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.

Object storage gateway for Ceph, providing S3-compatible API for container storage needs in Kubernetes.

Linux kernel feature for limiting, prioritizing, and isolating resource usage of container groups.

Technique for tracking and capturing changes in databases, useful in microservices architectures.

Practice of intentionally injecting failures in CI/CD pipelines to improve system resilience.

Platform for simulating various abnormal scenarios in Kubernetes environments to test reliability.

Static code analysis tool for detecting misconfigurations in Infrastructure as Code deployments.

Open-source networking, observability, and security solution for container workloads based on eBPF.

eBPF-powered service mesh offering high-performance traffic management and security for microservices.

Observability platform for Kubernetes providing deep visibility into container network flows.

High-performance networking mode in Cilium using extended Berkeley Packet Filter for packet processing and security.

Continuous integration and delivery platform supporting containerized application testing and deployment.

Fault tolerance pattern preventing cascading failures in distributed containerized systems.

Technique to detect failures and prevent system overload in microservices architectures, enhancing resilience.

Design pattern for handling large payloads in microservices by storing data externally, improving efficiency.

Open-source tool for static analysis of vulnerabilities in container images, enhancing security.

Kubernetes component managing interactions between cluster and cloud provider APIs, enabling cloud integration.

Specification for packaging and managing distributed applications across different environments.

Tooling for building container images from source code without Dockerfiles, adhering to best practices.

Architectural patterns specifically tailored for distributed, containerized applications in cloud environments.

Specification for describing event data in common formats, facilitating event-driven architectures in cloud environments.

Isolation technique in containerized microservices to prevent failure propagation across the system.

Group of servers or other resources that work together as a single system to provide high availability.

Kubernetes project for declarative management and provisioning of clusters using Kubernetes-style APIs.

Implementation of Cluster API for specific infrastructure providers, enabling cluster lifecycle management.

Kubernetes component that automatically adjusts the size of a cluster based on resource demands.

Process of estimating and allocating resources needed for container workloads in a cluster.

Mechanism for coordinating multiple Kubernetes clusters, enabling multi-cluster application deployment and management.

Interconnected network of container clusters, enabling cross-cluster communication and resource sharing.

Network configuration and management within and between container clusters, enabling inter-container communication.

Procedure for updating the Kubernetes version and components of a cluster while minimizing disruptions.

Centralized logging solution capturing logs from all containers and nodes in a cluster, aiding in troubleshooting.

Kubernetes component that automatically adjusts the number of nodes in a cluster based on resource utilization.

Kubernetes service type that exposes the service on an internal IP within the cluster, facilitating internal communication.

Kubernetes services accessible only within the cluster, used for internal communication between pods.

Kubernetes RBAC resource defining permissions across an entire cluster, not limited to a single namespace.

Kubernetes RBAC resource that binds a ClusterRole to users, groups, or service accounts across all namespaces.

Custom resource in Operator Lifecycle Manager describing a specific version of an Operator.

GitOps platform for managing and automating containerized application deployments, streamlining CD workflows.

Techniques to reduce initialization time of containers, particularly important in serverless environments.

Design pattern for handling failures in distributed container-based systems by reversing or compensating actions.

Kubernetes API object used to store non-confidential data in key-value pairs, accessible by pods.

Service mesh feature of Consul providing secure service-to-service communication with automatic TLS encryption.

Standardized unit of software that packages code and all its dependencies for quick, reliable deployment.

Methods used by attackers to escape container isolation and access the host system, posing security risks.

Software responsible for managing container lifecycle, including creation, execution, and destruction.

Security vulnerability allowing a process to break out of container isolation and access the host system.

Security measures and best practices to prevent containers from breaking out of their isolation.

Weaknesses in container runtimes or configurations that could allow processes to escape isolation.

Mechanisms to verify the health and readiness of containerized applications, ensuring service availability.

