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.

Process of teaching machine learning algorithms to make predictions using data in cloud environments.

Managing different iterations of machine learning models in cloud-based development pipelines.

Repository for storing and managing reusable software components in cloud development environments.

Experimental technique using molecules to store digital data, potentially integrated with cloud systems.

Tools and practices for managing multiple cloud accounts within an organization.

Using services from multiple cloud providers in a single architecture, optimizing for cost, performance, and redundancy.

Centralized platforms for managing data and application integrations across multiple cloud providers.

Distributing workloads across multiple cloud providers to optimize performance and reliability.

Tools and practices for administering resources and applications across multiple cloud providers.

Technologies enabling seamless networking across multiple cloud provider environments.

Coordinating microservices communication and security across multiple cloud environments.

Security system requiring two or more verification methods to access cloud resources.

Database systems supporting multiple data models (e.g., document, graph, relational) in a single backend.

Copying data across multiple geographic regions in cloud environments for redundancy and disaster recovery.

Bringing cloud capabilities closer to the network edge for improved performance and reduced latency.

Tools and strategies for monitoring and optimizing expenses across multiple cloud providers.

Strategies to minimize expenses and maximize efficiency when using multiple cloud providers.

Implementing backup and failover systems across multiple cloud providers for enhanced resilience.

Connecting and managing network resources across multiple cloud provider environments.

Strategies and tools for protecting data and applications across multiple cloud provider environments.

Tools for managing and orchestrating data across multiple cloud provider environments.

Unified network architecture spanning multiple cloud providers for seamless connectivity.

Cloud services supporting AI models that can process and integrate multiple types of data (e.g., text, image, audio).

User interfaces that combine multiple input/output methods (e.g., voice, touch, gesture) for cloud service interaction.

Cloud services for integrating and analyzing data from various sources and formats.

Service that enables private subnet instances to connect to the internet or other AWS services.

Isolated environment within a cluster to organize and manage resources in cloud platforms.

Systems allowing users to interact with cloud services using conversational language.

AI technology for understanding, interpreting, and generating human language in cloud environments.

Cloud-based platforms offering pre-built NLP models and APIs for text analysis and generation.

Smaller data centers located closer to end-users for reduced latency in cloud service delivery.

UI design approach mimicking physical objects for intuitive cloud service interaction.

Rules controlling inbound and outbound traffic for subnets in cloud virtual networks.

Stateless firewall for controlling traffic in and out of cloud subnets, enhancing network security.

Process of modifying network address information to route traffic between private and public networks.

Virtualizing network services traditionally run on proprietary hardware in cloud environments.

Tools for monitoring, analyzing, and troubleshooting cloud network performance and behavior.

Tools and practices for measuring and optimizing network performance in cloud environments.

Technology for managing and securing service-to-service communication in cloud-native environments.

Collecting and analyzing network data for visibility and troubleshooting in cloud environments.

Techniques for improving the efficiency and performance of data flow in cloud networks.

Defining and managing network infrastructure using code in cloud environments, enabling automated and version-controlled networking.

Cloud-based automated design of optimal neural network architectures for specific AI tasks.

Cloud platforms combining neural networks with symbolic reasoning for more robust AI systems.

Cloud-based AI integrating neural networks and symbolic AI for enhanced reasoning capabilities.

Cloud services using brain-inspired computing architectures for improved efficiency in AI tasks.

Cloud platforms providing access to brain-inspired computing architectures for AI applications.

Cloud-based offerings of brain-inspired computing architectures for specialized AI workloads.

Cloud-based AI processing using brain-inspired chip designs for improved efficiency and performance.

Cloud databases combining the scalability of NoSQL with the ACID guarantees of traditional relational databases.

Non-relational databases designed for flexible, scalable data storage and retrieval in cloud environments.

High-performance storage protocol for accessing SSDs over network fabrics in cloud environments.

Technology for quickly analyzing multidimensional data from multiple perspectives in cloud databases.

Cloud storage for unstructured data, accessed via APIs and organized as objects rather than files.

Scalable cloud storage services for unstructured data, accessible via APIs, ideal for large-scale data storage and retrieval.

