Common Development Terminology – SolveForce Fiber Internet, Cloud Computing & Telecommunications

A/B testing: a method of comparing two versions of a product or feature to determine which one performs better.
Acceptance criteria: a set of conditions that a software program must meet in order to be accepted by the customer or end user.
Accessibility: the practice of designing and developing products that can be used by as many people as possible, including those with disabilities.
Agile development: a method of software development that emphasizes rapid iteration, flexibility, and collaboration between cross-functional teams.
API (Application Programming Interface): a set of protocols, routines, and tools for building software and applications. APIs specify how software components should interact and APIs allow for communication between different systems.
Artificial Intelligence (AI): a branch of computer science that deals with the development of intelligent systems and algorithms that can perform tasks that typically require human intelligence, such as understanding natural language, recognizing images, and making decisions.
Automated testing: the use of software to perform repetitive or complex tasks, such as testing the functionality of an application, without human intervention.
Backend development: the work of creating and maintaining the server-side of an application, including the database and APIs.
Beta testing: the final testing phase before a product is released to the general public, where a small group of users test the product and provide feedback.
Bias in AI: the phenomenon of machine learning models making decisions that are unfair or discriminatory towards certain groups of people.
Big Data: a term used to describe large and complex data sets that traditional data processing techniques may struggle to handle.
Black box testing: a type of testing that examines the functionality of a software program without looking at its internal structure.
Blockchain: a decentralized and distributed digital ledger that is used to record transactions across multiple computers.
Bug tracking: the process of documenting and tracking bugs in a software program.
Bug: an error or defect in a software program that causes it to malfunction.
Bullet Point List All Development Terminology and Related Definitions.
Business Intelligence (BI): the practice of using data and analytics to support decision making.
Chatbot: a computer program that simulates conversation with human users through text or voice interactions.
Cloud Adoption: the process of moving from traditional on-premises infrastructure to cloud-based services.
Cloud AI as a Service (AIaaS): a cloud-based service that allows to access and use AI capabilities and algorithms using cloud-based resources, typically provided on a pay-per-use basis.
Cloud Analytics as a Service (AaaS): a cloud-based service that allows to perform data analysis and modeling using cloud-based resources, typically provided on a pay-per-use basis.
Cloud API Analytics: the practice of collecting, analyzing, and visualizing data about the usage and performance of APIs in a cloud environment.
Cloud API Developer portal: a portal that provides developers with the necessary resources and documentation to create and consume APIs in a cloud environment.
Cloud API Development: the practice of creating, testing and deploying APIs in a cloud environment.
Cloud API documentation: the process of providing documentation, examples, and tutorials for the use of APIs in a cloud environment.
Cloud API gateway aggregation: the practice of combining multiple API requests into a single request in order to improve performance and reduce the load on the backend systems.
Cloud API gateway caching: the practice of caching API responses in a cloud environment in order to improve performance and reduce the load on the backend systems.
Cloud API gateway deployment: the process of deploying and managing API gateways in a cloud environment.
Cloud API gateway management: the practice of managing and configuring API gateways in a cloud environment, including tasks such as security, routing, and scalability.
Cloud API gateway monitoring: the practice of monitoring the performance and availability of API gateways in a cloud environment.
Cloud API gateway orchestration: the practice of managing the flow of API requests and responses in a cloud environment, including tasks such as routing, transformation, and aggregation.
Cloud API gateway rate limiting: the practice of limiting the number of requests that can be made to an API in a given time period in order to prevent overloading the backend systems.
Cloud API gateway routing: the practice of directing API requests to the appropriate backend systems in a cloud environment.
Cloud API gateway scalability: the ability of API gateways in a cloud environment to handle an increasing number of requests and users.
Cloud API gateway security: the practice of securing API requests and responses in a cloud environment, including tasks such as authentication, authorization, and encryption.
Cloud API gateway transformation: the practice of modifying the request and response of an API in a cloud environment, such as converting data formats or adding headers.
Cloud API Gateway: a service that acts as a central point of entry for APIs, and is responsible for tasks such as authentication, authorization, rate limiting, and caching.
Cloud API Gateway: a service that acts as a centralized entry point for APIs, and is responsible for tasks such as authentication, authorization, rate limiting, and caching.
Cloud API Governance: the practice of managing and controlling the use of APIs in a cloud environment, including tasks such as security, versioning, and analytics.
Cloud API Integration: the process of integrating APIs with other systems and services in a cloud environment.
Cloud API Lifecycle management: the process of managing the entire lifecycle of an API, from development to retirement, in a cloud environment.
Cloud API Management as a Service (APIMaaS): a cloud-based service that allows to manage and govern the use of APIs in a cloud environment, typically provided on a pay-per-use basis.
Cloud API Management Platform: a platform that allows to manage and govern the use of APIs in a cloud environment, including tasks such as security, versioning, and analytics.
Cloud API Management: the practice of managing, securing and scaling the APIs that are used to access cloud-based resources and services.
Cloud API monetization: the practice of generating revenue from the usage of APIs in a cloud environment.
Cloud API monitoring: the process of monitoring the performance and availability of APIs in a cloud environment.
Cloud API Portal: a web-based portal that provides a user-friendly interface for discovering, testing, and subscribing to APIs in a cloud environment.
Cloud API Security: the practice of securing the APIs that are used to access cloud-based resources and services, including tasks such as authentication, authorization, and encryption.
Cloud API testing: the process of testing the functionality and performance of APIs in a cloud environment.
Cloud API versioning: the practice of managing different versions of an API in a cloud environment.
Cloud Application Development: the practice of developing and deploying applications in a cloud environment.
Cloud Application Integration: the process of integrating applications in a cloud environment with other systems and services.
Cloud Application Lifecycle Management: the process of managing the entire lifecycle of an application in a cloud environment, from development to retirement.
Cloud Application Performance Management (APM): the practice of monitoring, managing and optimizing the performance of applications in a cloud environment.
Cloud Application Security: the practice of securing applications in a cloud environment, including tasks such as vulnerability management, penetration testing, and threat detection.
Cloud Automation Tools: software tools that automate tasks and processes related to the deployment, management, and scaling of cloud-based resources and services, such as Terraform, Ansible, and CloudFormation.
Cloud Automation: the practice of automating tasks and processes related to the deployment, management, and scaling of cloud-based resources and services.
Cloud Auto-scaling: the ability of a cloud-based system to automatically scale its resources based on demand.
Cloud Backup and Recovery: the process of creating and restoring backups of cloud-based resources and data.
Cloud Backup as a Service (BaaS): a cloud-based service that allows to create and restore backups of data and applications in a cloud environment.
Cloud Brokerage: the practice of providing a single point of access to various cloud services and providers, to simplify procurement and management.
Cloud Business Intelligence (BI) as a Service: a cloud-based service that allows to access, visualize, and analyze business data using cloud-based resources, typically provided on a pay-per-use basis.
Cloud Compliance and Auditing: the practice of ensuring that cloud-based resources and services meet compliance requirements, such as HIPAA, SOC2, PCI DSS, and ISO 27001.
Cloud Compliance Automation: the practice of automating compliance-related tasks such as monitoring, reporting, and remediation in a cloud environment.
Cloud computing: the delivery of computing resources, such as servers, storage, and applications, over the internet.
Cloud Container as a Service (CaaS): a cloud-based service that allows to deploy and manage containerized applications, typically provided on a pay-per-use basis.
Cloud Container Orchestration: the practice of automating the deployment, scaling, and management of containerized applications and services in a cloud environment.
Cloud Container Security: the practice of securing containerized applications and environments, such as Kubernetes and Docker.
Cloud Containerization: the practice of packaging an application and its dependencies in a container, allowing it to run consistently across different environments.
Cloud Content Delivery Network (CDN): a network of servers distributed across the globe that are used to deliver content such as images, videos, and web pages to users with low latency and high availability.
Cloud Continuous Delivery (CD): the practice of automatically delivering code changes to production in a cloud environment.
Cloud Continuous Deployment (CD): the practice of automatically deploying code changes to production in a cloud environment.
Cloud Continuous Integration (CI): the practice of integrating code changes frequently and automatically in a cloud environment.
Cloud Continuous Integration and Continuous Deployment (CI/CD): the practice of automatically building, testing, and deploying software updates in a cloud environment.
Cloud Continuous Testing (CT): the practice of automatically testing code changes in a cloud environment.
Cloud Cost Management: the practice of monitoring, controlling and optimizing the costs associated with the use of cloud-based resources and services.
Cloud Cost Optimization: the process of reducing the cost of using cloud-based resources and services without compromising on performance or availability.
Cloud Data archiving: the process of moving data from operational systems to long-term storage for compliance, regulatory or business reasons.
