HCIA

1. Management and Monitoring

1.1. Cloud Eye Azure Monitor

1.1.1. Use Cases

1.1.1.1. Real-time monitoring of cloud resources (e.g., ECS, RDS, OBS)

1.1.1.2. Setting alarms for critical metrics to detect anomalies early.

1.1.1.3. Triggering auto-scaling policies based on resource utilization thresholds.

1.1.2. Additional Features

1.1.2.1. Multi-dimensional monitoring for CPU, memory, disk, and network metrics.

1.1.2.2. Customizable dashboards for visualizing key metrics across services.

1.1.2.3. Integration with Simple Message Notification (SMN) for real-time alerts via email or SMS

1.1.3. Management

1.1.3.1. Automatic activation after resource creation.

1.1.3.2. Flexible alarm rule configuration for multiple resources simultaneously.

1.1.3.3. Historical data analysis to identify trends and optimize performance

1.2. Cloud Trace Service (CTS) Azure Activity Log

1.2.1. Use Cases

1.2.1.1. Auditing resource operations for compliance and security purposes.

1.2.1.2. Tracking changes to cloud resources to troubleshoot issues or investigate incidents

1.2.1.3. Monitoring high-risk operations to prevent unauthorized access or misconfigurations

1.2.2. Additional Features

1.2.2.1. Logs all operations performed via the console, APIs, or system calls.

1.2.2.2. Encrypted storage of trace files for enhanced security.

1.2.2.3. Integration with Log Tank Service (LTS) for advanced log querying and analysis.

1.2.3. Management

1.2.3.1. Automatic creation of trackers to record operations.

1.2.3.2. Real-time notifications for key events via email or SMS.

1.3. O&M - Operation and Maintenance

1.3.1. Use Cases

1.3.1.1. Proactive Monitoring: Identificar e resolver problemas antes que afetem os usuários finais, garantindo alta disponibilidade dos serviços.

1.3.1.2. Performance Optimization: Monitorar métricas de desempenho para ajustar recursos e melhorar a eficiência operacional.

1.3.1.3. Rastrear e resolver falhas ou interrupções em tempo real para minimizar o impacto nos negócios.

1.3.1.4. Resource Governance: Gerenciar e organizar recursos na nuvem para evitar desperdícios e garantir conformidade

1.3.1.5. Audit and Compliance: Registrar e auditar todas as operações realizadas nos recursos para atender a requisitos legais e de segurança.

1.3.2. Additional Features

1.3.2.1. Unified Dashboard: Consolidação de métricas, logs e alarmes em um único painel para facilitar o gerenciamento. Personalização de visualizações com base nas necessidades do negócio.

1.3.2.2. Automation: Criação de políticas automatizadas para escalonamento, backups e notificações. Uso de templates (como ARM no Azure) para provisionamento consistente de recursos.

1.3.2.3. Integration with Other Services: Integração com serviços como Cloud Eye, CTS e TMS para gerenciamento centralizado. Notificações em tempo real via SMS, e-mail ou integrações com ferramentas externas.

1.3.3. Management

1.3.3.1. Monitoring Tools: Cloud Eye: Monitoramento em tempo real de métricas como CPU, memória, disco e rede. Cloud Trace Service (CTS): Registro detalhado das operações realizadas nos recursos.

1.3.3.2. Tag Management: Organização de recursos por tags para facilitar a alocação de custos e o gerenciamento. Identificação rápida de recursos não utilizados ou mal configurados.

1.3.3.3. Policy Enforcement: Configuração de regras de conformidade para garantir que todos os recursos sigam padrões organizacionais. Controle granular de acesso usando IAM (Identity and Access Management).

