GCP - Professional Cloud Developer - products and services

Here are the main products and features you should know for the Professional Cloud Developer exam:

1. Compute

• Google Compute Engine (GCE)
  • Virtual machine service that lets you run workloads on Google's infrastructure.
  • This means that you can create and manage virtual machines (VMs) on Google's servers, allowing you to scale your compute resources as needed without having to worry about managing the underlying hardware
  • One unique aspect of GCE is its ability to provide custom machine types, which allows you to specify the precise amount of CPU and memory that your VMs require. This can help you optimize your costs and performance by providing just the right amount of resources for your workloads.
  • GCE provides a variety of machine images, including pre-configured images with popular software and frameworks (such as Docker, Apache, and NGINX) and images for popular operating systems (such as Debian, Ubuntu, and Windows Server). GCE also provides a variety of networking options, including virtual private clouds, load balancing, and VPN connectivity, to help you build and manage your network infrastructure.
  • Some example of use cases for GCE include: running large-scale web applications, running Hadoop or Spark clusters for big data processing, or running scientific simulations or engineering workloads that require high-performance computing.
• Google Kubernetes Engine (GKE)
  • Managed Kubernetes service that lets you deploy, manage, and scale containerized applications. This means that you can use Kubernetes, an open-source platform for container orchestration, to manage your applications in the cloud without having to worry about the underlying infrastructure.
  • One unique aspect of GKE is its ability to provide automatic upgrades for your Kubernetes clusters, which ensures that your cluster stays up to date with the latest security patches and features. GKE also provides a variety of networking options, including virtual private clouds, load balancing, and network security policies, to help you build and manage your network infrastructure.
  • GKE provides several features to help you manage your Kubernetes clusters, such as the ability to scale your deployments up or down automatically based on demand, the ability to configure rolling updates and rollbacks for your deployments, and the ability to configure custom autoscaling policies.
  • Some example of use cases for GKE include: running microservices-based applications, running batch or stream processing workloads using tools like Apache Beam or Apache Flink, or running machine learning workloads using tools like TensorFlow or PyTorch.
• Google App Engine (GAE)
  • Platform as a Service (PaaS) that lets you build and deploy applications on Google's infrastructure without worrying about the underlying infrastructure. This means that you can focus on writing code and building your applications, while GAE handles tasks such as scaling, load balancing, and resource management.
  • One unique aspect of GAE is its ability to provide a fully managed environment for popular programming languages such as Java, Python, Node.js, Go, and PHP. GAE also provides a variety of runtimes, such as standard and flexible, that allow you to choose between different levels of flexibility and control.
  • In addition, GAE provides several features to help you manage your applications, such as the ability to configure automatic scaling based on demand, the ability to deploy and roll back new versions of your application with zero downtime, and the ability to integrate with other GCP services such as Cloud SQL and Cloud Storage.
  • Some example of use cases for GAE include: building and deploying web applications, building and deploying mobile backends and APIs, or building and deploying microservices-based applications.
• Google Cloud Functions
  • Event-driven serverless computing service that lets you run code in response to events, such as changes to data in a database. This means that you can write small pieces of code (known as functions) and have them automatically triggered by events in your GCP resources, without having to manage any servers or infrastructure.
  • One unique aspect of Cloud Functions is its ability to provide a flexible and cost-effective way to run serverless applications that can scale automatically to meet demand. Cloud Functions also supports multiple programming languages, including JavaScript, Python, Go, and more, and can integrate with other GCP services such as Cloud Storage, Pub/Sub, and BigQuery.
  • In addition, Cloud Functions provides several features to help you manage your serverless applications, such as the ability to configure automatic scaling based on demand, the ability to integrate with other GCP services using triggers and bindings, and the ability to monitor and debug your functions using Stackdriver.
  • Some example of use cases for Cloud Functions include: building serverless applications that perform real-time data processing, building serverless applications that handle user authentication and authorization, or building serverless applications that interact with other GCP services.

