Engineering Glossary

A Git reference with a specific meaning or function, such as HEAD or FETCH_HEAD.

A deployment environment used for testing changes before releasing to production in Git-based workflows.

A feature on Git hosting platforms allowing users to bookmark repositories of interest.

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.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Comparison of two popular log processors for containerized environments, with Fluent Bit being more lightweight.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

Customizable visualization panels for monitoring containerized environments and applications.

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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

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