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.

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 component allowing plugins to customize the scheduling process without changing the core scheduler code.

Modular components in Kubernetes scheduler implementing specific scheduling behaviors and policies.

Configurations defining sets of scheduling plugins to be used for different scheduling scenarios in Kubernetes.

Use of Berkeley Packet Filter programs to define fine-grained seccomp profiles for container security.

Security profiles defining allowed system calls for containers, enhancing isolation and reducing attack surface.

Kubernetes object for storing and managing sensitive information such as passwords, tokens, or keys.

Process of securely storing, distributing, and rotating sensitive data in containerized environments.

Linux kernel feature limiting the system calls a process can make, often used to secure containers.

Practices ensuring the integrity and security of container images and their dependencies throughout the development lifecycle.

OpenShift object defining a set of conditions that a pod must run with to be accepted into the system.

OpenShift feature for fine-grained control over the actions containers can perform and the resources they can access.

Integration of vulnerability and compliance scans into the continuous integration process for container images.

Tools and platforms for building and deploying serverless applications on container orchestration systems.

Techniques and tools for monitoring, tracing, and debugging serverless functions and applications.

Architectural patterns for designing and implementing serverless applications in containerized environments.

Practices and tools for securing serverless functions and applications in container-based infrastructures.

Orchestration of multiple serverless functions to create complex, event-driven applications.

Kubernetes abstraction defining a logical set of pods and a policy by which to access them.

Kubernetes feature for securely delivering service account tokens to pods, enhancing security.

Credentials automatically generated for pods to authenticate with the Kubernetes API server.

Technique allowing services to locate and communicate with each other dynamically in containerized environments.

Component of a service mesh responsible for managing and configuring the data plane proxies.

Component of a service mesh consisting of proxies that intercept and control service-to-service communication.

Specification defining a set of common APIs for service meshes on Kubernetes, promoting interoperability.

Architectural pattern providing a dedicated infrastructure layer for handling service-to-service communication.

Standard for measuring and characterizing the performance of service mesh deployments, aiding in optimization.

Custom resources in Prometheus Operator for declaratively defining how services should be monitored.

Mechanisms and configurations for enabling network communication between services in container orchestration platforms.

Kubernetes feature allowing services to route traffic based on node topology for improved performance.

Process of verifying the identity of services communicating with each other in a containerized environment.

Kubernetes object providing an identity for processes running in a pod, used for authentication and authorization.

Technique of dividing data across multiple database instances in containerized environments.

Additional containers in a pod that enhance or extend the functionality of the main container.

Design pattern where a sidecar container is deployed alongside the main application container to provide supporting features.

Proxy container deployed alongside application containers to handle network traffic and implement service mesh functionality.

Use of sidecar proxies to implement service mesh features like traffic management and security in Kubernetes.

Open-source application performance monitoring and observability platform for microservices and Kubernetes environments.

Tool facilitating continuous development for Kubernetes applications, handling building, pushing, and deploying application code.

Use of Skaffold to streamline the development workflow for Kubernetes applications in local environments.

Platform providing vulnerability scanning and security management for container images and Kubernetes deployments.

Approach to network management enabling dynamic, programmatically efficient network configuration in container environments.

Tool and workflow for building reproducible container images from source code, simplifying the build process.

Open-source, multi-cloud continuous delivery platform for releasing software changes with high velocity and confidence.

Integration of Splunk's data platform with containerized environments for logging, monitoring, and analysis.

Security platform providing vulnerability management, compliance, and runtime protection for Kubernetes.

Containerized applications that maintain data or state, requiring special considerations for persistence and scaling.

Kubernetes workload API object used to manage stateful applications, providing unique network identities and stable storage.

Design patterns for deploying and managing stateful applications in Kubernetes using StatefulSets.

Pods managed directly by the kubelet on a specific node, without the API server observing them.

Process of pre-creating storage resources in Kubernetes before they are claimed by persistent volume claims.

File-based method for providing authentication tokens to the Kubernetes API server, useful for bootstrap authentication.