2. Storage and Database

• Google Cloud Storage
  • Object storage service that lets you store and retrieve data in a highly available and durable manner. This means that you can store and access any type of unstructured data (such as images, videos, and log files) in the cloud, without having to worry about managing the underlying hardware or software.
  • One unique aspect of Cloud Storage is its ability to provide different storage classes with different performance characteristics and pricing options, such as Standard, Nearline, and Coldline. Cloud Storage also provides features such as object versioning, object lifecycle management, and access control to help you manage your data effectively.
  • Some key features include: multi-region and dual-region storage options, which provide high availability and low latency for data access
  • In addition, Cloud Storage integrates with other GCP services such as BigQuery, Dataflow, and Cloud Pub/Sub, making it a flexible and scalable solution for storing and processing data in the cloud. Cloud Storage also provides a variety of tools and libraries for accessing your data, such as the gsutil command-line tool, the Cloud Storage JSON API, and client libraries for popular programming languages such as Python, Java, and Go.
  • Some example of use cases for Cloud Storage include: storing and serving media files for web applications, storing backups and archives, or storing logs and other data for analysis using BigQuery or Dataflow.
• Google Cloud SQL
  • Fully managed relational database service that supports MySQL and PostgreSQL. This means that you can create and manage relational databases in the cloud, without having to worry about managing the underlying hardware or software.
  • One unique aspect of Cloud SQL is its ability to provide automatic backups and point-in-time recovery for your databases, ensuring that your data is safe and always available. Cloud SQL also provides features such as automatic failover, replication, and migration to help you manage your databases effectively.
  • In addition, Cloud SQL integrates with other GCP services such as App Engine, Compute Engine, and Kubernetes Engine, making it a flexible and scalable solution for running and managing relational databases in the cloud. Cloud SQL also provides a variety of tools and libraries for accessing your databases, such as the Cloud SQL Proxy for secure access to your databases from external applications, and client libraries for popular programming languages such as Python, Java, and Go.
  • Some example of use cases for Cloud SQL include: running mission-critical databases for enterprise applications, building and deploying applications that require relational databases, or migrating on-premises databases to the cloud using Cloud SQL's migration features.
• Google Cloud Spanner
  • Horizontally scalable, globally distributed, relational database service. This means that you can store and retrieve structured data in a highly available and scalable manner, with the ability to scale your database horizontally across multiple regions and continents. You could also explain some of the key features of Cloud Spanner, such as its ability to handle ACID transactions at global scale, its support for SQL queries and indexes, and its integration with other GCP services such as Cloud Functions and Cloud Dataflow.
  • One unique aspect of Cloud Spanner that sets it apart from other GCP database services is its ability to handle transactional consistency and global replication at scale. You could explain that Cloud Spanner achieves this by using a distributed architecture and a custom TrueTime API, which provides a consistent view of time across all replicas. This allows Cloud Spanner to provide strong consistency guarantees for transactions, even in a globally distributed environment.
  • Some example of use cases for _ include: financial services applications that require strong consistency and low latency across multiple regions, or e-commerce applications that require real-time inventory management and order processing.
• Google Cloud Bigtable
  • NoSQL database service that's designed to handle massive amounts of data with low latency. This means that you can store and retrieve structured or unstructured data in the cloud, without having to worry about managing the underlying hardware or software.
  • One unique aspect of Cloud Bigtable is its ability to provide high-performance access to large datasets, making it ideal for use cases such as time-series data, analytics, and machine learning. Cloud Bigtable also provides features such as automatic scaling, replication, and security to help you manage your databases effectively.
  • In addition, Cloud Bigtable integrates with other GCP services such as Hadoop, Dataflow, and BigQuery, making it a flexible and scalable solution for storing and processing data in the cloud. Cloud Bigtable also provides a variety of tools and libraries for accessing your data, such as the HBase API, the Cloud Bigtable client libraries, and the Apache Beam IO connectors.
  • Some example of use cases for _ include: building real-time analytics applications, storing and processing time-series data, or building scalable data pipelines using Apache Beam and Cloud Dataflow.
• Google Cloud Datastore
  • Fully managed NoSQL document database service. This means that you can store and retrieve structured data in the cloud, without having to worry about managing the underlying hardware or software.
  • One unique aspect of Cloud Datastore is its ability to provide a highly scalable and flexible database solution for applications with rapidly changing data requirements. Cloud Datastore also provides features such as automatic scaling, replication, and indexing to help you manage your databases effectively.
  • In addition, Cloud Datastore integrates with other GCP services such as App Engine and Compute Engine, making it a flexible and scalable solution for storing and processing data in the cloud. Cloud Datastore also provides a variety of tools and libraries for accessing your data, such as the Cloud Datastore client libraries, the Google Cloud Dataflow connector, and the Google Cloud Functions trigger.
  • Some example of use cases for Cloud Datastore include: building web and mobile applications that require flexible and scalable databases, storing and processing user-generated content, or building real-time analytics applications.
• Google Cloud Pub/Sub
  • Real-time messaging service that lets you send and receive messages between independent applications. This means that you can build distributed systems and applications that communicate with each other asynchronously and reliably, without having to worry about managing the underlying infrastructure.
  • One unique aspect of Cloud Pub/Sub is its ability to provide a scalable and reliable messaging service that can handle millions of messages per second, making it ideal for use cases such as streaming analytics, event-driven architectures, and IoT applications. Cloud Pub/Sub also provides features such as message ordering, message filtering, and message replay to help you manage your messages effectively.
  • In addition, Cloud Pub/Sub integrates with other GCP services such as Dataflow, Cloud Functions, and BigQuery, making it a flexible and scalable solution for building and processing data pipelines in the cloud. Cloud Pub/Sub also provides a variety of tools and libraries for accessing your messages, such as the Cloud Pub/Sub client libraries, the Apache Beam IO connectors, and the Cloud Functions trigger.
  • Some example of use cases for Cloud Pub/Sub include: building real-time analytics pipelines, building event-driven architectures for microservices-based applications, or building IoT applications that require reliable messaging between devices and cloud services.