Cloud Data backup: the process of creating copies of data in a cloud environment to protect against data loss or corruption.
Cloud Data catalog: a centralized repository that allows to discover, understand and manage the data across an organization.
Cloud Data governance: the practice of managing and controlling data in a cloud environment, including issues such as security, compliance, and accessibility.
Cloud Data integration: the process of combining data from different sources into a single, unified view.
Cloud Data lake as a service (DLaaS): a cloud-based service that allows to store and process large amounts of data in a centralized repository, typically provided on a pay-per-use basis.
Cloud Data lake: a centralized repository that allows to store all the structured and un-structured data at any scale.
Cloud Data migration: the process of moving data from on-premises systems to a cloud environment, or between different cloud providers.
Cloud Data pipeline: a series of steps and processes that are used to move data from one system or storage location to another.
Cloud Data quality: the practice of ensuring the accuracy, completeness, consistency and relevance of data stored in a cloud environment.
Cloud Data replication: the process of creating multiple copies of data in a cloud environment to protect against data loss and improve accessibility.
Cloud Data security: the practice of protecting data stored in a cloud environment from unauthorized access and use.
Cloud Data warehousing as a service (DWaaS): a cloud-based service that allows to store and process large amounts of data using cloud-based resources, typically provided on a pay-per-use basis.
Cloud Data warehousing: a data warehousing service provided by cloud providers, allowing to store and process large amounts of data using cloud-based resources.
Cloud Datamart: a subset of data stored in a data warehouse that is optimized for a specific business function or department.
Cloud DDoS Protection: the practice of protecting cloud-based resources and services from Distributed Denial of Service (DDoS) attacks.
Cloud DevOps: the practice of aligning development and operations teams to automate the software delivery process in a cloud environment.
Cloud Disaster Recovery (DR): the process of recovering cloud-based resources and services in the event of a disaster or outage.
Cloud Disaster Recovery as a Service (DRaaS): a cloud-based service that allows to recover data and applications in the event of a disaster or outage.
Cloud Disaster Recovery: the practice of creating and maintaining a plan to recover from a disaster or outage in a cloud environment.
Cloud Docker: an open-source platform for developing, shipping, and running containerized applications and services.
Cloud Edge computing: a distributed computing paradigm that brings data storage, processing and other functionalities closer to the sources of data, such as sensors or IoT devices.
Cloud Edge Computing: the practice of processing data and running applications at the edge of a network, closer to the source of data, rather than in a centralized data center or cloud.
Cloud Encryption and Key Management: the practice of encrypting data at rest and in transit in a cloud environment and managing the encryption keys.
Cloud Event Sourcing: the practice of storing all the events that happen in a system, rather than just the current state, in a cloud environment.
Cloud Event-Driven Analytics: the practice of analyzing the events that happen in a cloud environment to gain insights and make decisions.
Cloud Event-driven Architecture (EDA): a software architecture pattern that allows to process and respond to events in a cloud environment, typically using technologies such as messaging queues and webhooks.
Cloud Event-Driven Architecture (EDA): an architecture pattern where systems respond to events in real-time, often implemented using cloud-based services such as FaaS, CaaS, and IoTaaS.
Cloud Event-Driven Automation: the practice of automating tasks and processes in a cloud environment based on events.
Cloud Event-Driven Computing: the practice of processing and responding to events in a cloud environment, typically using technologies such as messaging queues and webhooks.
Cloud Event-Driven Governance: the practice of managing and controlling the use of event-driven systems and services in a cloud environment.
Cloud Event-Driven Integration: the practice of integrating systems and services in a cloud environment using events and messaging.
Cloud Event-Driven Microservices: a microservices architecture pattern where microservices communicate and coordinate using events in a cloud environment.
Cloud Event-Driven Monitoring: the practice of monitoring event-driven systems and services in a cloud environment.
Cloud Event-Driven Security: the practice of securing event-driven systems and services in a cloud environment.
Cloud Event-Driven Serverless: the practice of building event-driven applications that are deployed on a serverless computing platform in a cloud environment.
Cloud Firewall: a security service that controls incoming and outgoing network traffic to a cloud environment based on predefined security rules.
Cloud Function as a Service (FaaS): a cloud-based service that allows to execute code in response to specific events or triggers, typically provided on a pay-per-use basis.
Cloud Function as a Service (FaaS): a cloud-based service that allows to run code in response to specific events, such as an HTTP request or a message on a queue, without the need to manage the underlying infrastructure.
Cloud Global Accelerator: a service offered by some cloud providers that allows to route traffic to the nearest cloud-based resources in order to reduce latency and improve performance.
Cloud Global Network: a network of data centers and points of presence (PoPs) distributed across the globe that are used to provide low-latency and high-performance connectivity to cloud-based resources.
Cloud Governance Automation: the practice of automating tasks related to managing and controlling the use of cloud-based resources and services in an organization.
Cloud Governance: the practice of managing and controlling the use of cloud-based resources and services in an organization.
Cloud Hybrid Cloud: the practice of using a combination of on-premises infrastructure and cloud-based services to meet the needs of an organization.
Cloud Hybrid Cloud: the practice of using a combination of on-premises, private and public cloud resources and services.
Cloud Identity and Access Management (IAM): the practice of controlling access to cloud-based resources and services based on user identities and roles.
Cloud Infrastructure as a Service (IaaS): a cloud-based service that provides virtualized computing resources, such as servers, storage, and network connectivity, typically provided on a pay-per-use basis.
Cloud Infrastructure as a Service (IaaS): a cloud-based service that provides virtualized computing resources, such as servers, storage, and network connectivity.
Cloud Infrastructure as Code (IaC): the practice of managing and provisioning cloud-based resources using code and automation tools, rather than manual configuration.
Cloud Infrastructure as Code (IaC): the practice of provisioning and managing infrastructure using code, rather than manual configuration.
Cloud Interoperability: the ability to connect, integrate and work with various cloud services, regardless of the provider or platform.
Cloud IoT as a Service (IoTaaS): a cloud-based service that allows to collect, store, and analyze data from IoT devices using cloud-based resources, typically provided on a pay-per-use basis.
Cloud Kubernetes: an open-source container orchestration system for automating the deployment, scaling, and management of containerized applications and services in a cloud environment.
Cloud Load Balancing: the practice of distributing incoming traffic across multiple cloud-based resources to ensure high availability and performance.
Cloud Machine Learning as a Service (MLaaS): a cloud-based service that allows to build, train, and deploy machine learning models using cloud-based resources, typically provided on a pay-per-use basis.
Cloud Management and Automation: the process of automating and managing the deployment, scaling, and monitoring of cloud-based resources and services.
Cloud Management Platform (CMP): a platform that allows to manage and orchestrate multiple cloud environments and services from a single console.
Cloud Metrics and Billing: the practice of measuring, tracking and billing for the usage and cost of cloud-based resources and services.
Cloud Microservices: an architecture pattern where a large application is broken down into smaller, independently deployable services.
Cloud Migration Tools: software tools that automate the process of moving data, applications, and workloads from on-premises or other cloud environments to a new cloud environment.
Cloud Migration: the process of moving data, applications, and workloads from on-premises or other cloud environments to a new cloud environment.
Cloud Monitoring: the practice of monitoring cloud-based resources and services for performance, availability, and security.
Cloud Multi-cloud: the practice of using multiple cloud providers and services for different workloads and applications.
Cloud Multi-Cloud: the practice of using multiple cloud providers to distribute workloads, data, and services across different environments.
Cloud Network Security: the practice of securing a cloud environment’s network infrastructure, such as firewalls, VPNs, and intrusion detection/prevention systems.
Cloud Pen testing: the practice of simulating a cyber-attack on a cloud environment to identify security weaknesses.
Cloud Penetration Testing: the practice of simulating a cyber-attack on a cloud environment to identify security weaknesses.
Cloud Platform as a Service (PaaS): a cloud-based service that provides a platform for developing, deploying, and managing software applications, typically provided on a pay-per-use basis.
Cloud Platform as a Service (PaaS): a cloud-based service that provides a platform for developing, deploying, and managing software applications.
Cloud Portability: the ability to move data and workloads between different cloud environments, providers and platforms.
Cloud Private Network: a network that is used to connect on-premises resources to cloud-based resources in a secure and private manner, often using technologies such as VPN and Direct Connect.
Cloud Resource management: the practice of managing and optimizing the use of cloud-based resources such as compute, storage and network.
Cloud Resource Management: the practice of managing and optimizing the use of cloud-based resources such as compute, storage, and network.
Cloud Robotics as a Service (RaaS): a cloud-based service that allows to build, operate and manage robots using cloud-based resources, typically provided on a pay-per-use basis.