1.4. Tag Management Service (TMS) Azure Resource Manager (ARM) Templates

1.4.1. Use Cases

1.4.1.1. Organizing and categorizing cloud resources using tags for easier management

1.4.1.2. Simplifying cost allocation by tagging resources by department or project.

1.4.1.3. Enforcing governance policies by identifying untagged or incorrectly tagged resources.

1.4.2. Additional Features

1.4.2.1. Bulk tagging of resources across multiple services.

1.4.2.2. Support for custom tags with key-value pairs.

1.4.3. Management

1.4.3.1. Centralized dashboard to view and manage tags across all cloud services.

1.4.3.2. Integration with billing tools for detailed cost analysis.

2. IA & Big Data

2.1. ModelArts Azure Machine Learning

2.1.1. Use Cases

2.1.1.1. Training deep learning models.

2.1.1.2. Automating Machine Learning (ML) workflows.

2.1.1.3. Deploying models in production for real-time inference.

2.1.2. Additional Features

2.1.2.1. Support for popular frameworks like TensorFlow, PyTorch, and MXNet.

2.1.2.2. Integrated tools for data labeling, model training, and validation.

2.1.2.3. Support for automated pipelines to simplify the ML lifecycle.

2.1.3. Management

2.1.3.1. Centralized management of model training, deployment, and monitoring through a unified platform.

2.1.3.2. Integrated version control for datasets, models, and experiments.

2.1.3.3. Real-time monitoring of deployed models with performance metrics and drift detection.

2.2. Data Lake Insight (DLI) Azure Data Lake Analytics

2.2.1. Use Cases

2.2.1.1. Integration with big data tools like Spark and Hadoop.

2.2.1.2. Processing and analyzing large volumes of structured and unstructured data.

2.2.1.3. Running SQL queries directly on data stored in the Data Lake.

2.2.2. Additional Features

2.2.2.1. "Pay-per-use" model based on tasks, with no need to manage infrastructure.

