GCP Cloud Service and its business use case
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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.