Cloud Security and Compliance: the practice of ensuring that cloud-based resources and services meet security and compliance requirements, such as data protection and regulatory compliance.
Cloud Security Automation: the practice of automating security-related tasks such as monitoring, reporting, and remediation in a cloud environment.
Cloud Security Information and Event Management (SIEM): the practice of collecting, analyzing, and responding to security-related data and events in a cloud environment.
Cloud Security Operations Center (SOC): the practice of centralizing security-related operations, such as monitoring, incident response, and threat intelligence, in a cloud environment.
Cloud Serverless computing: a cloud computing paradigm where the cloud provider is responsible for allocating and managing the servers required to run an application, and the user only pays for the resources used.
Cloud Serverless Computing: a cloud-based computing model in which the cloud provider is responsible for managing and scaling the underlying infrastructure, allowing developers to focus on writing code.
Cloud Service Brokerage: the practice of providing a single point of access to various cloud services and providers, to simplify procurement and management.
Cloud Service Catalog: a centralized repository of all the cloud services available in an organization, with information on pricing, terms of service, and support.
Cloud Service Level Agreement (SLA): a contract between a cloud provider and a customer that outlines the level of service and availability that the provider will deliver for the customer’s cloud-based resources and services.
Cloud Service Management: the practice of managing and controlling the use of cloud-based services in an organization.
Cloud Software as a Service (SaaS): a cloud-based service that provides access to software applications over the internet, typically provided on a pay-per-use basis.
Cloud Software as a Service (SaaS): a cloud-based service that provides access to software applications over the internet.
Cloud Vulnerability Management: the practice of identifying, assessing, and mitigating security vulnerabilities in cloud-based resources and services.
Cloud Web Application Firewall (WAF): a security service that protects web applications from common web-based attacks such as SQL injection, cross-site scripting, and session hijacking.
Cloud-based Artificial Intelligence (AI) and Machine Learning (ML): the practice of building and deploying AI and ML models in a cloud environment.
Cloud-based Automated Deployment: the practice of automatically deploying and managing cloud-based resources and services using tools such as Ansible, Terraform, and CloudFormation.
Cloud-based Automated Testing: the practice of automatically testing cloud-based resources and services in a cloud environment.
Cloud-based Autonomous Systems: the practice of building and deploying systems that can operate independently in a cloud environment, using technologies such as AI and ML.
Cloud-based Big Data Processing: the practice of processing large amounts of data in a cloud environment using technologies such as Hadoop and Spark.
Cloud-based Blockchain: the practice of building and deploying blockchain-based applications and services in a cloud environment.
Cloud-based Business Continuity Management: the practice of creating and maintaining a plan to ensure the continuity of business operations in a cloud environment.
Cloud-based Capacity Planning: the practice of forecasting and managing the capacity of cloud-based resources and services in a cloud environment.
Cloud-based Cloud Native: The practice of designing and building applications and services that are specifically optimized to run in a cloud environment, using cloud-native technologies and principles such as containers and microservices.
Cloud-based Cloud-native Agile: The practice of applying Agile methodologies to the development and delivery of cloud-native applications and services in a cloud environment.
Cloud-based Cloud-native analytics as code: The practice of automating the collection, analysis, and visualization of data from cloud-native infrastructure and applications in a cloud environment, using tools such as Apache Kafka and Apache Spark.
Cloud-based Cloud-native analytics: The practice of collecting, analyzing, and visualizing data from cloud-native applications and services in a cloud environment, using tools such as Apache Kafka and Apache Spark.
Cloud-based Cloud-native Ansible: The practice of using Ansible as an automation tool for configuring and managing cloud-native infrastructure in a cloud environment.
Cloud-based Cloud-native automation as code: The practice of automating the deployment, scaling, and management of cloud-native infrastructure and applications in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native automation: The practice of automating the deployment, scaling, and management of cloud-native applications and services in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native AWS IAM: The practice of using AWS Identity and Access Management (IAM) as a tool for securing and managing access to AWS resources for cloud-native applications and services in a cloud environment.
Cloud-based Cloud-native CI/CD as code: The practice of automating the build, test, and deployment of cloud-native infrastructure and applications in a cloud environment, using tools such as Jenkins and GitLab.
Cloud-based Cloud-native CI/CD: The practice of automating the build, test, and deployment of cloud-native applications and services in a cloud environment, using tools such as Jenkins and GitLab.
Cloud-based Cloud-native containerization: The practice of packaging and deploying cloud-native applications and services in containers in a cloud environment, using tools such as Docker and Kubernetes.
Cloud-based Cloud-native development: The practice of building cloud-native applications and services, using technologies such as Kubernetes, Envoy, and Istio.
Cloud-based Cloud-native DevOps: The practice of integrating software development and IT operations in a cloud environment to optimize the delivery of cloud-native applications and services.
Cloud-based Cloud-native Disaster recovery as code: The practice of automating the creation and maintenance of a disaster recovery plan for cloud-native infrastructure and applications in a cloud environment.
Cloud-based Cloud-native disaster recovery: The practice of creating and maintaining a plan to recover cloud-native applications and services in the event of a disaster or outage in a cloud environment.
Cloud-based Cloud-native Docker: The practice of using Docker as a containerization platform for building and deploying cloud-native applications and services in a cloud environment.
Cloud-based Cloud-native Elasticsearch: The practice of using Elasticsearch as a search and analytics engine for collecting and analyzing log data from cloud-native applications and services in a cloud environment.
Cloud-based Cloud-native Envoy: The practice of using Envoy as a service proxy for managing and securing the communication between microservices in a cloud-native environment.
Cloud-based Cloud-native GitLab: The practice of using GitLab as a source code management, continuous integration, and continuous delivery (CI/CD) tool for building and deploying cloud-native applications and services in a cloud environment.
Cloud-based Cloud-native governance as code: The practice of automating the implementation and enforcement of governance policies for cloud-native infrastructure and applications in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native governance: The practice of managing and governing the use of cloud-native applications and services in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native Grafana: The practice of using Grafana as a visualization tool for monitoring and analyzing the performance of cloud-native applications and services in a cloud environment.
Cloud-based Cloud-native HashiCorp Vault: The practice of using HashiCorp Vault as a tool for automating the implementation and enforcement of security policies for cloud-native infrastructure and applications in a cloud environment.
Cloud-based Cloud-native Helm: The practice of using Helm as a package manager for Kubernetes to manage cloud-native applications and services in a cloud environment.
Cloud-based Cloud-native infrastructure as code (IaC): The practice of provisioning, configuring, and managing cloud-native infrastructure using code and automation in a cloud environment, using tools such as Terraform and Ansible.
Cloud-based Cloud-native integration: The practice of integrating cloud-native applications and services with other systems and services in a cloud environment.
Cloud-based Cloud-native Istio: The practice of using Istio as a service mesh for managing and securing the communication between microservices in a cloud-native environment.
Cloud-based Cloud-native Jenkins: The practice of using Jenkins as a continuous integration and continuous delivery (CI/CD) tool for building and deploying cloud-native applications and services in a cloud environment.
Cloud-based Cloud-native Kibana: The practice of using Kibana as a visualization tool for analyzing log data from cloud-native applications and services in a cloud environment.
Cloud-based Cloud-native Kubernetes A/B Testing: The practice of using Kubernetes and its ecosystem of tools, such as canary deployments, to perform A/B testing on cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Admission Controllers: The practice of using Kubernetes Admission Controllers to define and enforce validation and mutation policies for Kubernetes resources and operations in a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Admission Controllers: The practice of using Kubernetes Admission Controllers to enforce policies and validate the configuration of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes API Gateway: A service that provides external access to the services in a cluster, typically via HTTP and is responsible for request routing, protocol translation, and other functions.
Cloud-based Cloud-native Kubernetes API Server: The main component of a Kubernetes cluster, responsible for exposing the Kubernetes API and handling the storage and management of Kubernetes resources.
Cloud-based Cloud-native Kubernetes API Server: The practice of using Kubernetes API Server to manage the API endpoint for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Application Management: The practice of using Kubernetes and its ecosystem of tools, such as deployments, stateful sets, and services, to manage and automate the applications running on a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Application: The practice of using Kubernetes and its ecosystem of tools, such as containers, microservices, and service meshes, to build, deploy, and manage cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes as a Service (KaaS): The practice of using Kubernetes and its ecosystem of tools, such as managed Kubernetes services from cloud providers, to provide Kubernetes as a service to users and organizations.
Cloud-based Cloud-native Kubernetes as a Service : The practice of using Kubernetes and its ecosystem of tools, such as managed Kubernetes services from cloud providers, to provide Kubernetes as a service to users and organizations.