Kubernetes API feature allowing updates to an object's status without modifying its spec, enabling more granular updates.

Kubernetes feature for making scheduling decisions based on the available storage capacity of nodes.

Kubernetes objects defining different classes of storage with varying performance characteristics or policies.

Kubernetes object describing the "classes" of storage offered by the administrator, used for dynamic provisioning.

Software-defined storage for cloud-native applications, providing persistent storage for containers.

Incremental approach to modernizing legacy applications by gradually replacing components with microservices.

Network protocols optimized for real-time data transmission in containerized environments.

Kubernetes volume feature allowing mounting a specific directory of a volume instead of its root.

Open-source project enabling direct networking between pods and services across Kubernetes clusters.

Kubernetes API feature representing sub-components of a resource, allowing more granular access control and operations.

Open-source container management system for edge computing, extending Kubernetes to manage edge nodes efficiently.

Security platform providing real-time threat detection and response for containerized environments.

Specification for securing software update systems, applicable to container image distribution.

Kubernetes node attributes that repel certain pods from scheduling unless they have matching tolerations.

Kubernetes mechanism for controlling which pods can be scheduled on specific nodes, enabling node specialization.

Cloud-native CI/CD framework for Kubernetes, providing custom resources for defining pipelines and tasks.

Kubernetes-native CI/CD solution using custom resources to define pipelines as a series of tasks.

Tool allowing developers to run a single service locally while connecting to a remote Kubernetes cluster.

Platform providing vulnerability management and compliance monitoring for container images and registries.

Techniques for separating and securing resources belonging to different users or groups in multi-tenant Kubernetes clusters.

Open-source tool for static code analysis of Infrastructure as Code, including Kubernetes manifests.

Open-source project extending Prometheus for long-term storage and global query view in large-scale deployments.

Use of Thanos to provide durable storage and global query capability for Prometheus metrics.

Technique for limiting the rate at which a containerized service processes requests to prevent overload.

Development environment as code for teams building Kubernetes microservices, streamlining local development.

Settings controlling the maximum duration for various operations in containerized applications and infrastructure.

Kubernetes pod specifications that allow (but do not require) pods to be scheduled on nodes with matching taints.

Kubernetes feature coordinating resource allocation decisions to provide topology-aligned resource allocations.

Technique of duplicating live traffic to a separate service instance for testing or debugging purposes.

Gradual redirection of traffic from one version of a service to another, often used in canary deployments.

Distribution of incoming requests across multiple versions of a service, enabling advanced deployment strategies.

Open-source security scanner for containers and other artifacts, finding vulnerabilities and misconfigurations.

Set of best practices for building software-as-a-service apps that can be easily deployed to the cloud.

Container security platform providing vulnerability management, compliance, and runtime protection (now part of Prisma Cloud).

Comprehensive cloud-native security platform for containers, serverless, and cloud environments.

Physical network infrastructure upon which overlay networks for container communication are built.

Specialized, single-purpose machine images offering potential advantages in size and security over containers.

Filesystem service for Linux allowing files and directories of separate filesystems to be overlaid, forming a single coherent filesystem.

Kubernetes feature allowing a user to act as another user, useful for debugging and testing access controls.

Linux kernel feature providing per-process isolated instances of user and group ID number spaces.

Network virtualization technology used in container networking for creating overlay networks.

Virtual networks using VXLAN encapsulation to enable container communication across hosts.

Design pattern for providing clients with restricted direct access to a specific resource or service.

Admission controller webhooks in Kubernetes that validate resource requests without modifying them.

Kubernetes admission control mechanism for validating API requests against custom logic before persistence.

Open-source tool for backing up and restoring Kubernetes cluster resources and persistent volumes.

Guidelines defining supported version differences between Kubernetes components to ensure compatibility.

Kubernetes component that automatically adjusts the CPU and memory reservations for pods to optimize resource utilization.

Resource utilization data used by VPA to make decisions on adjusting pod resource allocations.