2.2.2.2. Support for multiple data formats, such as CSV, JSON, and Parquet.

2.2.3. Management

2.2.3.1. Simplified configuration of compute resources for query execution.

2.2.3.2. Monitoring of job performance and resource utilization via dashboards.

2.2.3.3. Easy integration with cloud storage for seamless data access.

2.3. Machine Learning Studio Azure Machine Learning Studio

2.3.1. Use Cases

2.3.1.1. Visual creation and rapid development of ML models without extensive coding.

2.3.1.2. Interactive experimentation to adjust hyperparameters and evaluate model performance.

2.3.2. Additional Features

2.3.2.1. Intuitive graphical interface for building ML pipelines.

2.3.2.2. Integration with Python libraries for greater flexibility.

2.3.3. Management

2.3.3.1. Interactive interface for managing ML pipelines and experiments.

2.3.3.2. Automated logging of experiment results for easy comparison and auditing.

2.3.3.3. Role-based access control (RBAC) for collaborative model development.

2.4. Enterprise Intelligence (EI) Azure Cognitive Services

2.4.1. Image Recognition Azure Computer Vision

2.4.1.1. Purpose

2.4.1.1.1. Identifies visual content in images

2.4.1.2. Use Cases

2.4.1.2.1. Object detection, image classification, and facial recognition

2.4.2. Optical Character Recognition (OCR) Azure AI Read OCR

2.4.2.1. Purpose

2.4.2.1.1. Detects and extracts text from images and converts it into machine-readable formats like JSON.

2.4.2.2. Use Cases

2.4.2.2.1. Automating document processing, invoice digitization, and license plate recognition.

2.4.3. Content Moderation Azure AI Content Safety

2.4.3.1. Purpose

2.4.3.1.1. Detects inappropriate or offensive content in images, text, or videos.

2.4.3.2. Use Cases

2.4.3.2.1. Ensuring compliance with content policies on social media platforms or e-commerce sites.

2.4.4. Short Sentence Recognition Azure Speech to Text

2.4.4.1. Purpose

2.4.4.1.1. Converts short audio files (up to one minute) into text

2.4.4.2. Use Cases

2.4.4.2.1. Voice commands, transcription of short voice notes

2.4.5. Question Answering Bot (QABot) Azure AI Language - Question Answering

2.4.5.1. Purpose

2.4.5.1.1. Builds and manages intelligent question-and-answer bots.

2.4.5.2. Use Cases

2.4.5.2.1. Customer service chatbots and virtual assistants.

2.4.6. EIHealth Azure Health Data Services

2.4.6.1. Purpose

2.4.6.1.1. Facilitates genomics research, drug discovery, and medical imaging using AI.

2.4.6.2. Use Cases

2.4.6.2.1. Analyzing medical data for diagnostics and research

2.4.7. Intelligent Video Analysis Service (VIAS) Azure Video Indexer

2.4.7.1. Purpose

2.4.7.1.1. Provides AI-driven video analytics for event detection and monitoring

2.4.7.2. Use Cases

2.4.7.2.1. Security surveillance, traffic monitoring, and retail analytics.

2.4.8. Recommender System (RES) Azure Machine Learning - Recommender Systems

2.4.8.1. Purpose

2.4.8.1.1. Offers personalized recommendations based on user behavior and preferences.

2.4.8.2. Use Cases

2.4.8.2.1. E-commerce product recommendations, streaming service suggestions.

2.4.9. Natural Language Processing (NLP) Azure AI Language Services

2.4.9.1. Purpose

2.4.9.1.1. Includes tools for sentiment analysis, text summarization, translation, and entity recognition.

2.4.9.2. Use Cases

2.4.9.2.1. Analyzing customer feedback, automating translations.

2.4.10. Voice Recognition and Synthesis Services Azure Speech Service (Speech-to-Text, Text-to-Speech)

2.4.10.1. Purpose

2.4.10.1.1. Includes speech-to-text, text-to-speech, and speaker identification capabilities.

2.4.10.2. Use Cases

2.4.10.2.1. Building voice assistants, real-time transcription services.

2.4.11. Summary

2.4.11.1. Vision-based services (e.g., Image Recognition, OCR). Speech-based services (e.g., Short Sentence Recognition). Text-based services (e.g., NLP tools). Specialized solutions for industries like healthcare (EIHealth) and security (VIAS).

