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.

High-density memory architecture using vertically stacked chips, enhancing performance and capacity in cloud server hardware.

Cloud infrastructure optimized for 5G networks, supporting high-speed, low-latency services and applications.

Technique to create multiple virtual networks on a shared physical 5G infrastructure, each optimized for specific use cases.

Integration of 5G networks with edge computing to enable low-latency, high-bandwidth applications closer to end-users.

Future convergence of 6G networks with cloud computing, promising ultra-high speeds and advanced capabilities.

Database transaction properties (Atomicity, Consistency, Isolation, Durability) ensuring data integrity in cloud environments.

Software ensuring AI systems adhere to ethical guidelines and regulations in cloud deployments.

Software for identifying and mitigating ethical issues and biases in AI models deployed in cloud environments.

Solutions for managing ethical considerations and regulatory compliance in cloud-based AI systems.

Structured approaches for managing AI development, deployment, and use in cloud environments.

Comprehensive solutions for managing, monitoring, and controlling AI models in cloud-based systems.

Cloud-based tools for explaining and understanding the decision-making processes of AI models.

Software for analyzing and explaining AI model decisions, crucial for transparency in cloud AI services.

Cloud platforms for discovering, sharing, and monetizing pre-trained AI models and algorithms.

Tools for tracking AI model performance and identifying deviations from expected behavior in cloud environments.

Systems for managing different versions of AI models and enforcing governance policies in cloud deployments.

Cloud-based development environments that use AI to assist programmers in writing and optimizing code.

Integration of AI capabilities into data analytics processes in cloud environments for enhanced insights.

Use of AI algorithms to predict and optimize resource allocation in cloud infrastructures.

Application of AI techniques to improve efficiency, performance, and cost-effectiveness of cloud resources.

Automated distribution of cloud resources using AI to optimize performance and cost-efficiency.

AI-based automation of cloud service selection and integration for complex workflows.

Automated creation of source code using AI models, often integrated into cloud development platforms.

Automated categorization and labeling of data using AI algorithms in cloud storage and processing systems.

Use of AI to improve network performance, efficiency, and security in cloud environments.

Intelligent distribution of computing resources in cloud environments using AI algorithms.

Enhanced SIEM systems using AI for improved threat detection and response in cloud environments.

Use of AI algorithms to identify and analyze potential security threats in cloud systems.

Proactive search for hidden threats in cloud environments using AI-powered analytics and automation.

Cloud infrastructure components designed or configured to enhance AI workload performance.

Database systems tailored for AI workloads, often featuring in-memory processing and distributed architectures.

Use of AI algorithms to identify unusual patterns or behaviors in cloud systems and data.

Intelligent automation of data and application integration processes in cloud environments using AI.

Cloud-based offering of AI capabilities, allowing businesses to leverage AI without extensive in-house expertise.

Cloud-based AI capabilities offered as a service, enabling easy integration of AI into applications.

Application of AI for automating and enhancing IT operations management in cloud environments.

Integrated solutions leveraging AI for automated monitoring, analysis, and management of cloud IT operations.

Use of AI-driven analytics to forecast and prevent system failures in cloud infrastructure.

Service that protects APIs in cloud environments by managing access, monitoring traffic, and preventing attacks.

Dedicated services for securing and managing APIs in cloud environments, including access control and threat protection.

Design approach prioritizing API creation before implementation, common in cloud-native application development.

Integration strategy using purpose-built APIs to connect data, devices, and applications in cloud ecosystems.

Persistent 3D digital content overlaid on the physical world, accessible via cloud infrastructure for augmented reality applications.

Cloud-based tools for analyzing user behavior and performance in augmented and virtual reality environments.

Cloud-hosted services enabling multi-user interaction in shared augmented or virtual reality spaces.

Specialized CDN optimized for delivering AR/VR content with low latency and high bandwidth.

Cloud-based tools and services for creating, testing, and deploying augmented and virtual reality applications.

JSON-based files defining infrastructure and configuration for Azure resource deployment.

Dynamic UIs that adjust based on user behavior, device capabilities, and context in cloud applications.

Techniques to identify and mitigate attacks on ML models in cloud-based AI systems.