Cloud-based Cloud-native Kubernetes Automation: The practice of using Kubernetes and its ecosystem of tools, such as kubeadm, kops, and kubicorn, to automate the deployment and management of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Autoscaling: The practice of using Kubernetes and its ecosystem of tools, such as Horizontal Pod Autoscaler (HPA) and Cluster Autoscaler, to automatically scale the number of replicas of a pod or the number of nodes in a cluster based on CPU or memory utilization.
Cloud-based Cloud-native Kubernetes Backup and Disaster Recovery: The practice of using Kubernetes and its ecosystem of tools, such as persistent volumes, backup operators, and disaster recovery solutions, to manage and automate the backup and disaster recovery of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Backup and Recovery: The practice of using Kubernetes and its ecosystem of tools, such as Velero and Restic, to backup and recover Kubernetes resources and data in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Blue-Green Deployments: The practice of using Kubernetes and its ecosystem of tools, such as deployments, to perform blue-green deployments on cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes CI/CD automation : The practice of using Kubernetes and its ecosystem of tools, such as Jenkins, Travis, and GitLab, to automate the build, test and deployment pipeline for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes CI/CD: The practice of using Kubernetes and its ecosystem of tools, such as Jenkins, Travis, and GitLab, to manage and automate the build, test, and deployment of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Cluster Autoscaler: A Kubernetes controller that automatically scales the number of nodes in a cluster based on pod resource utilization.
Cloud-based Cloud-native Kubernetes Cluster Federation: The practice of using Kubernetes and its ecosystem of tools, such as federation v2, to manage and automate multiple clusters in different regions, availability zones or clouds and enable cross-cluster communication.
Cloud-based Cloud-native Kubernetes Cluster Federation: The practice of using Kubernetes and its ecosystem of tools, such as federation v2, to manage and automate the federation of multiple Kubernetes clusters.
Cloud-based Cloud-native Kubernetes Cluster Management: The practice of using Kubernetes and its ecosystem of tools, such as kubeadm, kops, and kubicorn, to manage and automate the deployment and management of Kubernetes clusters.
Cloud-based Cloud-native Kubernetes Cluster Security: The practice of using Kubernetes and its ecosystem of tools, such as network policies, authentication and authorization, and admission controllers, to secure the communication and access in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Cluster-wide Resources: The practice of using Kubernetes Cluster-wide Resources to manage the configuration of cluster-wide resources such as namespaces, pods and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Compliance Automation: The practice of using Kubernetes and its ecosystem of tools, such as policy controllers, to automate the compliance and management of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Compliance: The practice of using Kubernetes and its ecosystem of tools, such as pod security policies, network policies, and admission controllers, to comply with regulatory and industry standards for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes ConfigMap and Secret Volume: The practice of using Kubernetes ConfigMap and Secret Volume to manage the configuration and secrets of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes ConfigMap and Secrets: Kubernetes objects used to manage configuration data and secrets for pods and other Kubernetes resources.
Cloud-based Cloud-native Kubernetes ConfigMaps: The practice of using Kubernetes ConfigMaps to manage configuration data for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Configuration Management: The practice of using Kubernetes and its ecosystem of tools, such as ConfigMap, Secrets, and Helm, to manage and automate the configuration of a Kubernetes cluster and its resources.
Cloud-based Cloud-native Kubernetes Container Security: The practice of using Kubernetes and its ecosystem of tools, such as admission controllers, network policies, and security contexts, to secure the execution of containers in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Control Plane: The set of components that manage the state and behavior of a Kubernetes cluster, including the API server, etcd, and kube-controller-manager.
Cloud-based Cloud-native Kubernetes Cost Optimization: The practice of using Kubernetes and its ecosystem of tools, such as cluster-autoscaler and spot instances, to optimize the cost of running a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes CronJobs: The practice of using Kubernetes CronJobs to schedule the execution of batch-like cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Custom Resource Definition (CRD): A Kubernetes feature that allows to extend the Kubernetes API with custom resources and custom controllers.
Cloud-based Cloud-native Kubernetes Custom Resources : The practice of using Kubernetes Custom Resources, a way to extend the Kubernetes API, to manage and automate custom resources in a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Custom Resources: The practice of using Kubernetes Custom Resources to extend the Kubernetes API for managing cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes DaemonSet: A higher-level Kubernetes object that ensures a copy of a pod is running on all or a subset of nodes in a cluster
Cloud-based Cloud-native Kubernetes DaemonSets: The practice of using Kubernetes DaemonSets to manage the scaling and rollouts of daemon-like cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Debugging: The practice of using Kubernetes and its ecosystem of tools, such as logging, monitoring, and tracing, to troubleshoot and debug cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Deployment Strategies: The practice of using Kubernetes and its ecosystem of tools, such as blue-green, canary, and rolling updates, to manage the deployment of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Deployment Strategies: The practice of using Kubernetes and its ecosystem of tools, such as rolling update, blue-green, and canary, to manage and automate the deployment strategies for a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Deployment: A higher-level Kubernetes object that manages the creation and updates of replicasets and pods in a declarative way.
Cloud-based Cloud-native Kubernetes Deployment: The practice of using Kubernetes and its ecosystem of tools, such as deployments, stateful sets, and continuous delivery, to deploy and manage cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Deployments: The practice of using Kubernetes Deployments to manage the scaling and rollouts of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Disaster recovery: The practice of using Kubernetes and its ecosystem of tools, such as replication controllers, backup operators and disaster recovery solutions, to ensure the availability and recoverability of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Downward API: The practice of using Kubernetes Downward API to access the metadata and status of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Edge and IoT: The practice of using Kubernetes and its ecosystem of tools, such as k3s, to manage and automate the deployment and management of cloud-native applications and services in edge and IoT environments.
Cloud-based Cloud-native Kubernetes Egress: The practice of using Kubernetes and its ecosystem of tools, such as egress controllers, to manage and automate the egress traffic of a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Endpoints: A higher-level Kubernetes object that represents a set of IP addresses and ports of pods backing a Service.
Cloud-based Cloud-native Kubernetes Event-Driven Architecture: The practice of using Kubernetes and its ecosystem of tools, such as Kafka, RabbitMQ and NATS, to build and manage event-driven architecture for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Event-Driven Automation: The practice of using Kubernetes and its ecosystem of tools, such as event-driven controllers, to automate the management and operations of a Kubernetes cluster based on events.
Cloud-based Cloud-native Kubernetes Federation: The practice of using Kubernetes and its ecosystem of tools, such as federation v2, to manage and automate the deployment and management of multiple Kubernetes clusters across different regions and clouds.
Cloud-based Cloud-native Kubernetes Federation: The practice of using Kubernetes Federation to manage and orchestrate multiple Kubernetes clusters as a single logical cluster.
Cloud-based Cloud-native Kubernetes Governance Automation: The practice of using Kubernetes and its ecosystem of tools, such as GitOps, to automate the governance and management of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Governance: The practice of using Kubernetes and its ecosystem of tools, such as role-based access control, admission controllers, and multi-tenancy, to manage and enforce policies and standards for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Health Checking: The practice of using Kubernetes and its ecosystem of tools, such as liveness and readiness probes, to check the health and availability of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Helm : The practice of using Helm, a package manager for Kubernetes, to automate the installation, configuration, and management of Kubernetes applications and services.
Cloud-based Cloud-native Kubernetes High Availability: The practice of using Kubernetes and its ecosystem of tools, such as etcd, HA proxies and load balancers, to ensure the availability and reliability of a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Horizontal Pod Autoscaler (HPA): A Kubernetes controller that automatically scales the number of replicas of a pod based on CPU or memory utilization.
Cloud-based Cloud-native Kubernetes Hybrid and Multi-cloud: The practice of using Kubernetes and its ecosystem of tools, such as kubefed, to manage and automate the deployment and management of cloud-native applications and services across multiple clouds and environments.
Cloud-based Cloud-native Kubernetes Infrastructure: The practice of using Kubernetes and its ecosystem of tools, such as virtualization, containerization, and orchestration, to manage and automate the infrastructure of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Ingress: A higher-level Kubernetes object that provides external access to the services in a cluster, typically via HTTP.
Cloud-based Cloud-native Kubernetes Ingress: The practice of using Kubernetes and its ecosystem of tools, such as Ingress controllers, to manage and automate the ingress traffic of a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Ingress: The practice of using Kubernetes Ingress to expose and route external traffic to cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Integration: The practice of using Kubernetes and its ecosystem of tools, such as service meshes, ingress controllers, and API gateways, to integrate and connect cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Job and CronJob: A higher-level Kubernetes object that represents a single or recurring batch job that runs to completion.