3. DevOps

3.1. Cloud Container Engine (CCE) Azure Kubernetes Service (AKS)

3.1.1. Use Cases

3.1.1.1. Deploying, scaling, and managing containerized applications using Kubernetes

3.1.1.2. Migrating legacy applications to containers for better resource utilization and scalability.

3.1.1.3. Running microservices-based architectures with automated orchestration.

3.1.1.4. Processing real-time data streams for quick analysis and decision-making.

3.1.2. Additional Features

3.1.2.1. Supports heterogeneous computing architectures (e.g., GPU, NPU, Arm).

3.1.2.2. Multi-AZ and multi-region disaster recovery for high availability

3.1.2.3. Integration with CI/CD pipelines for seamless container lifecycle management.

3.1.2.4. Advanced scheduling policies (e.g., Huawei Volcano) for optimized resource utilization.

3.1.3. Management

3.1.3.1. Centralized dashboard for monitoring cluster performance and workloads.

3.1.3.2. Role-based access control (RBAC) for secure access to clusters.

3.1.3.3. Automatic scaling of nodes and workloads based on demand.

3.1.3.4. Integration with VPC networks for secure and efficient communication between containers.

3.2. Software Repository for Container (SWR) Azure Container Registry (ACR)

3.2.1. Use Cases

3.2.1.1. Storing, managing, and distributing container images securely across teams.

3.2.1.2. Automating image builds and updates as part of CI/CD pipelines.

3.2.1.3. Enabling version control and rollback capabilities for container images

3.2.2. Additional Features

3.2.2.1. Global image replication to ensure low-latency access across regions

3.2.2.2. Image vulnerability scanning to detect and mitigate security risks.

3.2.2.3. Integration with CCE to simplify container deployment workflows

3.2.3. Management

3.2.3.1. Fine-grained access control to repositories using IAM policies.

3.2.3.2. Monitoring tools to track repository usage and image pull statistics.

3.2.3.3. Automated cleanup policies to manage storage efficiently.

3.3. Cloud Deployment Service (CDS) Azure DevOps

3.3.1. Use Cases

3.3.1.1. Automating CI/CD pipelines for faster application releases

3.3.1.2. Managing multi-cloud or hybrid cloud deployments seamlessly

3.3.1.3. Monitoring and troubleshooting application deployment processes in real time

3.3.2. Additional Features

3.3.2.1. Visualized pipeline orchestration with drag-and-drop tools.

3.3.2.2. Prebuilt integrations with code repositories like GitHub, GitLab, and CodeHub.

3.3.2.3. Support for canary releases, blue-green deployments, and rollbacks

3.3.3. Management

3.3.3.1. Centralized dashboard to monitor pipeline execution and deployment status

3.3.3.2. Permission management to define roles and restrict access to pipelines or environments.

3.3.3.3. Real-time logs and analytics for debugging failed builds or deployments.

4. Cloud Migration

4.1. Cloud Migration Service (CMS) Azure Migrate

4.1.1. Use Cases

4.1.1.1. Assessing on-premises workloads for migration to Huawei Cloud.

4.1.1.2. Planning and executing application, database, and infrastructure migrations

4.1.1.3. Migrating virtual machines (VMs) from VMware, Hyper-V, or physical servers to the cloud

4.1.2. Additional Features

4.1.2.1. Provides migration assessment reports, including cost estimates and dependency analysis.

4.1.2.2. Supports both online and offline migrations to minimize downtime.

4.1.2.3. Integration with other Huawei Cloud services for seamless post-migration operations

4.1.3. Management

4.1.3.1. Centralized dashboard for tracking migration progress and performance metrics.

4.1.3.2. Automated migration workflows with rollback options in case of failures.

4.1.3.3. Role-based access control (RBAC) to manage migration tasks securely.

4.2. Server Migration Service (SMS) Azure Site Recovery

4.2.1. Use Cases

4.2.1.1. Migrating servers (physical or virtual) to Huawei Cloud with minimal downtime.

4.2.1.2. Disaster recovery by replicating on-premises workloads to the cloud for failove

4.2.1.3. Moving legacy applications or workloads between regions or data centers.

4.2.2. Additional Features

4.2.2.1. Supports heterogeneous environments, including Windows and Linux servers.

4.2.2.2. Incremental data synchronization to reduce bandwidth usage during migrations.

4.2.2.3. Pre-migration testing to ensure compatibility and performance post-migration.

4.2.3. Management

4.2.3.1. Real-time monitoring of migration progress through a unified console

4.2.3.2. Automated scheduling of migrations during off-peak hours to minimize disruptions

4.2.3.3. Detailed logs and reports for auditing and troubleshooting migration tasks.

5. Security

5.1. Identity and Access Management (IAM) Azure Active Directory (Azure AD)

5.1.1. Use Cases

5.1.2. Additional Features

5.1.3. Management

5.2. Anti-DDoS Azure DDoS Protection

5.2.1. Use Cases

5.2.2. Additional Features

5.2.3. Management

5.3. Web Application Firewall (WAF) Azure Web Application Firewall

5.3.1. Use Cases

5.3.2. Additional Features

5.3.3. Management

5.4. Key Management Service (KMS) Azure: Azure Key Vault

5.4.1. Use Cases

5.4.2. Additional Features

5.4.3. Management

5.5. Host & Container Security Service Microsoft Defender for Cloud - Azure Defender for Servers - Azure Defender for Containers