3. Networking

• Virtual Private Cloud (VPC)
  • a networking service that provides a private network for your GCP resources. This means that you can create a private and secure environment for your applications and services to communicate with each other within GCP, without exposing them to the public internet.
  • One unique aspect of VPC is its ability to provide granular control over your network, including IP address ranges, subnets, and firewall rules. VPC also provides features such as VPN connectivity, Cloud Router, and Cloud VPN to help you manage your network effectively.
  • In addition, VPC integrates with other GCP services such as Compute Engine, Kubernetes Engine, and App Engine, making it a flexible and scalable solution for running and managing your applications and services in the cloud. VPC also provides a variety of tools and APIs for managing your network, such as the gcloud command-line tool, the VPC API, and the Cloud Console.
  • Some example of use cases for VPC include: building multi-tiered applications with different security requirements, building hybrid cloud environments that connect to on-premises infrastructure, or building applications that require secure communication between components.
• Load balancing
  • a service that lets you distribute traffic across multiple instances or regions, ensuring that your applications and services are highly available and performant. This means that you can provide a seamless and responsive experience for your users, even under heavy traffic loads or during maintenance events.
  • One unique aspect of Load Balancing is its ability to provide different load balancing algorithms and types to suit different types of traffic and applications. For example, you can use HTTP(S) Load Balancing to distribute traffic across multiple instances or regions for web applications, or use TCP/UDP Load Balancing to distribute traffic across multiple instances or regions for non-web applications.
  • In addition, Load Balancing integrates with other GCP services such as Compute Engine, Kubernetes Engine, and App Engine, making it a flexible and scalable solution for running and managing your applications and services in the cloud. Load Balancing also provides features such as auto-scaling, health checking, and session affinity to help you manage your traffic effectively.
  • Some example of use cases for Load Balancing include: building highly available web applications with global reach, building multi-region disaster recovery solutions, or building microservices-based applications with multiple backends.
• Cloud CDN
  • a service that caches content at Google's edge locations for faster delivery to users. This means that you can provide a faster and more responsive experience for your users, especially for content that is frequently accessed.
  • One unique aspect of Cloud CDN is its ability to provide a low-latency and high-throughput content delivery solution, with Google's global network of edge locations. Cloud CDN also provides features such as SSL/TLS termination, HTTP/2 support, and edge caching policies to help you manage your content delivery effectively.
  • In addition, Cloud CDN integrates with other GCP services such as Load Balancing, Compute Engine, and Storage, making it a flexible and scalable solution for delivering content in the cloud. Cloud CDN also provides a variety of tools and APIs for configuring and monitoring your content delivery, such as the gcloud command-line tool, the Cloud CDN API, and the Cloud Console.
  • Some example of use cases for Cloud CDN include: delivering static and dynamic content for web applications, streaming media content for video and audio applications, or serving content from global locations for a better user experience.
• Cloud DNS
  • a managed, authoritative DNS service that provides a scalable and reliable solution for resolving domain names to IP addresses. This means that you can manage your DNS records and zones in the cloud, without having to worry about managing the underlying infrastructure.
  • One unique aspect of Cloud DNS is its ability to provide a highly available and performant DNS resolution service, with low latency and global reach. Cloud DNS also provides features such as zone transfers, DNSSEC support, and import/export tools to help you manage your DNS records and zones effectively.
  • In addition, Cloud DNS integrates with other GCP services such as Compute Engine, Kubernetes Engine, and App Engine, making it a flexible and scalable solution for managing DNS in the cloud. Cloud DNS also provides a variety of tools and APIs for configuring and monitoring your DNS, such as the gcloud command-line tool, the Cloud DNS API, and the Cloud Console.
  • Some example of use cases for Cloud DNS include: managing DNS records for web applications, managing DNS records for microservices-based applications, or migrating DNS records from on-premises to the cloud using Cloud DNS's import/export tools.
• Network Security
  • GCP provides a range of services and features that help you secure your VPC network and the resources running on it. This means that you can enforce security policies and controls to protect your data and applications in the cloud.
  • One unique aspect of GCP's Network Security features is its ability to provide a layered and defense-in-depth approach to security, with multiple layers of protection at different levels of the network stack. For example, you can use VPC Service Controls to limit access to your APIs, use Cloud Armor to protect your applications from DDoS attacks, and use Cloud VPN to encrypt your traffic between GCP and your on-premises network.
  • In addition, GCP's Network Security features integrate with other GCP services such as IAM, Cloud Audit Logging, and Security Command Center, making it a comprehensive and integrated solution for securing your cloud infrastructure. GCP's Network Security features also provide a variety of tools and APIs for configuring and monitoring your security, such as the gcloud command-line tool, the VPC Firewall API, and the Security Command Center API.
  • Some example of use cases for GCP’s Network Security include: securing a multi-tiered application with different security requirements, securing a hybrid cloud environment that connects to on-premises infrastructure, or securing an application that requires compliance with industry-specific regulations.