Devices or software providing local access to cloud-based object storage systems.

Centralized repositories for storing and analyzing large volumes of observability data in cloud environments.

Systems for collecting, processing, and routing observability data in cloud environments.

Software development approach prioritizing built-in monitoring and debugging in cloud applications.

Analyzing and interpreting scent-related data using cloud computing resources, enabling digital olfactory applications.

Running ML models on edge devices, reducing reliance on cloud processing for certain tasks.

Technology for rapid analysis of multidimensional data in cloud-based data warehouses.

Open-source framework for building serverless functions with Docker and Kubernetes in cloud environments.

Kubernetes extension for automating application management tasks in cloud-native environments.

Using light-based processing for high-speed, energy-efficient computation in cloud data centers.

Remote software updates for devices connected to cloud services, often used for IoT.

Adhering to Payment Card Industry Data Security Standard requirements in cloud environments.

Dividing large datasets or databases into smaller, more manageable parts in cloud storage systems.

Pricing structure where users only pay for the cloud resources they actually use.

Billing model where customers are charged based on their actual usage of cloud resources.

Pricing scheme where users are billed only when their serverless functions are invoked.

Direct network link between two virtual private clouds, often in different accounts or regions.

Simulated cyberattacks to evaluate the security of cloud-based systems and applications.

Non-volatile memory that combines speed of DRAM with data persistence, used in cloud storage systems.

Storage resources in cloud environments that exist independently of any specific pod or container.

Light-based chips for high-speed data processing in cloud infrastructure, improving energy efficiency.

Defining and managing continuous integration/deployment workflows using code in cloud environments.

Ability to restore cloud-based data to a specific moment in the past, enabling granular data recovery options.

Implementing and maintaining security and compliance rules across cloud resources.

Creating, updating, and enforcing rules governing cloud resource usage and security.

Serverless platform supporting multiple programming languages for cloud function development.

Using multiple data storage technologies to handle varied data storage needs in cloud applications.

Creating serverless applications using multiple programming languages in cloud environments.

Refining and improving cloud-based systems after initial migration from on-premises.

Deploying encryption methods resistant to quantum computer attacks in cloud systems.

Metric for measuring energy efficiency of cloud data centers, comparing total power consumption to IT equipment power usage.

Using AI to anticipate resource needs and adjust cloud capacity automatically, optimizing performance and cost-efficiency.

Using data analytics and machine learning to forecast future cloud resource requirements and optimize infrastructure.

Using data analytics to anticipate and prevent equipment failures in cloud infrastructure.

Advanced analytics providing recommendations for actions in cloud-based decision making.

Tool for estimating costs of cloud services based on expected usage, helping in budgeting and resource planning.

Techniques for processing sensitive data while preserving privacy in cloud environments.

Tools and methods for protecting personal data privacy in cloud computing, ensuring compliance and user trust.

Techniques for analyzing data in the cloud while maintaining individual privacy.

Extracting useful information from large datasets without compromising individual privacy in the cloud.

Performing data analysis in the cloud while protecting sensitive information, balancing insights with data privacy.

Dedicated 5G networks deployed for specific enterprise use, often integrated with cloud services.

Service enabling private connectivity between VPCs, AWS services, and on-premises applications.

Tools for securing, controlling, and monitoring access to critical cloud resources and data.

Memory-efficient data structures for approximate data analysis in cloud environments.

Design pattern using specialized containers that run before app containers in a pod, used for setup or dependency management.

Specialized containers that run to completion before other containers in a Kubernetes pod, used for initialization tasks.

Specification of CPU and memory limits for initialization containers in Kubernetes pods, ensuring efficient resource utilization.

Deprecated Kubernetes feature for dynamically modifying resource configurations before they become visible to the system.

Methods and patterns for containers within a pod to exchange data and signals, utilizing shared volumes or localhost networking.

Architectural designs for deploying and managing containerized applications on edge devices in Internet of Things scenarios.

Open-source service mesh providing traffic management, security, and observability for cloud-native applications.

Core services of the Istio service mesh responsible for configuring proxies and implementing policies.

