google cloud platform training in hyderabad

Google Cloud Platform (GCP) is a suite of cloud computing services provided by Google. It offers a range of services including computing, storage, networking, big data, machine learning, and IoT, as well as cloud management, security, and developer tools.

Key Components:

1. Compute Services:
   • Google Compute Engine (GCE): Provides virtual machines (VMs) running in Google’s data centers.
   • Google Kubernetes Engine (GKE): Managed Kubernetes service for deploying, managing, and scaling containerized applications.
   • App Engine: Platform as a Service (PaaS) that allows developers to build and deploy applications on Google’s infrastructure.
   • Cloud Functions: Event-driven serverless compute platform for lightweight, asynchronous tasks.
2. Storage and Databases:
   • Google Cloud Storage: Object storage for unstructured data.
   • Cloud SQL: Managed relational database service for MySQL, PostgreSQL, and SQL Server.
   • Cloud Spanner: Globally distributed, horizontally scalable, strongly consistent database.
   • Bigtable: NoSQL database designed for large analytical and operational workloads.
3. Networking:
   • Virtual Private Cloud (VPC): Provides networking functionality to GCP resources, including subnets, firewalls, and VPNs.
   • Cloud Load Balancing: Distributes incoming network traffic across multiple VM instances.
   • Cloud CDN: Content delivery network to deliver content with low latency.
4. Big Data and Analytics:
   • BigQuery: Serverless, highly scalable, and cost-effective multi-cloud data warehouse.
   • Dataflow: Stream and batch data processing service.
   • Dataproc: Managed Spark and Hadoop service.
   • Pub/Sub: Messaging service for building event-driven systems and real-time analytics.
5. AI and Machine Learning:
   • AI Platform: Tools and services for training, deploying, and managing machine learning models.
   • AutoML: Enables developers with limited ML expertise to train high-quality models.
   • TensorFlow on GCP: Framework for building machine learning models, integrated with GCP.
6. Management Tools:
   • Cloud Console: Web-based interface for managing GCP resources.
   • Cloud Shell: Command-line access to GCP resources.
   • Cloud Deployment Manager: Infrastructure as code tool for deploying GCP resources.
   • Operations Suite (formerly Stackdriver): Monitoring, logging, and diagnostics for applications on GCP.
7. Identity and Security:
   • Cloud IAM (Identity and Access Management): Manages access to GCP resources.
   • Cloud Identity: Identity services for managing users and groups.
   • Security Command Center: Security and risk management platform.

Advantages of GCP:

• Scalability: Easily scale resources up or down based on demand.
• Performance: Leverages Google’s global network infrastructure for high-speed and low-latency services.
• Security: Provides robust security features and compliance certifications.
• Innovation: Access to cutting-edge technologies, particularly in AI and machine learning.
• Cost Efficiency: Flexible pricing models, including sustained use discounts and committed use contracts.

Use Cases:

• Application Hosting: Deploy and manage web applications and APIs.
• Data Analytics: Perform large-scale data processing and analytics.
• Machine Learning: Train and deploy machine learning models for various use cases.
• Storage Solutions: Store and manage large volumes of data, from structured to unstructured.
• IoT: Connect, manage, and analyze IoT devices and data.