4. Security

• Identity and Access Management (IAM)
  • IAM is a service that lets you manage access to GCP resources by creating and managing permissions for users, groups, and service accounts. This means that you can control who can access your resources and what actions they can perform on them.
  • One unique aspect of IAM is its ability to provide fine-grained and flexible access control, with roles and permissions that can be customized to your specific needs. IAM also provides features such as organization policies, audit logging, and conditional access to help you manage your access control effectively.
  • In addition, IAM integrates with other GCP services such as Cloud Storage, Compute Engine, and BigQuery, making it a central and integrated solution for managing access to your cloud resources. IAM also provides a variety of tools and APIs for configuring and monitoring your access control, such as the gcloud command-line tool, the IAM API, and the Cloud Console.
  • Some example of use cases for IAM include: managing access to a multi-tenant application with different user roles, managing access to a data warehouse with different data access levels, or managing access to a set of resources for a specific project or team.
• Cloud Identity-Aware Proxy (IAP)
  • IAP is a service that provides secure access to applications by verifying user identity and checking for authorization before granting access. This means that you can protect your applications from unauthorized access and data breaches.
  • One unique aspect of IAP is its ability to provide context-aware access control, with policies that can be customized based on the user's identity, device, location, and other factors. IAP also provides features such as multi-factor authentication, session management, and audit logging to help you manage your access control effectively.
  • In addition, IAP integrates with other GCP services such as Compute Engine, Kubernetes Engine, and App Engine, making it a central and integrated solution for securing access to your cloud applications. IAP also provides a variety of tools and APIs for configuring and monitoring your access control, such as the gcloud command-line tool, the IAP API, and the Cloud Console.
  • Some example of use cases for IAP include: securing access to a web application with role-based access control, securing access to a set of APIs with API-level access control, or securing access to a set of resources for a specific project or team.
• Security Command Center
  • Security Command Center is a security management and data risk platform that provides visibility into your security posture across GCP services. This means that you can detect and respond to security threats and vulnerabilities in your cloud infrastructure.
  • One unique aspect of Security Command Center is its ability to provide a unified and centralized view of your security risks, with automated and real-time insights into security events and configurations across your GCP services. Security Command Center also provides features such as security health analytics, container security, and threat detection to help you manage your security risks effectively.
  • In addition, Security Command Center integrates with other GCP services such as IAM, Cloud Audit Logging, and Cloud Storage, making it a comprehensive and integrated solution for managing security in the cloud. Security Command Center also provides a variety of tools and APIs for configuring and monitoring your security, such as the gcloud command-line tool, the Security Command Center API, and the Cloud Console.
  • Some example of use cases for Security Command Center include: identifying and remediating misconfigurations and vulnerabilities in your cloud infrastructure, detecting and responding to security incidents and threats, or demonstrating compliance with industry-specific regulations and standards.
• Key Management Service (KMS)
  • KVM is a service that lets you manage cryptographic keys used to protect your data in the cloud. This means that you can secure your sensitive data and comply with regulatory requirements by managing your cryptographic keys in a centralized and secure manner.
  • One unique aspect of KMS is its ability to provide a scalable and flexible key management solution, with support for multiple key types and algorithms, key versioning and rotation, and fine-grained key access control. KMS also provides features such as key usage logs, audit trails, and hardware security modules (HSMs) to help you manage your keys and comply with regulatory requirements.
  • In addition, KMS integrates with other GCP services such as Cloud Storage, BigQuery, and Compute Engine, making it a central and integrated solution for managing keys in the cloud. KMS also provides a variety of tools and APIs for configuring and monitoring your key management, such as the gcloud command-line tool, the KMS API, and the Cloud Console.
  • Some example of use cases for KMS include: encrypting and decrypting sensitive data stored in the cloud, protecting keys used for securing network traffic, or securing access to sensitive resources by encrypting authentication tokens.
• Data Loss Prevention (DLP)
  • DLP is a service that helps you discover and protect sensitive data in the cloud. This means that you can comply with regulatory requirements and prevent data breaches by identifying and classifying sensitive data in your cloud infrastructure.
  • One unique aspect of DLP is its ability to provide a comprehensive and scalable data discovery and classification solution, with support for multiple data types and formats, custom data detectors, and advanced machine learning algorithms. DLP also provides features such as data masking, redaction, and de-identification to help you protect your sensitive data from unauthorized access and exposure.
  • In addition, DLP integrates with other GCP services such as Cloud Storage, BigQuery, and Data Loss Prevention API, making it a central and integrated solution for managing sensitive data in the cloud. DLP also provides a variety of tools and APIs for configuring and monitoring your data loss prevention, such as the gcloud command-line tool, the DLP API, and the Cloud Console.
  • Some example of use cases for DLP include: identifying and protecting sensitive data stored in the cloud, detecting and preventing data leaks and breaches, or complying with regulatory requirements such as GDPR, HIPAA, and PCI DSS.