Istio traffic management resource defining policies that apply to traffic intended for a service after routing has occurred.

Istio resources managing inbound and outbound traffic for the service mesh, providing fine-grained control over traffic flow.

Istio design that removes the Mixer component, improving performance and scalability of the service mesh.

Istio resources allowing the addition of entries to the service registry, enabling traffic to external dependencies.

Istio traffic management resources defining a set of traffic routing rules to apply when a host is addressed.

Istio feature providing a sidecar-less service mesh implementation, reducing resource overhead and simplifying deployment.

Open-source, end-to-end distributed tracing system for monitoring and troubleshooting microservices-based distributed systems.

Kubernetes operator for automated deployment and management of Jaeger tracing infrastructure in container environments.

Implementation of distributed tracing using Jaeger to monitor and optimize performance in microservices architectures.

Cloud-native CI/CD solution for Kubernetes, automating the entire software development lifecycle for containerized applications.

Tool for building optimized Docker and OCI images for Java applications without the need for a Docker daemon.

Kubernetes resource that creates one or more pods to perform a specific task and ensures they successfully complete, ideal for batch processes.

Lightweight Kubernetes distribution designed for edge, IoT, and resource-constrained environments, simplifying cluster deployment.

Terminal-based UI for managing and monitoring Kubernetes clusters, providing an intuitive interface for cluster operations.

Kubernetes-based Event Driven Autoscaling, enabling fine-grained autoscaling for event-driven workloads based on event sources.

Kubernetes Universal Declarative Operator, a toolkit for writing operators using a declarative spec, simplifying complex application management.

Tool for building container images from a Dockerfile inside a container or Kubernetes cluster, without requiring a Docker daemon.

Framework for application-level data management in Kubernetes, ensuring consistent backups of stateful apps.

Open-source project for multi-cluster application management in Kubernetes, facilitating resource distribution across clusters.

Lightweight virtual machines that feel and perform like containers, but provide the workload isolation and security of VMs.

Major update to Kata Containers, introducing improved performance, reduced footprint, and enhanced security features.

Cloud-native application lifecycle orchestration tool, automating deployment, testing, and remediation for containerized apps.

Technique allowing network packets to bypass the kernel, improving performance in container networking scenarios.

Linux feature providing isolation for system resources, fundamental to container implementation and resource separation.

CNI plugin using WireGuard to create a mesh overlay network for Kubernetes, enhancing security and simplicity.

Kubernetes-based platform for building, deploying, and managing serverless workloads, simplifying serverless application development.

Knative component providing tools for routing events between publishers and subscribers in cloud-native applications.

Knative component for deploying and managing serverless workloads, handling scaling and routing of containerized applications.

Tool allowing WebAssembly modules to run in Kubernetes clusters, enabling language-agnostic workloads.

Network virtualization solution for Kubernetes integrating the Open vSwitch software switch to provide advanced networking features.

Implementation of Kube-OVN to leverage Open vSwitch for flexible, high-performance container networking.

Tool for checking whether Kubernetes deployments adhere to Center for Internet Security (CIS) best practices.

Use of kube-bench to perform automated security audits of Kubernetes clusters against established benchmarks.

Kubernetes networking solution providing direct container-to-container networking with BGP-based routing.

Tool for static code analysis of Kubernetes object definitions, identifying misconfigurations and potential issues.

Service that listens to the Kubernetes API server and generates metrics about the state of Kubernetes objects.

Open-source system extending native containerized application orchestration capabilities to hosts at Edge.

Tool for bootstrapping a minimum viable Kubernetes cluster, simplifying the process of setting up a production-grade cluster.

Open-source tool for auditing Kubernetes clusters and workloads for potential security issues and best practice violations.

Framework for building Kubernetes APIs using custom resource definitions (CRDs), simplifying the creation of operators and controllers.

Tool providing real-time cost visibility and insights for running applications on Kubernetes, optimizing resource allocation.

Kubernetes Cluster Federation, enabling resource management across multiple Kubernetes clusters from a single control plane.

Kubernetes-native serverless framework, allowing deployment of small bits of code (functions) without managing underlying infrastructure.