Systems for informing administrators or users about important events or issues in cloud environments.

Process of examining AI algorithms for bias, errors, or unintended consequences in cloud-based systems.

Seamless, context-aware interaction methods for accessing cloud services in IoT environments.

Tools for identifying unusual patterns or behaviors in cloud systems, often using machine learning techniques.

Network management approach that optimizes performance based on specific application requirements in cloud environments.

Technique for quickly estimating query results in large-scale cloud databases, trading accuracy for speed.

Low-cost cloud storage for infrequently accessed data with longer retrieval times, e.g., Amazon Glacier, Azure Archive Storage.

Cloud-based storage for software build outputs, dependencies, and related metadata.

Hypothetical cloud offering of human-level AI capabilities across various domains.

Cloud-based environments for developing and testing advanced AI systems approaching human-level intelligence.

Systematic recording of actions and events in cloud systems for security and compliance purposes.

Chronological records of system activities for reconstructing and examining the sequence of events in cloud environments.

Integration of machine learning and natural language processing in cloud-based data analytics workflows.

Shared, persistent digital content overlaid on the physical world, accessible via cloud infrastructure.

Cloud-based systems for managing and delivering AR content and experiences, enabling scalable AR applications.

Cloud-powered generation of AR graphics and content, offloading processing from end-user devices.

Collections of EC2 instances that automatically adjust capacity based on defined conditions.

Automatic adjustment of cloud resources to match workload demands, ensuring optimal performance and cost-efficiency.

Automated machine learning processes for model selection, hyperparameter tuning, and feature engineering in cloud environments.

Cloud-based services automating the machine learning pipeline from data preparation to model deployment and monitoring.

AI-driven tools for improving efficiency and performance of machine learning workflows in the cloud.

Systems that automatically implement and maintain cloud resource policies and compliance.

Continuous, AI-driven assessment of cloud systems against regulatory and security standards.

AI-powered tools for identifying, categorizing, and mapping data assets in cloud environments.

AI-driven systems for managing data quality, security, and compliance in cloud environments.

Cloud-based tools using AI to clean, transform, and prepare data for analysis, streamlining data preparation processes.

AI-powered generation and selection of features for machine learning models in cloud environments.

AI-driven coordination of security incident detection and resolution in cloud systems, automating response workflows.

Cloud services that automate the process of creating and optimizing machine learning models, from data prep to deployment.

Cloud-based systems that automate the end-to-end machine learning model development process, including feature engineering and model selection.

AI-driven tools for simulating cyberattacks to identify vulnerabilities in cloud systems and applications.

AI-powered analysis of cloud architectures to identify potential security risks and attack vectors.

Self-managing, self-healing cloud services requiring minimal human intervention, leveraging AI for operations.

Ultra-low latency edge computing extensions of Azure for 5G networks, bringing cloud resources closer to users.

Service for creating, assigning, and managing policies to control Azure resources and ensure compliance.

Cloud-based services facilitating data exchange and process integration between business partners.

Architectural approach creating backend services tailored to specific frontend application needs in cloud environments.

Physical server dedicated to a single tenant, offering direct hardware access in cloud environments.

Dedicated physical servers without virtualization, offered as a cloud service for high-performance workloads.

Execution of series of jobs without user interaction, often for large-scale data processing in the cloud (e.g., AWS Batch, Azure Batch).

Distributed computing environments for processing and analyzing massive datasets in the cloud.

Interface for monitoring and managing cloud service usage and costs, providing detailed breakdowns and forecasts.

Use of cloud resources for computational biology and genomics research, enabling large-scale analysis and modeling.

Evaluation of cloud data center effects on local ecosystems and wildlife to ensure sustainable operations.

Cloud storage that provides fixed-size raw storage volumes, typically used for databases or file systems.

Cloud services providing persistent block-level storage volumes for use with compute instances.

Cloud-based tools for analyzing and visualizing blockchain data and transactions, offering insights into network activity.

Distributed databases using blockchain technology for enhanced security and immutability in cloud environments.

Software for managing and enforcing rules, permissions, and consensus mechanisms in blockchain networks deployed on cloud platforms.