5. Development Tools and Services

• Cloud Source Repositories
  • a private Git repository that lets you store and manage your code on Google's infrastructure. This means that you can collaborate with your team members and manage your source code more effectively in the cloud.
  • One unique aspect of Cloud Source Repositories is its tight integration with other GCP services such as Cloud Build, App Engine, and Kubernetes Engine, making it a central and integrated solution for managing your source code in the cloud. Cloud Source Repositories also provides features such as code search, code reviews, and branch management to help you manage your source code more effectively.
  • In addition, Cloud Source Repositories integrates with popular Git tools and clients, making it easy to integrate with your existing workflows and tools. Cloud Source Repositories also provides a variety of tools and APIs for configuring and monitoring your code management, such as the gcloud command-line tool, the Cloud Source Repositories API, and the Cloud Console.
  • Some example of use cases for Cloud Source Repositories include: managing source code for a web application, managing source code for a mobile application, or managing source code for a machine learning project.
• Google Cloud Build
  • a fully managed continuous integration and delivery service that lets you build, test, and deploy your code on GCP. This means that you can automate your build and deployment workflows and deliver your applications faster and more reliably.
  • One unique aspect of Cloud Build is its ability to provide a scalable and flexible build and deployment solution, with support for multiple programming languages, build environments, and deployment targets. Cloud Build also provides features such as build triggers, custom build steps, and build history to help you manage your build and deployment workflows effectively.
  • In addition, Cloud Build integrates with other GCP services such as Cloud Source Repositories, Container Registry, and Kubernetes Engine, making it a central and integrated solution for managing your build and deployment workflows in the cloud. Cloud Build also provides a variety of tools and APIs for configuring and monitoring your build and deployment workflows, such as the gcloud command-line tool, the Cloud Build API, and the Cloud Console.
  • Some example of use cases for Cloud Build include: building and deploying a web application, building and deploying a containerized application, or building and deploying a machine learning model.
• Google Cloud Debugger
  • service that lets you inspect and debug code running in production without stopping the application. This means that you can identify and fix issues in your application code faster and with minimal impact to your users.
  • One unique aspect of Cloud Debugger is its ability to provide a non-intrusive and real-time debugging solution, with support for multiple programming languages, runtime environments, and cloud platforms. Cloud Debugger also provides features such as snapshot capture, log analysis, and breakpoint management to help you diagnose and resolve issues in your application code.
  • In addition, Cloud Debugger integrates with other GCP services such as Compute Engine, Kubernetes Engine, and App Engine, making it a central and integrated solution for debugging your cloud applications. Cloud Debugger also provides a variety of tools and APIs for configuring and monitoring your debugging, such as the gcloud command-line tool, the Cloud Debugger API, and the Cloud Console.
  • Some example of use cases for Cloud Debugger include: diagnosing and fixing issues in a web application, identifying and resolving performance bottlenecks in a microservices architecture, or tracing and analyzing application behavior in a distributed system.
• Stackdriver Logging, Monitoring, and Tracing
  • Stackdriver is a set of services that provide visibility into the performance and health of your applications and infrastructure in the cloud. This means that you can monitor and troubleshoot your cloud infrastructure in real-time, diagnose issues quickly, and optimize your resources for maximum efficiency.
