattern using specialized containers that run before app containers in a pod, used for setup or dependency management.