Cloud-based solutions for connecting blockchain networks with existing enterprise systems and applications.

Kubernetes scheduling constraint that allows pods to be co-located with other pods based on labels.

Kubernetes features for influencing pod scheduling based on the topology of the cluster and other pods.

Kubernetes scheduling constraint that prevents pods from being co-located with other pods based on labels.

Kubernetes object that limits the number of pods of a replicated application that can be down simultaneously.

Stages a pod goes through from creation to termination, including pending, running, succeeded, failed, and unknown phases.

Custom resources in Prometheus Operator for declaratively defining how groups of pods should be monitored.

Mechanisms and configurations for enabling network communication between pods in a Kubernetes cluster.

Additional resources consumed by pod infrastructure on top of the container requests and limits.

High-level summary of where a pod is in its lifecycle, used for pod status reporting and monitoring.

Kubernetes feature allowing importance-based scheduling and eviction of pods when resources are constrained.

Kubernetes object defining privilege and access control settings for a pod or container, enhancing security.

Deprecated Kubernetes feature for cluster-level control over pod security sensitive aspects.

Kubernetes-defined levels (privileged, baseline, restricted) of security settings for pods.

Kubernetes object limiting the number of pods that can be down simultaneously for a given application.

Isolation boundary for one or more containers in a pod, providing shared kernel namespaces and cgroups.

Deprecated cluster-level resource controlling security-sensitive aspects of pod specification.

Tool for managing containers and images without a container daemon, compatible with Docker CLI.

Open-source tool for checking Kubernetes resources against configurable best practices, enhancing reliability.

Use of multiple data storage technologies in microservices architectures to best fit varying data storage needs.

Network address translation technique used in container networking for mapping ports, enabling external access.

Process of forwarding a network port from the host to a container, enabling external access to containerized services.

Web-based management UI for Docker environments, simplifying container operations and monitoring.

Cloud-native storage platform providing persistent storage for containerized applications in multi-cloud environments.

Software-defined storage solution by Portworx for containerized stateful applications in Kubernetes.

Kubernetes scheduling policies that filter out nodes that cannot run a pod, based on resource requirements and constraints.

Kubernetes process of evicting lower-priority pods to schedule higher-priority pods when resources are constrained.

CustomResourceDefinition feature for defining custom columns in kubectl output for custom resources.

Kubernetes scheduling policies that rank nodes that have passed the predicate phase, determining the best placement for a pod.

Kubernetes objects defining the relative importance of pods, used in scheduling and preemption decisions.

Kubernetes feature allowing pods to be assigned different priorities, influencing scheduling and eviction decisions.

Containers running with elevated permissions, potentially accessing host resources and devices.

Deployment strategy gradually rolling out changes to a subset of users or servers, monitoring for issues.

High-performance networking mode in Calico using extended Berkeley Packet Filter for packet processing.

Kubernetes volume type that maps several existing volume sources into the same directory, combining multiple configs.

Open-source systems monitoring and alerting toolkit, widely used for Kubernetes and container monitoring.

Component of Prometheus handling alerts, including silencing, inhibition, aggregation, and notifications.

Kubernetes operator that makes it easy to deploy and manage Prometheus instances, simplifying monitoring setup.

Component allowing ephemeral and batch jobs to expose their metrics to Prometheus, enhancing monitoring coverage.

Feature allowing Prometheus to send samples to compatible remote storage systems, enabling long-term storage.

Definitions for recording rules and alerting rules in Prometheus, allowing for complex queries and alert conditions.

Process of choosing the appropriate network protocol for container communication based on requirements.

Kubernetes classifications (Guaranteed, Burstable, BestEffort) for pods based on resource requirements and limits.

Platform providing vulnerability management, policy compliance, and runtime defense for containers.

Container registry by Red Hat offering advanced features like vulnerability scanning and fine-grained access controls.

Kubernetes authorization mechanism for fine-grained control over the actions users can perform.

Guidelines for implementing secure and manageable role-based access control in Kubernetes environments.

Use of role-based access control to implement isolation and access management in multi-tenant Kubernetes clusters.

Integration of Remote Direct Memory Access for high-performance, low-latency container networking.