Cloud-based Cloud-native Kubernetes Jobs: The practice of using Kubernetes Jobs to manage the scaling and rollouts of batch-like cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Management Platform : The practice of using Kubernetes Management Platform, a platform to manage and operate Kubernetes clusters, to automate the management and operations of a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Metrics and Analytics: The practice of using Kubernetes and its ecosystem of tools, such as Prometheus, Grafana, Elasticsearch and Kibana, to collect, analyze and visualize metrics for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Monitoring and Logging: The practice of using Kubernetes and its ecosystem of tools, such as Prometheus, Grafana, Elasticsearch and Kibana, to collect, analyze and visualize metrics and logs for a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Monitoring and Logging: The practice of using Kubernetes and its ecosystem of tools, such as Prometheus, Grafana, Elasticsearch, and Kibana, to monitor and log the performance and behavior of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Multi-Cluster Management: The practice of using Kubernetes and its ecosystem of tools, such as federation v2, to manage and automate multiple clusters in different regions, availability zones or clouds.
Cloud-based Cloud-native Kubernetes Multi-cluster: The practice of using multiple Kubernetes clusters for different purposes, such as development, staging, and production, or for different regions or teams.
Cloud-based Cloud-native Kubernetes Multi-tenancy: The practice of using Kubernetes and its ecosystem of tools, such as namespaces, network policies, and role-based access control, to provide multi-tenancy and isolation for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Multi-Tenancy: The practice of using Kubernetes and its ecosystem of tools, such as namespaces, role-based access control, to support multiple tenants and isolate resources in a shared cluster.
Cloud-based Cloud-native Kubernetes Namespace: A virtual cluster, used to divide a cluster into multiple virtual clusters sharing the same physical cluster.
Cloud-based Cloud-native Kubernetes Namespaces: The practice of using Kubernetes Namespaces to isolate and organize cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Namespaces: The practice of using Kubernetes Namespaces to logically isolate and organize resources in a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Network Policies: The practice of using Kubernetes Network Policies to define and enforce network-level access controls for pods and services in a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Network Policies: The practice of using Kubernetes Network Policies to secure and manage network communication for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Network Segmentation: The practice of using Kubernetes and its ecosystem of tools, such as network policies, service meshes, and ingress controllers, to segment and isolate the network traffic in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Networking: The practice of using Kubernetes and its ecosystem of tools, such as Calico, Flannel, and CNI plugins, to manage and automate the networking of a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Networking: The practice of using Kubernetes and its ecosystem of tools, such as service meshes, ingress controllers, and network policies, to manage and automate the networking of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Node Security: The practice of using Kubernetes and its ecosystem of tools, such as pod security policies, network policies, and admission controllers, to secure the execution of nodes in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Node: The worker machine in a Kubernetes cluster, running the kubelet and container runtime, and responsible for running pods and containers.
Cloud-based Cloud-native Kubernetes Operations (K8sOps): The practice of using Kubernetes and its ecosystem of tools, such as Prometheus, Grafana, Elasticsearch and Kibana, to operate and manage a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Operations: The practice of using Kubernetes and its ecosystem of tools, such as monitoring, logging, and scaling, to manage and automate the operations of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Operator: A Kubernetes controller that automates the management and operations of a specific type of application or service on a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Operators : The practice of using Kubernetes Operators, a pattern for building and managing Kubernetes-native applications, to automate the operational tasks of a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Operators: The practice of using Kubernetes Operators to automate the management and scaling of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Optimization: The practice of using Kubernetes and its ecosystem of tools, such as resource quotas, limits, and autoscaling, to optimize the performance and cost of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Performance: The practice of using Kubernetes and its ecosystem of tools, such as monitoring, logging, and scaling, to optimize and measure the performance of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Persistent Volume (PV) and Persistent Volume Claim (PVC): Kubernetes objects used to manage persistent storage for pods and other Kubernetes resources.
Cloud-based Cloud-native Kubernetes Persistent Volumes: The practice of using Kubernetes Persistent Volumes to manage persistent storage for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Platform Hardening: The practice of using Kubernetes and its ecosystem of tools, such as network policies, pod security policies, and admission controllers, to harden the security of the Kubernetes platform itself.
Cloud-based Cloud-native Kubernetes Pod Disruption Budget: The practice of using Kubernetes and its ecosystem of tools, such as Pod Disruption Budget, to define and enforce policies for pod availability during updates, evictions, and failures in a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Pod Lifecycle: The practice of using Kubernetes Pod Lifecycle to manage the lifecycle of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Pod Preset: A Kubernetes object that allows to inject environment variables, command-line arguments, and other configuration data into pods at runtime.
Cloud-based Cloud-native Kubernetes Pod Preset: The practice of using Kubernetes Pod Preset to manage the configuration of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Pod Priority and Preemption: The practice of using Kubernetes Pod Priority and Preemption to manage the resource allocation for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Pod Security Policy: The practice of using Kubernetes Pod Security Policy to define and enforce security policies for pods and their containers in a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Pod Security: The practice of using Kubernetes and its ecosystem of tools, such as pod security policies, network policies, and security contexts, to secure the execution of pods in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Pod: The smallest and simplest unit in the Kubernetes object model, representing a single process or container running on a node.
Cloud-based Cloud-native Kubernetes PodDisruptionBudget: The practice of using Kubernetes PodDisruptionBudget to ensure the availability of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes PodSecurityPolicy: The practice of using Kubernetes PodSecurityPolicy to secure the execution of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes ReplicaSet: A higher-level Kubernetes object that ensures a specified number of replicas of a pod are running at any given time, unlike Replication Controller which is now Deprecated.
Cloud-based Cloud-native Kubernetes Replication Controller: A higher-level Kubernetes object that ensures a specified number of replicas of a pod are running at any given time.
Cloud-based Cloud-native Kubernetes Resilience: The practice of using Kubernetes and its ecosystem of tools, such as pod disruption budgets, health checking, and disaster recovery, to ensure the availability and recoverability of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Resource Management: The practice of using Kubernetes and its ecosystem of tools, such as resource quotas, limits, and requests, to manage and allocate resources for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Resources Management: The practice of using Kubernetes and its ecosystem of tools, such as resource quotas, limits, and HPA, to manage and optimize the resources of a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Role-Based Access Control (RBAC): The practice of using Kubernetes RBAC to manage access control for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Role-based Access Control (RBAC): The practice of using Kubernetes Role-based Access Control to define and enforce access policies for users and service accounts in a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Rollout and Rollback: The practice of using Kubernetes and its ecosystem of tools, such as deployments, stateful sets, and continuous delivery, to manage and automate the rollout and rollback of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Scalability: The practice of using Kubernetes and its ecosystem of tools, such as horizontal pod autoscaler, to scale a Kubernetes cluster and its resources horizontally.
Cloud-based Cloud-native Kubernetes Scale and Auto-scaling: The practice of using Kubernetes and its ecosystem of tools, such as deployments, stateful sets, and horizontal pod autoscaling, to manage and automate the scaling of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Secrets: The practice of using Kubernetes Secrets to securely store sensitive information for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Security scanning: The practice of using Kubernetes and its ecosystem of tools, such as SAST,DAST,IAST, to scan and identify vulnerabilities and misconfigurations in cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Security: The practice of using Kubernetes and its ecosystem of tools, such as network policies, pod security policies, and admission controllers, to secure and protect a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Security: The practice of using Kubernetes and its ecosystem of tools, such as network policies, pod security policies, and admission controllers, to secure and protect cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Self-Healing: The practice of using Kubernetes and its ecosystem of tools, such as liveness and readiness probes, to automatically detect and recover from failures in a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Service Accounts: The practice of using Kubernetes Service Accounts to manage authentication and authorization for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Service Catalog: The practice of using Kubernetes and its ecosystem of tools, such as Service Catalog, to manage and automate the provisioning and binding of services for a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Service Discovery: The practice of using Kubernetes and its ecosystem of tools, such as services, ingress controllers, and service meshes, to discover and connect to cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Service Mesh: A configurable infrastructure layer for microservices application that makes communication flexible, reliable, and fast.
Cloud-based Cloud-native Kubernetes Service Mesh: The practice of using Kubernetes and its ecosystem of tools, such as Istio, Envoy, and Linkerd, to manage and secure the communication between microservices in a cloud-native environment.
Cloud-based Cloud-native Kubernetes Service Mesh: The practice of using Kubernetes and its ecosystem of tools, such as Istio, Linkerd and Envoy, to manage and automate service-to-service communication and traffic management in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Service: A higher-level Kubernetes object that defines a stable endpoint for pods and provides load balancing and service discovery.
Cloud-based Cloud-native Kubernetes Services: The practice of using Kubernetes Services to provide stable network connections and load balancing for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Sidecar: A container that runs alongside the main container in a pod and provides additional functionality such as logging, monitoring, and service discovery.