System for evolving the Kubernetes API over time, allowing for backwards compatibility and gradual feature deprecation.

Plugins intercepting requests to the Kubernetes API server before object persistence, enforcing policies.

Feature allowing the Kubernetes API to be extended with additional APIs, enabling custom resources and controllers.

Key-value pairs for attaching non-identifying metadata to objects, used for tooling and client retrieval.

Mechanisms (HPA, VPA, Cluster Autoscaler) for automatically adjusting resources based on demand and metrics.

CNCF initiative ensuring consistency and portability across Kubernetes distributions and installations.

API objects automatically created when other objects change state, providing a cluster activity audit trail.

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

Field on resources preventing accidental deletion, ensuring proper cleanup of related resources or external dependencies.

Process of automatically deleting objects that no longer have owners, preventing resource leaks.

Evolved ingress and service networking, providing a more expressive, extensible way to configure traffic routing.

Key-value pairs attached to objects for identification and selection, crucial for organizing and selecting object subsets.

Cluster-wide aggregator of resource usage data, providing CPU and memory metrics for Horizontal Pod Autoscaler.

Software extensions to Kubernetes that use custom resources to manage applications and their components.

Relationship between API objects where one object owns another, used for cascading deletion and orphan handling.

Label query over a set of resources, used for identifying and grouping API objects based on their labels.

Policies and timelines for Kubernetes version releases, support, and deprecation, guiding upgrade planning.

Fully functional Kubernetes clusters running on top of other Kubernetes clusters, enabling multi-tenancy.

Web-based tool for security risk analysis of Kubernetes resource manifests, identifying vulnerabilities.

Kubernetes native configuration management tool that allows customization of YAML manifests without templating.

Technique in Kustomize for creating variations of a common base of Kubernetes resources, enabling environment-specific configs.

Method in Kustomize for modifying existing Kubernetes resources without directly editing the original YAML files.

Cloud security platform providing compliance monitoring and threat detection for containerized environments.

Unit of a container image representing a set of files or changes, crucial for image building and optimization.

Storage mechanism used by container runtimes, allowing efficient storage and quick updates of container images.

Design pattern ensuring only one instance of a distributed application component is active at a time.

Process where multiple instances of an operator elect a leader to prevent conflicts in resource management.

Security concept of giving a user or process only the minimum levels of access necessary to perform its function.

User-friendly, open-source IDE for managing Kubernetes clusters, providing a unified interface for multiple clusters.

Observability platform providing insights into the behavior of highly distributed software systems and microservices.

Kubernetes policy for constraining resource allocations (CPU and memory) for pods or containers in a namespace.

Kubernetes object defining the min, max, and default compute resources for pods and containers in a namespace.

Lightweight service mesh for Kubernetes, providing observability, reliability, and security for microservices.

Component of Linkerd responsible for proxying traffic between services, implementing service mesh functionality.

Linkerd resource providing service-specific traffic metrics and control, enhancing observability and management.

High-performance, minimal-footprint proxy used in Linkerd's data plane for efficient service mesh operations.

Framework allowing various security models to be plugged into the Linux kernel, enhancing container isolation.

Process of connecting external load balancers with Kubernetes services for distributing incoming traffic.

Methods used by load balancers to distribute traffic across multiple container instances or pods.

Kubernetes service type that provisions an external load balancer in cloud environments for routing external traffic to services.

Kubernetes volume type for locally attached storage, providing high-performance storage options for stateful workloads.

Technique for routing traffic to geographically or topologically closer endpoints in distributed container deployments.

Techniques for collecting, processing, and storing logs from distributed containerized applications for centralized analysis.

Design of systems for capturing, storing, and analyzing logs from containers and orchestration platforms.

Horizontally-scalable, multi-tenant log aggregation system designed for use with Prometheus and Kubernetes.

Cloud-native distributed block storage system for Kubernetes, providing persistent storage for stateful workloads.

Implementation of Longhorn for providing resilient, replicated block storage in Kubernetes clusters.

Comparison of container runtimes operating at different abstraction levels, balancing performance and features.

Technique for precomputing and storing query results, improving performance in microservices architectures.