GCP Cloud Service and its business use case

GCP Cloud Service and its business use case

1. Google Compute Engine

Service: Virtual machines (VMs) running in Google’s data center.

Use Case: Host websites, run batch processing tasks, and develop and test applications.

2. Google App Engine

Service: A fully managed serverless platform for building and deploying applications.

Use Case: Quickly build and deploy web and mobile applications without managing the underlying infrastructure.

3. Google Kubernetes Engine (GKE)

Service: Managed Kubernetes service.

Use Case: Run containerized applications with ease, orchestrate microservices, and manage scaling and updates.

4. Google Cloud Storage

Service: Object storage for companies of all sizes.

Use Case: Store and retrieve any amount of data at any time, suitable for websites, backup, and archival.

5. BigQuery

Service: A fully managed data warehouse for large-scale data analytics.

Use Case: Analyze large datasets quickly and efficiently, generate business intelligence reports, and run SQL queries on terabytes of data.

6. Google Cloud Pub/Sub

Service: Messaging service for event-driven systems.

Use Case: Real-time messaging between independent applications, event ingestion, and delivery for stream analytics and data integration.

7. Google Cloud Functions

Service: Event-driven serverless compute platform.

Use Case: Execute code in response to events, build lightweight microservices, and handle background processing tasks.

8. Google Cloud SQL

Service: Managed relational database service for MySQL, PostgreSQL, and SQL Server.

Use Case: Host relational databases for applications without managing the underlying infrastructure, ideal for web and mobile applications.

9. Google Cloud Spanner

Service: Fully managed, scalable, and globally distributed relational database.

Use Case: Run mission-critical applications requiring strong consistency, high availability, and horizontal scaling.

10. Google Cloud Firestore

Service: NoSQL document database built for automatic scaling and high performance.

Use Case: Store and sync data for serverless applications, real-time synchronization for mobile and web apps.

11. Google Cloud Bigtable

Service: Fully managed, scalable NoSQL database.

Use Case: Store and analyze time series data, IoT data, and large-scale analytical workloads.

12. Google Dataflow

Service: Fully managed stream and batch data processing service.

Use Case: Process large datasets, perform ETL operations, and handle real-time data analytics.

13. Google Cloud Run

Service: Fully managed compute platform for containerized applications.

Use Case: Deploy and run stateless containers that are invocable via web requests or Pub/Sub events.

14. Google AI Platform

Service: Suite of machine learning tools and services.

Use Case: Build, deploy, and manage machine learning models, handle end-to-end machine learning workflows.

15. Google Cloud IAM (Identity and Access Management)

Service: Manage access to cloud resources.

Use Case: Control who can take action on specific resources, ensuring that the right users have the correct permissions.

16. Google Cloud VPC (Virtual Private Cloud)

Service: Provides a private network to host GCP resources.

Use Case: Isolate resources in a virtual network, connect securely to on-premises data centers.

17. Google Cloud CDN (Content Delivery Network)

Service: Global content delivery network.

Use Case: Deliver content with low latency and high transfer speeds, improve user experience by serving cached content from locations closest to users.

18. Google Cloud Monitoring (formerly Stackdriver)

Service: Monitoring, logging, and diagnostics service.

Use Case: Monitor the health of applications and infrastructure, gain insights from logs and metrics.

19. Google Cloud Data Fusion

Service: Fully managed, cloud-native data integration service.

Use Case: Build and manage ETL/ELT data pipelines, integrate data from various sources into data warehouses or lakes.

20. Google Cloud Dataproc

Service: Managed Spark and Hadoop service.

Use Case: Process large datasets using open-source data tools like Apache Spark, Hadoop, and Hive, suitable for big data analytics.

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Jagan Rajagopal AWS Certified Solution Associate ,Aws Coach Jagan ,Azure ,Terraform的更多文章

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