5.5.1. Use Cases

5.5.2. Additional Features

5.5.3. Management

5.6. Huawei Cloud Agency Azure Managed Identity

5.6.1. Use Cases

5.6.2. Additional Features

5.6.3. Management

6. Computing

6.1. Bare Metal Server (BMS) Azure Bare Metal

6.1.1. Use Cases

6.1.1.1. High-performance workloads (HPC, Big Data)

6.1.1.2. Latency-sensitive applications

6.1.1.3. Environments requiring physical isolation

6.1.2. Additional Features

6.1.2.1. GPU support for AI/ML

6.1.2.2. Direct connections to private networks (VPC)

6.1.3. Management

6.1.3.1. Options for automated provisioning

6.1.3.2. Support for hardware customization

6.2. Auto Scaling Azure Autoscale

6.2.1. Scaling Policies

6.2.1.1. Metric-based (CPU, memory, etc.)

6.2.1.2. Scheduled scaling policies

6.2.2. Use Cases

6.2.2.1. Dynamic adjustment to handle website traffic spikes

6.2.2.2. Cost reduction by shutting down idle instances

6.2.3. Integration with Other Services:

6.2.3.1. Integration with Cloud Eye (Huawei)

6.2.3.2. Compatibility with Load Balancers

6.3. FunctionGraph (Serverless) Azure Functions

6.3.1. Supported Triggers

6.3.1.1. HTTP/HTTPS events

6.3.1.2. Changes in storage buckets (OBS/Blob Storage)

6.3.1.3. Database or queue events (DMS/Service Bus)

6.3.2. Use Cases

6.3.2.1. Real-time processing (log analysis etc)

6.3.2.2. Automation of administrative tasks

6.3.3. Comparison with Azure Functions:

6.3.3.1. Huawei FunctionGraph supports multiple languages like Python, Java, Node.js, while Azure Functions also integrates with .NET and PowerShell

6.4. Elastic Cloud Server (ECS) AzureVirtual Machines (VMs)

6.4.1. Instance Families

6.4.1.1. General-purpose: For standard workloads

6.4.1.2. Compute-optimized: For CPU-intensive workloads.

6.4.1.3. Memory-optimized: For databases and analytics

6.4.2. Storage Options

6.4.2.1. EVS (Elastic Volume Service): Persistent block storage for ECS instances, ideal for databases and other stateful applications.

6.4.2.2. OBS (Object Storage Service): Unstructured data storage, suitable for backups, big data, and media files.

6.4.2.3. SFS (Scalable File Service): Fully managed shared file storage, ideal for scenarios where multiple ECS instances need to access the same file system simultaneously (e.g., distributed applications, content management systems).