  • One unique aspect of Stackdriver is its ability to provide a comprehensive and integrated solution for logging, monitoring, and tracing your cloud infrastructure. Stackdriver Logging lets you collect, process, and analyze log data from your applications and infrastructure, Stackdriver Monitoring lets you monitor the health and performance of your cloud resources, and Stackdriver Tracing lets you trace the flow of requests through your application.
  • In addition, Stackdriver integrates with other GCP services such as Compute Engine, Kubernetes Engine, and App Engine, making it a central and integrated solution for managing your cloud infrastructure. Stackdriver also provides a variety of tools and APIs for configuring and monitoring your infrastructure, such as the gcloud command-line tool, the Stackdriver API, and the Cloud Console.
  • Some example of use cases for Stackdriver include: monitoring and optimizing the performance of a web application, diagnosing and resolving issues in a microservices architecture, or analyzing the behavior of a distributed system.
• Cloud Deployment Manager
  • a service that lets you define and deploy infrastructure resources using templates. This means that you can automate your infrastructure deployment workflows, make them repeatable and reliable, and reduce the risk of human error.
  • One unique aspect of Cloud Deployment Manager is its ability to provide a declarative and version-controlled approach to infrastructure deployment, with support for multiple cloud resources and services. Cloud Deployment Manager also provides features such as parameterization, conditionals, and loops to help you create flexible and dynamic infrastructure templates.
  • In addition, Cloud Deployment Manager integrates with other GCP services such as Compute Engine, Kubernetes Engine, and Cloud Storage, making it a central and integrated solution for managing your infrastructure deployment in the cloud. Cloud Deployment Manager also provides a variety of tools and APIs for configuring and monitoring your deployment workflows, such as the gcloud command-line tool, the Cloud Deployment Manager API, and the Cloud Console.
  • Some example of use cases for Cloud Deployment Manager include: deploying a web application, deploying a Kubernetes cluster, or deploying a machine learning environment.
• Cloud Endpoints
  • a service that lets you create and deploy APIs that are secured, monitored, and managed. This means that you can build and expose your APIs to external clients and partners with ease, while ensuring security, scalability, and reliability.
  • One unique aspect of Cloud Endpoints is its ability to provide a comprehensive and integrated solution for API management, with support for multiple programming languages, API frameworks, and deployment platforms. Cloud Endpoints also provides features such as authentication, authorization, rate limiting, and monitoring to help you manage your APIs effectively.
  • In addition, Cloud Endpoints integrates with other GCP services such as Cloud Functions, App Engine, and Kubernetes Engine, making it a central and integrated solution for managing your APIs in the cloud. Cloud Endpoints also provides a variety of tools and APIs for configuring and monitoring your API management, such as the gcloud command-line tool, the Cloud Endpoints API, and the Cloud Console.
  • Some example of use cases for Cloud Endpoints include: building and exposing a RESTful API for a web application, building and exposing a gRPC API for a microservices architecture, or building and exposing a machine learning API for an AI application.

6. Other Concepts

• Application development with App Engine flexible environment: App Engine flexible environment allows you to deploy and run custom-built Docker containers, making it easier to customize and scale your applications.
• Developing for Big Data and Machine Learning with Google Cloud Platform: Google Cloud provides a range of services for processing and analyzing big data, including BigQuery, Dataflow, Dataproc, and more. It also provides a suite of machine learning tools, including AutoML and TensorFlow.