Platform for managing multiple Kubernetes clusters across different cloud providers and on-premises.

Technique to control the rate of requests or traffic in distributed systems, often implemented in API gateways.

Kubernetes volume type providing direct access to a block device for applications requiring this capability.

Architectural approach emphasizing responsiveness, resilience, elasticity, and message-driven communication in distributed systems.

Security practice of mounting a container's root filesystem as read-only to prevent runtime modifications.

Kubernetes volumes mounted with read-only access, enhancing security by preventing modifications.

Service for storing and distributing container images, crucial for managing and deploying containerized applications.

HTTP API provided by container registries for image management, authentication, and metadata retrieval.

Process of verifying the identity of clients making requests to containerized services or APIs.

Technique of locally caching container images to improve pull times and reduce network usage.

Process of replicating container images across multiple registries for improved availability and reduced latency.

Mechanism for registries to send notifications about events such as image pushes, pulls, or deletions.

Scheduled, periodic releases of software components, often used in microservices architectures for coordinated updates.

Kubernetes controller ensuring a specified number of pod replicas are running at any given time.

Process of verifying the identity of clients making requests to containerized services or APIs.

Technique of duplicating incoming requests to a separate service instance for testing or debugging purposes.

Scheduling strategy aiming to consolidate workloads onto a minimal number of nodes for efficient resource utilization.

Constraints set on the amount of compute resources (CPU, memory) a container can consume, preventing resource hogging.

Kubernetes objects for limiting aggregate resource consumption per namespace, ensuring fair resource allocation.

Specifications of the minimum amount of compute resources required by a container, aiding in scheduling decisions.

Process of optimizing resource allocations for containers to balance performance and efficiency.

Kubernetes object providing constraints that limit aggregate resource consumption per namespace.

Limits on the number of retry attempts for failed operations in distributed systems to prevent cascading failures.

Implementation of automatic reattempts for failed operations in microservices to improve system resilience.

Design pattern for handling transient failures in distributed systems by automatically repeating failed operations.

Kubernetes RBAC object defining a set of permissions within a particular namespace, controlling access to resources.

Kubernetes RBAC object that binds a role to a set of users or service accounts within a namespace.

Kubernetes objects associating Roles and ClusterRoles with users, groups, or service accounts.

Kubernetes RBAC objects defining sets of permissions at namespace or cluster level respectively.

Process of reverting a deployment to a previous version in case of issues with the current version.

Deployment strategy where new versions of an application are gradually rolled out by replacing instances incrementally.

Networking solution for Kubernetes providing native Layer 3 networking without overlays for improved performance.

Open-source cloud-native storage orchestrator for Kubernetes, providing the platform, framework, and support for storage solutions.

Kubernetes operator for file, block, and object storage systems, automating deployment and management of storage services.

Containers running without root privileges, enhancing security by reducing the potential impact of container breakouts.

Lightweight, portable container runtime adhering to the OCI specification, used as a low-level runtime by container engines.

Inner workings and components of the RunC container runtime, including its interaction with Linux kernel features.

Security technology that detects and prevents real-time attacks on containerized applications.

Measures and tools for protecting containerized applications during execution, including threat detection and prevention.

Process of identifying and alerting on security threats to running containers and orchestration platforms.

CRI component responsible for managing the entire lifecycle of a container, including creation, execution, and deletion.

Security policies enforcing mandatory access controls on containers and pods in Linux-based systems.

Monitoring approach focusing on Service Level Objectives to ensure application performance and reliability.

Standard specification for service meshes on Kubernetes, providing a common interface for various implementations.

Standards and implementation for securely identifying and authenticating workloads in dynamic environments.

Use of Single Root I/O Virtualization to provide high-performance networking for containers.

Design pattern for managing data consistency across microservices in distributed transactions.

Implementation of the Saga pattern to maintain data consistency in microservices architectures.

Container runtimes providing additional isolation between the container and the host system for enhanced security.

Techniques for isolating running containers from the host system and each other to enhance security.

Kubernetes API for directly managing the number of replicas of a workload without modifying the entire object.

Kubernetes component responsible for assigning pods to nodes based on resource requirements, policies, and constraints.