Physical or virtual machine running the container runtime and hosting containers, providing the execution environment.

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

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

Stages a container goes through from creation to termination, including running, paused, and stopped states.

Processes and tools for managing containers throughout their lifecycle, from creation to deletion.

Process of capturing and managing log output from containerized applications for monitoring and troubleshooting.

Configuration file specifying how to build a container image, including base image, commands, and environment setup.

Quantitative measures of container performance, resource usage, and health, used for monitoring and optimization.

Specification and libraries for configuring network interfaces in Linux containers, standardizing networking.

Standard for developing plugins to configure network interfaces in Linux containers, ensuring interoperability.

Architecture and principles governing how containers communicate within and across hosts in orchestrated environments.

Automated arrangement, coordination, and management of software containers, crucial in large-scale deployments.

Kubernetes mechanisms (liveness, readiness, startup) for checking container health and controlling lifecycle.

Technique for capturing and analyzing real user interactions with containerized applications.

Rules defining how containers should be restarted in case of failures or exits, enhancing application resilience.

Software responsible for running containers, managing their lifecycle from creation to deletion.

Measures and tools to protect containers during execution, preventing unauthorized access or malicious activities.

Lightweight processes that manage the lifecycle of containers and provide abstraction between runtimes.

Standardized interface for container orchestration platforms to interact with storage systems.

Standard for developing storage plugins for container orchestration platforms, enabling storage extensibility.

Process of identifying security vulnerabilities in container images and their dependencies.

Mechanism for verifying the integrity and authenticity of container images before deployment.

Automated process of releasing container images to production environments after passing tests.

Ongoing process of validating deployed containers against defined policies and expectations.

Container networking solution using VPP (Vector Packet Processing) for optimized performance.

Approach prioritizing API definition before implementation, crucial for microservices architecture.

Linux kernel feature for limiting, prioritizing, and isolating resource usage of container groups.

Components managing the overall state of a container cluster, including API server, scheduler, and controllers.

Process of updating the core components of a container orchestration system while maintaining cluster stability.

Kubernetes component running controller processes to regulate the state of the cluster, maintaining desired state.

Software framework for building Kubernetes controllers and operators, simplifying custom resource management.

Kubernetes feature allowing custom resource API version conversions via external webhooks.

Storage optimization technique used in container filesystems to reduce duplication and improve efficiency.

Process of marking a node as unschedulable in Kubernetes, preventing new pods from being assigned to it.

Flexible, extensible DNS server used for service discovery in Kubernetes clusters, replacing kube-dns.

Horizontally scalable, multi-tenant, long-term storage for Prometheus metrics, enhancing monitoring capabilities.

Use of Cortex to provide isolated Prometheus monitoring for multiple users or teams in shared environments.

Kubernetes object for creating time-based jobs, running containers on a schedule for automated tasks.

Kubernetes patterns for running batch processes and scheduled tasks in containerized environments.

Kubernetes operators capable of managing resources across multiple namespaces, enabling broader automation.

Kubernetes API extension allowing pods to consume custom metrics for autoscaling and monitoring.

Extension of the Kubernetes API that defines new, custom resource types specific to a cluster's needs.

Process of converting custom resources between different API versions in Kubernetes, enabling API evolution.

Kubernetes API extension allowing the definition of custom resources, enabling cluster customization.

Kubernetes feature for extending the API with custom resources, enabling cluster customization.

User-defined API objects in Kubernetes, extending the core functionality of the platform for specific use cases.

Alternative or additional scheduler implementation for Kubernetes, allowing custom pod placement logic.

Alternative scheduling implementations in Kubernetes for custom pod placement strategies, enabling specialized scheduling logic.

Kubernetes feature providing DNS-based service discovery for containers within the cluster.

Use of Data Plane Development Kit for high-performance packet processing in container networks.

Kubernetes object ensuring that all (or some) nodes run a copy of a specific pod, useful for cluster-wide services.