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.

Policies and processes for ensuring data quality, security, and compliance in cloud environments.

Cloud-based tools for combining data from various sources into a unified view, enabling comprehensive data analysis.

Centralized repository storing structured and unstructured data at scale in its native format.

Large-scale storage repository for raw data in various formats in cloud environments.

Hybrid architecture combining data lake storage with data warehouse query capabilities in cloud environments.

Tracking the origin, movement, and transformations of data throughout its lifecycle in cloud systems.

Monitoring data origins, transformations, and usage across cloud environments for compliance and analysis.

Subset of a data warehouse focused on specific business functions or departments in cloud environments.

Techniques to protect sensitive information by obscuring or replacing identifiable data in cloud storage.

Decentralized approach to data management, treating data as a product owned by domain teams in cloud environments.

Tools supporting implementation of data mesh principles in cloud-based data architectures.

Process of transferring data between storage types, formats, or systems in cloud environments.

Automated process for extracting, transforming, and loading data between cloud systems or services.

Systematic analysis of how data processing activities affect privacy in cloud environments.

Cloud-based tools for assessing, improving, and maintaining the quality of data assets.

Specifying the physical location where cloud-stored data resides for compliance with regulations.

Controls for protecting intellectual property and sensitive information in cloud-based data sharing.

Platforms for sharing and monetizing data science models and algorithms in the cloud.

Tools for tracking changes and collaborating on data science projects in cloud environments.

Techniques for managing uneven data distribution in cloud-based parallel processing.

Concept that data is subject to the laws of the country in which it is stored in cloud environments.

Cloud-based tools for replacing sensitive data with non-sensitive equivalents for security purposes.

Capability to access and restore previous versions of data in cloud storage systems.

Technology that provides a unified view of data from multiple cloud and on-premises sources without moving it.

Tools and techniques for creating visual representations of data stored in cloud environments.

Centralized repository optimized for analytics and business intelligence in cloud environments.

Process of collecting, storing, and managing data from varied sources in a central cloud repository for analysis.

Monitoring and recording database activities in cloud environments for security and compliance purposes.

Process of creating copies of databases and recovering them in cloud environments for data protection.

Technique to store frequently accessed data in memory for faster retrieval in cloud databases.

Process of encoding data to protect sensitive information in cloud-based databases.

Automatic switching to a redundant database when the primary system fails in cloud environments.

Techniques ensuring continuous database operation in cloud environments, minimizing downtime.

Process of moving databases from on-premises or other cloud environments to a target cloud platform.

Managed service for migrating databases to cloud platforms with minimal downtime.

Optimizing database operations for improved speed and efficiency in cloud environments.

Intermediary service managing connections between applications and cloud databases for improved security and performance.

Adjusting database resources vertically (more powerful hardware) or horizontally (more instances) in cloud environments.

Distributing data across multiple database instances for improved performance and scalability in the cloud.

Process of updating database software to newer versions in cloud environments, improving performance and security.

Managed cloud service providing database functionality without the need for infrastructure management.

Storage for messages that can't be processed successfully in cloud-based message queuing systems.

Distributed cloud computing model leveraging blockchain technology for increased security and user control.

Blockchain-based platforms for trading compute resources across distributed cloud networks.

Self-sovereign identity management using blockchain technology in cloud environments.

Systems for user authentication and authorization using blockchain in cloud environments.

Distributed file storage systems using peer-to-peer networks in cloud computing environments.

Security approach using decoys to detect, deceive, and deflect attackers in cloud environments.

Contrasting approaches to defining cloud infrastructure, focusing on desired state vs. step-by-step instructions.

Subset of machine learning using neural networks with multiple layers for complex pattern recognition in cloud environments.

Tools and practices for automatically releasing and updating applications in cloud environments.

Service for declaratively managing and deploying cloud resources on Google Cloud Platform.

Automated, end-to-end pipelines for software delivery in cloud environments, streamlining development and deployment processes.

Collaborative approach integrating development and operations teams for efficient cloud-based software delivery.

Measurements for assessing the performance and efficiency of DevOps processes in cloud environments.

Integration of security practices into DevOps processes using automated tools in cloud environments.

Integrated tools for implementing security throughout the DevOps lifecycle in cloud environments.

Tools and practices for configuring, monitoring, and securing IoT devices connected to cloud platforms.

Process of setting up and configuring IoT devices for secure connection to cloud platforms.

Cloud-based tools for adding noise to data to protect individual privacy while maintaining overall accuracy.

Tools for tracking and optimizing user interactions with cloud-based applications and services.

Virtual representations of physical objects or systems, often leveraging cloud resources for simulation and analysis.

Virtual models of physical assets or processes, often using cloud computing for data processing and simulation.

Cloud-based virtual representations of physical entities for monitoring, simulation, and optimization.

Dedicated network connection between on-premises infrastructure and cloud service providers.

Cloud infrastructure designs separating compute, storage, and networking for flexible resource allocation.

Cloud storage architecture separating storage management functions from physical storage devices.

Strategies and tools for recovering data and systems after a catastrophic event in cloud environments.

Process of training machine learning models across multiple cloud nodes for improved speed and efficiency.

Cloud services distributed across multiple physical locations but managed centrally.

Techniques for solving complex problems across multiple cloud nodes while satisfying various constraints.

Cloud-based systems for running data analysis and machine learning tasks across multiple nodes.

Cloud-based security systems deploying decoys across multiple points to detect and mislead attackers.

Process of training AI models across multiple edge devices and cloud resources, enabling decentralized learning at scale.

Technique for tuning machine learning models across multiple cloud nodes for improved performance.

Managing and coordinating blockchain networks across multiple cloud environments.

Tracking and analyzing requests as they flow through cloud-based microservices architectures.

Specialized programming languages designed for efficient cloud resource management and deployment.

Monitoring and identifying unauthorized or unintended changes in cloud infrastructure configurations.

Extension of Azure Functions that enables stateful operations in serverless compute environments.

Adaptive security measures that adjust permissions based on context in cloud environments.

Responsible disposal and recycling of outdated or decommissioned cloud hardware.

Strategies for sustainably disposing of and recycling obsolete cloud computing hardware.

Process of moving data from source systems into a data warehouse in cloud environments.

Approaches to moving to the cloud that minimize environmental impact and energy consumption.

Artificial intelligence algorithms running on devices at the network edge, often integrated with cloud backends.

Methods for reducing the size of AI models to run efficiently on edge devices with cloud support.

Managing and updating AI model versions across distributed edge devices and cloud systems.

Measures to protect AI models and data on edge devices connected to cloud networks.

Artificial intelligence and machine learning capabilities deployed on edge devices, often with cloud integration.

Processing and analyzing data near its source on edge devices before sending insights to the cloud.

Software platforms for developing and deploying analytics applications on edge devices with cloud connectivity.

Identifying unusual patterns or behaviors in data at the network edge before sending alerts to the cloud.

Storing frequently accessed data on edge devices or servers to reduce latency and cloud bandwidth usage.

Network architectures optimized for connecting edge computing devices to cloud resources.

Processing and analyzing visual data on edge devices before sending results to the cloud.

Distributing and caching content on edge servers to reduce latency and cloud bandwidth consumption.

Database systems designed to run on edge devices with intermittent connectivity to cloud backends.

Creating AI-generated content on edge devices with potential cloud-based model updates and synchronization.

Techniques to improve AI model performance on edge devices, reducing latency and cloud dependence.

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.