Engineering Glossary

A single set of stashed changes in Git, which can be reapplied later to the working directory.

Automated tests or processes that run when changes are proposed in a Git repository, ensuring code quality and compatibility.

Notifications or updates a user receives about activity in Git repositories they're interested in or contributing to.

The main Git repository that contains submodules, managing references to specific versions of nested repositories.

A symbolic reference in Git that points to another reference, most commonly used for HEAD pointing to the current branch.

A Git object containing metadata about a tag, including the tagger, date, and optional message.

A group of users on a Git platform with shared access permissions to repositories within an organization.

A user with administrative privileges for managing a team's membership and access rights in a Git organization.

A chronological display of events and activities in a Git repository or user profile on hosting platforms.

A short-lived Git branch created to develop a specific feature or fix a particular issue.

Keywords or categories assigned to Git repositories to help users discover related projects on hosting platforms.

A visual representation of visitor activity and clone statistics for a Git repository on hosting platforms.

The process of moving Git objects between repositories during push, fetch, or clone operations.

A Git object representing a directory structure, containing references to blobs (files) and other trees (subdirectories).

A Git object that stores the hierarchy of files and directories in a repository at a specific point in time.

A Git term referring to an object that resolves to a tree, such as a commit, tag, or tree.

A state of a Git branch that has no commits yet, typically seen when initializing a new repository.

The state of the Git index containing conflicting changes from different branches during a merge operation.

A Git object not accessible from any reference, potentially subject to garbage collection if not recovered.

The remote branch that a local branch is set to track, used as a reference for pull and push operations.

An individual account on a Git platform, associated with personal repositories and contributions.

An authenticated request from a Git client to a server, typically for operations like push or fetch.

The unique identifier for a user account on Git platforms, used for authentication and mention notifications.

A team in a Git organization that is visible to all members of the organization, as opposed to secret teams.

A feature allowing users to receive notifications about activity in Git repositories they're interested in.

Alerts received for all notable events in a watched Git repository, including issues, pull requests, and releases.

Alerts displayed on Git platforms' web interfaces, notifying users of relevant activity or mentions.

The directory on your local machine where you modify files before staging and committing them in Git.

The set of files and directories in your project that are currently checked out and available for editing.

A Git feature allowing multiple working directories to be associated with a single repository.

Permission to make changes to a Git repository, including pushing commits and modifying branches.

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.