Cloud-based Cloud-native Kubernetes Statefulset: A higher-level Kubernetes object that manages the creation and updates of pods and their associated storage in a stateful way.
Cloud-based Cloud-native Kubernetes StatefulSets: The practice of using Kubernetes StatefulSets to manage the scaling and rollouts of stateful cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Storage Management: The practice of using Kubernetes and its ecosystem of tools, such as dynamic provisioning, storage classes, and storage operators, to manage and automate the storage of a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Storage: The practice of using Kubernetes and its ecosystem of tools, such as dynamic provisioning, storage classes, and storage operators, to manage and automate the storage of cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Testing: The practice of using Kubernetes and its ecosystem of tools, such as test-driven development, continuous integration, and end-to-end testing, to test and validate cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Upgrade and Migration: The practice of using Kubernetes and its ecosystem of tools, such as kubeadm, kops, and kubicorn, to upgrade and migrate a Kubernetes cluster.
Cloud-based Cloud-native Kubernetes Volume: The practice of using Kubernetes Volume to manage the storage of data for cloud-native applications and services in a Kubernetes environment.
Cloud-based Cloud-native Kubernetes Webhook: A Kubernetes feature that allows to call external service for validation and mutation of Kubernetes resources before they are accepted by the API server.
Cloud-based Cloud-native Kubernetes: The practice of using Kubernetes as a platform for building and deploying cloud-native applications and services in a cloud environment.
Cloud-based Cloud-native Kubernetes-based PaaS : The practice of using Kubernetes as the underlying platform for a Platform-as-a-Service (PaaS) to provide a platform for cloud-native applications and services.
Cloud-based Cloud-native logging as code: The practice of automating the collection and analysis of logs from cloud-native infrastructure and applications in a cloud environment, using tools such as Elasticsearch and Kibana.
Cloud-based Cloud-native logging: The practice of collecting, analyzing, and visualizing log data from cloud-native applications and services in a cloud environment, using tools such as Elasticsearch and Kibana.
Cloud-based Cloud-native management: The practice of managing cloud-native applications and services in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native Microservices: The practice of building and deploying cloud-native applications and services as a set of loosely coupled and independently deployable services in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native monitoring as code: The practice of automating the monitoring of cloud-native infrastructure and applications in a cloud environment, using tools such as Prometheus and Grafana.
Cloud-based Cloud-native monitoring: The practice of monitoring cloud-native applications and services in a cloud environment, using tools such as Prometheus and Grafana.
Cloud-based Cloud-native network as code: The practice of automating the configuration and management of networking infrastructure for cloud-native infrastructure and applications in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native networking: The practice of configuring and managing the networking infrastructure for cloud-native applications and services in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native observability: The practice of understanding the behavior and performance of cloud-native applications and services in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native operations: The practice of managing cloud-native applications and services, using technologies such as Kubernetes, Envoy, and Istio.
Cloud-based Cloud-native Prometheus: The practice of using Prometheus as a monitoring and alerting system for cloud-native applications and services in a cloud environment.
Cloud-based Cloud-native scaling: The practice of scaling cloud-native applications and services in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native security as code: The practice of automating the implementation and enforcement of security policies for cloud-native infrastructure and applications in a cloud environment, using tools such as HashiCorp Vault and AWS IAM.
Cloud-based Cloud-native security: The practice of securing cloud-native applications and services in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native security: The practice of securing cloud-native applications and services, using technologies such as Kubernetes, Envoy, and Istio.
Cloud-based Cloud-native Serverless as code: The practice of automating the build and deployment of cloud-native applications and services using a serverless computing model in a cloud environment, using tools such as AWS Lambda and Google Cloud Functions.
Cloud-based Cloud-native serverless: The practice of building and deploying cloud-native applications and services using a serverless computing model in a cloud environment, using tools such as AWS Lambda and Google Cloud Functions.
Cloud-based Cloud-native Service discovery as code: The practice of automating the discovery and registration of services in a cloud-native environment, using tools such as Consul and Eureka.
Cloud-based Cloud-native service discovery: The practice of discovering and registering services in a cloud-native environment, using tools such as Consul and Eureka.
Cloud-based Cloud-native Service Mesh as code: The practice of automating the management and security of service communication in a cloud-native environment, using tools such as Istio and Linkerd.
Cloud-based Cloud-native service mesh: The practice of managing and securing the communication between microservices in a cloud-native environment, using tools such as Istio and Linkerd.
Cloud-based Cloud-native Service orchestration as code: The practice of automating the management and scaling of services in a cloud-native environment, using tools such as Kubernetes and Mesos.
Cloud-based Cloud-native service orchestration: The practice of managing and scaling services in a cloud-native environment, using tools such as Kubernetes and Mesos.
Cloud-based Cloud-native storage as code: The practice of automating the configuration and management of storage for cloud-native infrastructure and applications in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native storage: The practice of configuring and managing storage for cloud-native applications and services in a cloud environment, using tools such as Kubernetes and Istio.
Cloud-based Cloud-native Terraform: The practice of using Terraform as an infrastructure as code (IaC) tool for provisioning and managing cloud-native infrastructure in a cloud environment.
Cloud-based Cloud-native testing: The practice of testing cloud-native applications and services in a cloud environment.
Cloud-based Collaboration: the practice of allowing multiple users to work on the same document, project, or task in a cloud environment, using tools such as Google Docs and Microsoft Teams.
Cloud-based Compliance Automation: the practice of automating compliance-related tasks such as monitoring, reporting, and remediation in a cloud environment.
Cloud-based Compliance Management: the practice of ensuring that an organization’s use of cloud-based resources and services complies with relevant laws, regulations, and industry standards.
Cloud-based Computer Vision: the practice of processing and understanding images and videos in a cloud environment, using object detection and facial recognition technologies.
Cloud-based Content Management System (CMS): a system that allows to create, manage, and publish digital content in a cloud environment, using tools such as WordPress and Drupal.
Cloud-based Cost Management: the practice of monitoring, controlling and optimizing the costs associated with the use of cloud-based resources and services.
Cloud-based Cost Optimization: the process of reducing the cost of using cloud-based resources and services without compromising on performance or availability.
Cloud-based Data Analytics: the practice of collecting, analyzing, and visualizing data in a cloud environment to gain insights and make decisions.
Cloud-based Data Archiving: the practice of storing and preserving data in a cloud environment for long-term retention.
Cloud-based Data Backup: the practice of creating and maintaining backups of data in a cloud environment.
Cloud-based Data Encryption: the practice of encrypting data at rest and in transit in a cloud environment.
Cloud-based Data Governance as a Service (DGaaS): a cloud-based service that allows to manage and govern the use of data in a cloud environment.
Cloud-based Data Governance: the practice of managing and controlling the use of data in a cloud environment, including tasks such as classification, retention, and access control.
Cloud-based Data Management Platform (DMP): a platform that allows to manage and govern the use of data in a cloud environment.
Cloud-based Data Management: the practice of managing and storing data in a cloud environment, including tasks such as backup, recovery, and archiving.
Cloud-based Data Recovery: the practice of recovering data in a cloud environment in the event of a disaster or outage.
Cloud-based Data Warehousing: the practice of storing and managing large amounts of data in a cloud environment for reporting and analytics.
Cloud-based Database Management: the practice of managing and maintaining databases in a cloud environment.
Cloud-based Digital Twin: the practice of creating a virtual representation of a physical asset in a cloud environment, allowing to simulate and analyze its behavior.
Cloud-based e-commerce platform: a platform that allows to create, manage and deploy e-commerce websites and applications in a cloud environment, using tools such as Magento and Shopify.
Cloud-based Edge Computing: The practice of processing data and running applications at the edge of a network, closer to the source of data, rather than in a centralized data center or cloud.
Cloud-based Encryption and Key Management: the practice of encrypting data at rest and in transit in a cloud environment and managing the encryption keys.
Cloud-based Event-Driven Architecture (EDA): An architectural pattern in which an application reacts to specific events or changes in the environment, such as a message, a change in data or a user action, rather than being triggered by a request.
Cloud-based Function-as-a-Service (FaaS): A cloud-based service that allows to run code in response to specific events, without having to manage the underlying servers.
Cloud-based Gaming: the practice of building and deploying games in a cloud environment, where the game logic and data are processed in the cloud and the game is streamed to the player.
Cloud-based Identity and Access Management (IAM): the practice of controlling access to cloud-based resources and services based on user identities and roles.
Cloud-based Incident Management: the practice of identifying, responding to and resolving incidents in a cloud environment.
