Cloud Computing Glossary

Central message hub for bi-directional communication between IoT applications and devices.

Tools for connecting and managing diverse IoT devices and data streams in cloud environments.

Cloud-based tools for managing IoT devices from deployment through retirement, ensuring efficient device administration.

Remote software updates for IoT devices managed through cloud platforms, ensuring device security and functionality.

Cloud-based platform for developing, managing, and integrating IoT applications and devices.

Lightweight communication protocols designed for IoT devices in cloud environments.

Cloud service for processing and acting on data from IoT devices based on predefined conditions.

Measures to protect IoT devices, data, and infrastructure in cloud-connected environments.

Real-time processing and analysis of data streams from IoT devices in the cloud.

Cloud service for creating and managing cryptographic keys for data encryption, ensuring secure data protection.

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

Kubernetes-native serverless framework enabling function deployment without managing underlying infrastructure.

Rules for controlling network traffic between pods in Kubernetes clusters, enhancing security in cloud-native environments.

Tools for assessing and improving security configurations in Kubernetes environments.

Managed Kubernetes platforms offered by major cloud providers for container orchestration.

Kubernetes native configuration management tool for customizing application configurations.

AWS service for running Lambda functions at edge locations to customize content delivery.

Template for EC2 instance settings used in Auto Scaling groups, defining instance type, AMI, and other parameters.

Security concept of granting only the minimum permissions necessary in cloud environments.

Managing software licenses efficiently to reduce costs in cloud environments, ensuring compliance and cost-effectiveness.

Migration strategy of moving applications to the cloud without significant modifications.

Minimalist virtual machines designed for fast startup and low resource usage in cloud environments.

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

Using liquid to dissipate heat in densely packed cloud data centers for improved efficiency.

Service that distributes incoming network traffic across multiple servers to ensure no single server becomes overwhelmed.

Process of collecting, analyzing, and visualizing log data from cloud resources for insights and troubleshooting.

AI-driven analysis of log data to identify patterns, anomalies, and insights in cloud environments.

Tools for building application integrations with minimal hand-coding in cloud environments.

Cloud-based tools for creating applications with little to no traditional programming.

Cloud services enabling app development with minimal coding, using visual interfaces and pre-built components.

Lightweight messaging protocol designed for IoT devices and low-bandwidth networks in cloud ecosystems.

Cloud-based services for building, training, and deploying machine learning models at scale.

Centralized management of critical business data entities in cloud environments.

Tools for developing AI models that can learn how to learn, improving adaptability in cloud environments.

Cloud tools for organizing, integrating, and governing metadata across various data sources.

Advanced antenna designs using engineered materials to enhance cloud network performance.

Gathering and storing quantitative data about cloud resource performance and usage.

Architectural style where frontend apps are composed from independently deployable components in the cloud.

Automating the process of dividing cloud networks into secure zones for enhanced protection.

Connecting and coordinating interactions between independent microservices in cloud architectures.

Strategic process of preparing and scheduling the move of applications and data to cloud environments.

Cloud-based platforms for developing and deploying mixed reality applications, combining virtual and physical environments.

Bringing cloud computing capabilities closer to mobile users to reduce latency and improve performance.

Extending cloud computing capabilities to the edge of mobile networks for lower latency.

Process of making machine learning models available for use in cloud production environments.

Techniques for understanding and interpreting decisions made by AI models in cloud environments.

Tracking the performance and behavior of deployed machine learning models in cloud environments.

Centralized repository for storing and managing machine learning models in cloud environments.

Process of making trained machine learning models available for real-time predictions in cloud environments.

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