6.4.3. Practical Use Cases

6.4.3.1. Hosting enterprise applications

6.4.3.2. Running Dev/Test environments

6.5. ECS com Auto Scaling Azure Virtual Machine Scale Set

6.5.1. Advanced Configuration

6.5.1.1. Policies based on combined metrics

6.5.1.2. Minimum/maximum instance configuration

6.5.2. Real-world Scenarios:

6.5.2.1. Mobile applications facing seasonal traffic spikes

6.5.2.2. Backend infrastructure for online games

7. Storage

7.1. Elastic Volume Service (EVS) Azure Managed Disks

7.1.1. Use Cases

7.1.1.1. Persistent storage for virtual machines (ECS/BMS)

7.1.1.2. Databases requiring high IOPS and low latency

7.1.1.3. Enterprise applications with stateful workloads.

7.1.2. Additional Features

7.1.2.1. Supports snapshots for data backup and recovery

7.1.2.2. High durability and availability with multiple replicas

7.1.2.3. Scalable performance tiers (e.g., SSD, HDD)

7.1.3. Management

7.1.3.1. Automated volume resizing without downtime

7.1.3.2. Integration with disaster recovery solutions.

7.1.3.3. Easy attachment/detachment to ECS instances.

7.2. Object Storage Service (OBS) Azure Blob Storage

7.2.1. Use Cases

7.2.1.1. Backup and archiving of unstructured data.

7.2.1.2. Hosting static content like images, videos, and documents

7.2.1.3. Big Data storage for analytics and machine learning pipelines

7.2.2. Additional Features

7.2.2.1. Big Data storage for analytics and machine learning pipelines

7.2.2.2. Multi-region replication for high availability.

7.2.2.3. Integration with CDN for faster content delivery

7.2.3. Management

7.2.3.1. RESTful APIs for programmatic access.

7.2.3.2. Fine-grained access control using IAM policies.

7.2.3.3. Pay-as-you-go pricing based on storage usage.

7.3. Cloud Backup and Recovery (CBR) Azure Backup

7.3.1. Use Cases

7.3.1.1. Automated backup of ECS, EVS, and databases

7.3.1.2. Disaster recovery for critical workloads

7.3.1.3. Long-term retention of compliance-related data

7.3.2. Additional Features

7.3.2.1. Incremental backups to save storage space and reduce costs.

7.3.2.2. Cross-region backup replication for added resilience.

7.3.2.3. Encryption at rest and in transit for enhanced security.

7.3.3. Management

7.3.3.1. Centralized dashboard for managing backup policies.

7.3.3.2. Scheduled backups with customizable retention periods.

7.4. Scalable File Service (SFS) Azure Files

7.4.1. Use Cases

7.4.1.1. Shared file storage for distributed applications (e.g., web servers).

7.4.1.2. High-performance computing (HPC) requiring shared access to files.

7.4.1.3. Media processing workflows like video editing or rendering.

7.4.2. Additional Features

7.4.2.1. Supports NFS and SMB protocols for compatibility with Linux and Windows systems.

7.4.2.2. Elastic scaling of capacity up to petabytes without downtime.

7.4.2.3. High throughput and low latency with SFS Turbo option

7.4.3. Management

7.4.3.1. Multi-instance access with up to 256 ECS or BMS connections simultaneously.

7.4.3.2. Simplified file system creation via the cloud console or APIs

7.4.3.3. Integration with Cloud Eye for performance monitoring

8. Network

8.1. Virtual Private Cloud (VPC) Azure Virtual Network (VNet)

8.1.1. Use Cases

8.1.1.1. Isolated network environments for secure application hosting

8.1.1.2. Multi-tier application architectures with private subnets for backend services.

8.1.1.3. Secure communication between cloud resources.

8.1.2. Additional Features

8.1.2.1. Customizable IP address ranges

8.1.2.2. Subnet-level security groups and ACLs (Access Control Lists).

8.1.2.3. Integration with VPN and Direct Connect for hybrid cloud setups.

8.1.3. Management

8.1.3.1. Centralized control via the cloud console or APIs

8.1.3.2. Monitoring and troubleshooting with flow logs.

8.1.3.3. Easy configuration of routing tables and peering connections

8.2. Elastic IP (EIP) Azure Public IP Address

8.2.1. Use Cases

8.2.1.1. Assigning public IPs to ECS or BMS instances for internet-facing applications.

8.2.1.2. Hosting web servers, APIs, or other publicly accessible services.

8.2.1.3. Dynamic reassignment of IP addresses during failover scenarios

8.2.2. Additional Features

8.2.2.1. Pay-as-you-go billing based on bandwidth usage.

8.2.2.2. Support for dynamic IP reassignment across instances

8.2.3. Management

8.2.3.1. Allocation and association via the cloud console or CLI.

8.2.3.2. Monitoring of bandwidth usage to optimize costs.

8.3. Load Balancer Azure Load Balancer

8.3.1. Use Cases

8.3.1.1. Distributing traffic among multiple ECS/BMS instances to ensure high availability.

8.3.1.2. Scaling applications horizontally by balancing workloads across instances.

8.3.1.3. Scaling applications horizontally by balancing workloads across instances.

8.3.2. Additional Features

8.3.2.1. Support for Layer 4 (TCP/UDP) and Layer 7 (HTTP/HTTPS) load balancing

8.3.2.2. Health checks to monitor instance availability.

8.3.3. Management

8.3.3.1. Configuration of listener rules for traffic routing.

8.3.3.2. Real-time monitoring of traffic metrics via Cloud Eye