Cloud-based Industrial IoT (IIoT): the practice of connecting and managing industrial equipment and machinery in a cloud environment.
Cloud-based Infrastructure Management: the practice of managing and maintaining the underlying infrastructure of a cloud environment, including tasks such as provisioning, scaling, and monitoring.
Cloud-based Internet of Things (IoT) Platforms: Platforms that allow to connect and manage IoT devices in a cloud environment, where data is collected, processed, and stored in the cloud.
Cloud-based Internet of Things (IoT): the practice of connecting and managing IoT devices in a cloud environment, where data is collected, processed and stored in the cloud.
Cloud-based IoT Security: the practice of securing IoT devices and the data they generate in a cloud environment.
Cloud-based Master Data Management (MDM): the practice of managing and maintaining a consistent and accurate view of critical data in a cloud environment.
Cloud-based Media Processing: the practice of processing, transcoding, and delivering media files in a cloud environment, using technologies such as ffmpeg and AWS Elastic Transcoder.
Cloud-based Mobile App Development: the practice of building and deploying mobile applications in a cloud environment, using cloud-based services and platforms.
Cloud-based Mobile Backend as a Service (MBaaS): a cloud-based service that allows to create and manage the backend services for mobile applications, such as user management, data storage, and push notifications.
Cloud-based Monitoring and Logging: the practice of collecting, analyzing, and visualizing data about the performance, availability, and usage of cloud-based resources and services.
Cloud-based Multi-cloud: The practice of using multiple cloud service providers for different applications and services, in order to reduce vendor lock-in, increase flexibility and optimize costs.
Cloud-based Natural Language Processing (NLP): the practice of processing and understanding human language in a cloud environment, using technologies such as sentiment analysis, text summarization, and language translation.
Cloud-based Network Management: the practice of managing and maintaining network infrastructure in a cloud environment.
Cloud-based Network Security: the practice of securing a cloud environment’s network infrastructure, such as firewalls, VPNs, and intrusion detection/prevention systems.
Cloud-based Performance Optimization: the practice of optimizing the performance of cloud-based resources and services in a cloud environment.
Cloud-based Predictive Maintenance: the practice of using data and analytics to predict when equipment or machinery will fail in a cloud environment, allowing to plan maintenance and prevent downtime.
Cloud-based Proximity Marketing: the practice of using data and analytics to deliver targeted marketing messages to customers based on their location in a cloud environment.
Cloud-based Quantum Computing: the practice of building and deploying quantum computing applications in a cloud environment.
Cloud-based Remote Workforce Management: the practice of managing and supporting a remote workforce in a cloud environment, including tasks such as communication, collaboration, and security.
Cloud-based Risk Management: the practice of identifying, assessing and mitigating risks associated with the use of cloud-based resources and services.
Cloud-based Robotics: the practice of building and deploying robots in a cloud environment, that can interact with their environment, process sensor data and complete tasks autonomously.
Cloud-based Security Operations Center (SOC): the practice of centralizing security-related operations, such as monitoring, incident response, and threat intelligence, in a cloud environment.
Cloud-based Serverless Computing: The practice of building and running applications and services without having to manage the underlying servers, scaling automatically based on demand.
Cloud-based Service Level Management: the practice of ensuring that the service level agreements (SLAs) for cloud-based resources and services are met.
Cloud-based Service Management: the practice of managing and maintaining cloud-based services, including tasks such as provisioning, scaling, and monitoring.
Cloud-based Smart City: the practice of using data and analytics to improve the quality of life in a city by optimizing the use of resources such as energy, transportation, and public services in a cloud environment.
Cloud-based Smart Home: the practice of using data and analytics to improve the quality of life in a home by optimizing the use of resources such as energy, security, and comfort in a cloud environment.
Cloud-based Smart Retail: the practice of using data and analytics to improve the retail experience by optimizing the use of resources such as inventory, customer service, and marketing in a cloud environment.
Cloud-based Storage Management: the practice of managing and maintaining storage in a cloud environment, including tasks such as backup, recovery, and archiving.
Cloud-based Streaming Data Processing: the practice of processing large amounts of streaming data in a cloud environment using technologies such as Apache Kafka and Apache Storm.
Cloud-based Time Series Data Processing: the practice of processing and analyzing time-series data in a cloud environment, using technologies such as InfluxDB and Grafana.
Cloud-based Virtual Reality (VR) and Augmented Reality (AR): the practice of building and deploying VR and AR applications and services in a cloud environment.
Cloud-based Vulnerability Management: the practice of identifying, assessing, and mitigating security vulnerabilities in cloud-based resources and services.
Cloud-based Website Development: the practice of building and deploying websites in a cloud environment, using cloud-based services and platforms.
Cloud-Native Governance: the practice of managing and controlling the use of cloud-native technologies and environments in an organization.
Cloud-Native Monitoring: the practice of monitoring cloud-native applications and environments, such as Kubernetes and Docker.
Cloud-Native Security: the practice of securing cloud-native applications and environments, such as Kubernetes and Docker.
Cloud-Native: the practice of building and running applications using cloud-specific technologies and architectures that are optimized for the cloud.
Code review: the process of reviewing code written by other developers to ensure quality, maintainability, and compliance with coding standards.
Code smell: a symptom of poor design or bad code practices in a software program.
Codebase: the set of all files and directories that make up a software program.
Computer Vision (CV): a subfield of AI that deals with the processing and understanding of visual information, such as images and videos.
Containerization: a method of packaging and deploying software so that it runs consistently across different environments.
Continuous delivery: a software development practice where code changes are automatically deployed to production after passing through a series of testing and validation stages.
Continuous integration: a software development practice where developers regularly integrate their code changes into a shared repository, with automated testing and building processes to ensure code quality.
Cryptography: the practice of securing communication and information by transforming it into a form that is unreadable to unauthorized parties.
Cybersecurity: the practice of protecting networks, systems, and devices from unauthorized access, use, disclosure, disruption, modification, or destruction.
Data annotation: the process of adding labels or tags to data samples, typically used in supervised learning to train machine learning models.
Data anonymization: the process of removing or obscuring personal information from a dataset to protect the privacy of individuals.
Data augmentation: the process of creating new data samples by applying various transformations to existing samples.
Data cleaning: the process of identifying and removing errors, inconsistencies, and duplicate data from a dataset.
Data Engineering: the practice of designing, building, and maintaining the infrastructure and tools needed to store, process and analyze large data sets.
Data Governance: the practice of managing and controlling the data, who can access it, how it is used, and how it is protected.
Data lineage: the process of tracking the origin and movement of data within an organization.
Data Mining: the process of discovering patterns and relationships in large data sets using techniques from machine learning and statistics.
Data preprocessing: the process of preparing a dataset for analysis or modeling, including tasks such as cleaning, normalization, and feature extraction.
Data privacy: the practice of protecting the personal information of individuals from unauthorized access and use.
Data Science: the practice of extracting insights and knowledge from data using a combination of mathematical and computational methods, statistics and machine learning.
Data Visualization: the practice of creating visual representations of data, such as charts and graphs, to help understand and communicate insights and patterns in the data.
Data Warehouse: a large collection of data that is optimized for reporting and analysis, typically used to store historical data.
Database: a collection of data that is organized and stored in a structured way, often used to store information for software programs.
Dataset bias: the phenomenon where a machine learning model is trained on a dataset that is not representative of the real-world population, leading to inaccurate or biased predictions.
Debugging: the process of finding and fixing errors in a software program.
Deep Learning (DL): a subset of ML that deals with the development of neural networks, which are algorithms that are inspired by the structure and function of the human brain.
Dependency: a component or library that a software program relies on in order to function.
Deployment pipeline: a series of stages that code changes go through before being deployed to production.
Deployment: the process of making a new version of an application available to users.
Design patterns: a set of solutions to common software design problems, that can be reused in different situations.
Development Terminology Definitions
DevOps: the practice of combining development and operations teams to improve the speed and quality of software delivery.
Docker: a platform for developing, shipping, and running applications in containers.
Edge computing: a distributed computing paradigm that brings data storage, processing and other functionalities closer to the sources of data, such as sensors or IoT devices.
End-to-end testing: a type of testing that verifies that a software program can perform its functions from start to finish, including all components, interfaces, and external systems.
Exception handling: the process of anticipating and handling errors and exceptions that may occur in a software program.
Explainable AI (XAI): a subfield of AI that aims to make machine learning models more transparent and interpretable for users.
Fairness in AI: the practice of developing machine learning models that are fair and unbiased towards different groups of people.
Feature: a specific function or capability of a software program.
Frontend development: the work of creating and maintaining the client-side of an application, including the user interface and interactions.
Full stack development: the work of creating and maintaining both the frontend and backend of an application.