8.4. Direct Connect Azure ExpressRoute

8.4.1. Use Cases

8.4.1.1. Establishing private, high-speed connections between on-premises data centers and the cloud

8.4.1.2. Supporting hybrid cloud architectures with secure communication channels.

8.4.2. Additional Features

8.4.2.1. Dedicated bandwidth options for consistent performance.

8.4.2.2. Multi-region connectivity support for global enterprises.

8.4.3. Management

8.4.3.1. Provisioning via the console or APIs with flexible bandwidth options.

8.4.3.2. Monitoring link health and performance metrics.

8.5. NAT Gateway Azure NAT Gateway

8.5.1. Use Cases

8.5.1.1. Enabling outbound internet access for resources in private subnets without exposing their IPs.

8.5.1.2. Facilitating secure communication between private VPC resources and external services.

8.5.2. Additional Features

8.5.2.1. High throughput and scalability for large-scale deployments.

8.5.3. Management

8.5.3.1. Easy setup via the console with customizable rules.

8.6. Content Delivery Network (CDN) Azure CDN

8.6.1. Use Cases

8.6.1.1. Accelerating content delivery globally for websites, APIs, and media streaming platforms

8.6.1.2. Accelerating content delivery globally for websites, APIs, and media streaming platforms

8.6.2. Additional Features

8.6.2.1. Integration with OBS for seamless content distribution.

8.6.3. Management

8.6.3.1. Configuration of caching policies and custom domains

8.7. VPC peering VNet Peering

8.7.1. Use Cases

8.7.1.1. Connecting multiple VPCs within the same or different regions for resource sharing.

8.7.2. Additional Features

8.7.2.1. Low-latency communication between VPCs without using public internet routes.

8.7.3. Management

8.7.3.1. Creation of peering connections via the console with route table updates.

8.8. VPC Endpoint Services Azure Private Link + Private Endpoints

8.8.1. Use Cases

8.8.1.1. Enabling private access to cloud services without exposing traffic to the public internet.

8.8.2. Additional Features

8.8.2.1. Supports integration with services like OBS, RDS, and API Gateway.

8.8.3. Management

8.8.3.1. Setup of endpoint policies to control access.

9. Database

9.1. Relational Database Service (RDS) Azure SQL Database

9.1.1. Use Cases

9.1.1.1. Hosting transactional databases for enterprise applications (e.g., ERP, CRM)

9.1.1.2. Running relational databases for e-commerce platforms and financial systems

9.1.1.3. Supporting web applications with structured data requirements

9.1.2. Additional Features

9.1.2.1. Multi-AZ deployment for high availability.

9.1.2.2. Automated backups and point-in-time recovery

9.1.2.3. Support for various database engines (e.g., MySQL, PostgreSQL, SQL Server).

9.1.3. Management

9.1.3.1. Easy scaling of compute and storage resources.

9.1.3.2. Monitoring and performance tuning via Cloud Eye (Huawei)

9.1.3.3. Integration with disaster recovery solutions

9.2. GaussDB (NoSQL) Azure Cosmos DB

9.2.1. Use Cases

9.2.1.1. Managing large-scale unstructured or semi-structured data

9.2.1.2. Applications requiring low-latency access to NoSQL databases (e.g., IoT, gaming).

9.2.1.3. Real-time analytics and recommendation engines.

9.2.2. Additional Features

9.2.2.1. Fully managed NoSQL database with high throughput and low latency

9.2.2.2. Multi-model support (key-value, document, graph, etc.).

9.2.2.3. Multi-model support (key-value, document, graph, etc.).

9.2.3. Management

9.2.3.1. Automatic scaling based on workload demands

9.2.3.2. Fine-grained access control with IAM integration.

9.2.3.3. Monitoring with built-in metrics and alerts

9.3. Data Warehouse Service (DWS) Azure Synapse Analytics

9.3.1. Use Cases

9.3.1.1. Building enterprise data warehouses for business intelligence and reporting

9.3.1.2. Analyzing large datasets for trends, insights, and decision-making.

9.3.1.3. Analyzing large datasets for trends, insights, and decision-making.

9.3.2. Additional Features

9.3.2.1. Columnar storage for optimized analytical queries

9.3.2.2. Integration with big data tools like Hadoop and Spark.

9.3.2.3. High concurrency support for multiple users querying simultaneously

9.3.3. Management

9.3.3.1. Automated scaling of compute nodes and storage capacity.

9.3.3.2. Query optimization tools for faster performance.

9.3.3.3. Centralized management via the cloud console.

9.4. Document Database Service (DDS) Azure Cosmos DB (MongoDB API)

9.4.1. Use Cases

9.4.1.1. Storing JSON-like documents for web and mobile applications.

9.4.1.2. Applications requiring flexible schema design (e.g., content management systems).

9.4.1.3. Real-time analytics on document-based datasets.

9.4.2. Additional Features

9.4.2.1. MongoDB-compatible API for seamless migration of existing workloads.

9.4.2.2. Built-in sharding for horizontal scaling of large datasets.

9.4.2.3. High availability with replica sets across multiple zones

9.4.3. Management

9.4.3.1. Simplified cluster management via the cloud console or APIs.

9.4.3.2. Automated backups and recovery options.

9.4.3.3. Real-time monitoring of performance metrics.