Functional testing: a type of testing that verifies that a software program performs as intended.
Generative Adversarial Networks (GANs): a type of deep learning model that consists of two neural networks, one that generates new data and the other that tries to distinguish between real and generated data.
Generative models: a subset of machine learning that deals with the generation of new data or content based on a given set of inputs.
Git: a distributed version control system that allows multiple developers to work on the same codebase at the same time.
GitHub: a web-based platform for version control and collaboration that uses Git.
Graph database: a type of database that stores data in the form of nodes and edges, which can be used to represent relationships and connections between data.
Hyperparameter tuning: the process of selecting the best set of hyperparameters for a machine learning model by trying different combinations and evaluating their performance.
Integration testing: a type of testing that verifies that different components of a software program work together as expected.
Integration: the process of combining different components or systems into a cohesive whole.
Internet of Things (IoT): the network of physical devices, vehicles, buildings, and other objects that are embedded with sensors, software, and network connectivity, which enables them to collect and exchange data.
Iteration: a single cycle of development, typically consisting of planning, design, development, testing, and deployment.
Jenkins: an open-source automation server that can be used to automate tasks such as building, testing, and deploying software.
Kanban: a method of visualizing and managing the flow of work, typically represented by a board with columns for different stages of development.
Kubernetes: an open-source container orchestration system that automates the deployment, scaling, and management of containerized applications.
Linting: the process of automatically checking code for compliance with coding standards and best practices.
Load testing: a type of testing that measures the performance of a software program under a heavy load.
Logic error: an error in a software program that causes it to produce incorrect results without crashing.
Machine Learning (ML): a subset of AI that deals with the development of algorithms and models that can learn from data and improve their performance over time.
Microservices: a software architecture pattern where a single application is broken down into a collection of small, loosely-coupled services.
Mocking: the process of creating fake objects or services in order to test a software program’s behavior and interactions with other components or systems.
Model interpretability: the ability to understand and explain the decisions made by a machine learning model.
MVP (Minimum Viable Product): a version of a product with just enough features to satisfy early customers and provide feedback for future development.
Natural Language Generation (NLG): a subfield of NLP that deals with the generation of human-like text based on structured data and rules.
Natural Language Processing (NLP): a subfield of AI that deals with the processing and understanding of natural language, such as text and speech.
Neural Style Transfer: a deep learning technique that allows to transfer the style of an image to another image.
NoSQL: a term used to describe non-relational databases that do not use the traditional table-based structure of relational databases.
Object-oriented design (OOD): the process of designing software using objects and classes.
Object-oriented programming (OOP): a programming paradigm that uses objects, which are instances of classes, to represent and manipulate data.
Object-relational mapping (ORM): a technique that allows a software program to interact with a relational database using objects and classes, rather than SQL.
Pair programming: a software development practice where two developers work together on a single codebase, with one typing and the other reviewing and offering suggestions.
Pairing: a method of working on a project together, where two people work on the same task at the same time.
Performance optimization: the process of improving the performance and efficiency of a software program.
Performance testing: a type of testing that measures the performance and scalability of an application under various loads and conditions.
Performance testing: a type of testing that verifies that a software program can perform its functions quickly and efficiently under various conditions and loads.
Predictive modeling: the practice of using statistical and machine learning techniques to predict future events or outcomes based on historical data.
Product backlog: a prioritized list of features and requirements for a product, used in agile development.
Product owner: a role in agile development responsible for defining and prioritizing the features and requirements of a product.
Profiling: the process of analyzing the performance and resource usage of a software program to identify and optimize bottlenecks.
Progressive enhancement: a method of web development where basic functionality is provided for all users, with additional features and enhancements added for users with more capable devices and browsers.
Pull request: a request to merge code changes into a shared repository, typically accompanied by comments and feedback from other developers.
Quality assurance (QA): the practice of verifying that a product meets a set of quality standards.
Quantum computing: a type of computing that uses the properties of quantum mechanics, such as superposition and entanglement, to perform operations on data.
Recommender Systems: a subset of AI that deals with the development of algorithms and models that can make personalized recommendations to users based on their preferences and behavior.
Refactoring: the process of changing the structure of existing code without modifying its behavior, to improve readability, maintainability, and performance.
Regression testing: a type of testing that verifies that changes to a software program have not introduced new bugs or broken existing functionality.
Reinforcement Learning (RL): a subfield of ML that deals with the development of algorithms and models that can learn by interacting with an environment and receiving feedback in the form of rewards or penalties.
Responsive design: a method of web design where the layout and functionality of a website adapts to the size and capabilities of the user’s device.
REST (Representational State Transfer): an architectural style for building web services that use a standardized set of methods and conventions for interacting with resources over the internet.
Robotics: the branch of engineering that deals with the design, construction, and operation of robots.
S s
Scaling: the process of increasing the capacity and performance of an application to handle more users or data.
Scrum: a specific implementation of agile development, with roles such as Product Owner, Scrum Master, and Development Team, and ceremonies such as Sprint Planning, Daily Scrum, Sprint Review, and Sprint Retrospective.
Security testing: a type of testing that verifies that a software program is secure against unauthorized access and data breaches.
Security: the practice of protecting a software program and its data from unauthorized access, misuse, and attacks.
Sentiment Analysis: the process of determining the emotional tone or opinion expressed in a piece of text.
Serverless computing: a cloud computing paradigm where the cloud provider is responsible for allocating and managing the servers required to run an application, and the user only pays for the resources used.
Service-oriented architecture (SOA): a software architecture pattern where a system is composed of a collection of independent services that communicate with each other over a network.
Smoke testing: a type of testing that verifies that a software program can perform its most basic functions without crashing.
Speech Recognition: the process of converting spoken language into text.
Speech Synthesis: the process of converting text into spoken language.
Stack: a set of technologies and tools used to build a particular application or service.
Story points: a method of estimating the relative size and complexity of a task or feature, used in agile development.
Streaming analytics: the process of analyzing and processing streaming data in real-time to extract insights and make decisions.
Streaming data: a type of data that is generated in real-time and often used in fields such as social media, finance, and IoT.
Stress testing: a type of testing that verifies that a software program can handle unexpected and extreme loads without crashing.
Syntax error: an error in a software program that is caused by incorrect use of the programming language’s grammar or syntax.
System testing: a type of testing that verifies that a software program can perform its functions in a real-world environment, including all hardware, software, and network components.
Test case: a set of inputs, expected outputs, and instructions for testing a specific aspect of a software program.
Test coverage: the percentage of the codebase that is covered by tests.
Test harness: a set of tools and infrastructure for running and managing tests.
Test script: a set of instructions for performing a test or series of tests.
Test suite: a set of tests that are run together to verify the functionality of a software program.
Test-driven development (TDD): a software development practice where developers write tests for new code before writing the code itself, to ensure that the code meets the requirements and behaves as expected.
Test-driven development: a software development practice where developers write tests for new code before writing the code itself, to ensure that the code meets the requirements and behaves as expected.
Text Mining: the process of extracting useful information from unstructured text data, such as social media posts, reviews, and articles.
Time series analysis: the process of analyzing and modeling time series data to extract insights and make predictions.
Time series data: a type of data that is collected over time and often used in fields such as finance, economics, and weather forecasting.
Transfer Learning: a technique where a model that has been trained on one task is used as a starting point for a model on a new task.
UML (Unified Modeling Language): a standard language and notation for modeling software systems and their components, such as class diagrams and state diagrams.
Unit testing: a type of testing that verifies that individual units of code, such as functions or methods, work as intended.
User acceptance testing (UAT): a type of testing that verifies that a software program meets the needs and expectations of its intended users.
User story: a description of a feature or requirement from the perspective of an end user, used in agile development.
Version control: the practice of tracking and managing changes to a codebase over time.
Version: a specific release of a software program, typically identified by a number or date.
Versioning: the practice of keeping track of different versions of a software program and its components.
Virtualization: the creation of a virtual version of a computer or operating system, typically
Waterfall development: a traditional method of software development, with distinct phases such as requirements gathering, design, development, testing, and deployment, and little to no overlap between phases.
Web development: the work of creating and maintaining websites and web applications.
Web scraping: the process of automatically extracting data from websites.
Web service: a service or application that can be accessed over the internet using standard protocols and technologies, such as HTTP and XML.
Web socket: a protocol for two-way communication between a client and a server over a single, long-lived connection.
Webpack: a JavaScript module bundler that allows developers to manage and optimize the dependencies and assets of their web applications.
White box testing: a type of testing that examines the internal structure and logic of a software program.
YAML: a human-readable data serialization format that is often used for configuration files and data definition.
Zero-downtime deployment: a strategy for deploying new versions of a software program without interruption of service.