I hope you enjoyed learning about GCP. Let’s do a quick review of what you learned.

Google Cloud Platform is a collection of online services that organizations can use to build, host, and deliver applications, and it runs in Google’s data centers around the world.

GCP’s primary compute offerings are Compute Engine, App Engine, Cloud Run, Kubernetes Engine, and Cloud Functions.

Compute Engine runs virtual machines, which Google calls instances. A preemptible instance is a VM that will only run for 24 hours, and Google can remove it at any time without warning. Preemptible instances cost about 70% less than normal instances.

App Engine runs web and mobile applications. Cloud Run and Kubernetes Engine run containers. Cloud Functions runs individual functions. App Engine, Cloud Run, and Cloud Functions are referred to as serverless.

In the raw storage area, Google provides Cloud Storage for unstructured objects and Filestore for traditional file sharing.

GCP’s relational database offerings are Cloud SQL for MySQL, PostgreSQL, or Microsoft SQL Server, and Cloud Spanner, which is a massively scalable database that can run globally.

Google’s NoSQL offerings include Bigtable, Firestore, Firebase Realtime Database, and Memorystore. Bigtable is best for running large analytical workloads. Firestore is ideal for building client-side mobile and web applications. Firebase Realtime Database is best for syncing data between users in real time, such as for collaboration apps. Memorystore is an in-memory datastore that’s typically used to speed up applications by caching frequently requested data.

Google’s data warehouse service is BigQuery.

GCP’s equivalent of an on-premises network is a Virtual Private Cloud, or VPC. You can connect VPCs together using VPC Network Peering. To create a secure connection between a VPC and an on-premises network, you can use Cloud VPN, which runs over the internet, or you can create a dedicated, private connection using either Cloud Interconnect or Peering.

Google offers lots of options for migrating your on-premises applications. VMware Engine is a complete VMware environment that runs on GCP. Migrate for Compute Engine helps you migrate your local virtual machines from VMware to GCP. Migrate for Anthos will convert a VM into a container that’s managed by Google Kubernetes Engine. The Transfer Appliance is a physical storage server that you can use to ship large amounts of data to Google.

For authentication, Managed Service for Microsoft Active Directory lets you connect your on-premises Active Directory to one that’s hosted on GCP. If you aren’t using Active Directory, then you can use Cloud Identity instead.

Google offers lots of artificial intelligence services that don’t require any knowledge of machine learning. At the moment, Google divides these services into four categories: Sight, Language, Conversation, and Structured Data. Some of the key APIs are Vision, Video Intelligence, Translation, Natural Language, Text-to-Speech, Speech-to-Text, DialogFlow, and Recommendations.

If you need to train models using custom data, but you still don’t want to write any machine learning code, you may be able to use Google’s AutoML suite of services to do it. If you need to build custom models that are outside the scope of the AutoML suite, then you can use the AI Platform suite. It includes many services, but the most important ones are AI Platform Training and AI Platform Prediction.

Google is particularly strong in the data analytics area. Pub/Sub acts as a buffer for services that may not be able to handle huge spikes of incoming data. BigQuery is the main option for storage and interactive analytics.

Dataproc is a managed implementation of Hadoop and Spark. Dataflow is a managed implementation of Apache Beam. Dataprep lets you do data processing without writing any code, and it uses Dataflow under the hood. To visualize or present your data with graphs, charts, etc., you can use Data Studio or Looker. Data Studio is free, but Looker is a more sophisticated business intelligence platform.

If you want to create a processing pipeline that runs tasks in multiple GCP services, then you can use Composer, which is a managed implementation of Apache Airflow. Data Fusion is similar to Composer except that it has a graphical interface and doesn’t require you to write any code.

To manage and ingest data from Internet of Things devices, Google offers IoT Core.

Cloud Build lets you create continuous integration / continuous deployment pipelines. Cloud Source Repositories are private Git repositories hosted on GCP. Artifact Registry is a private artifact store hosted on GCP.

Cloud CDN caches your content on Google’s global network, which reduces the time it takes for your users to retrieve it. Cloud Load Balancing redirects application traffic to groups of VM instances distributed in different locations, and it can automatically scale the number of instances up or down as needed. You can use Cloud Armor to mitigate Distributed Denial of Service attacks. 

A region is basically a data center. Each region contains at least three zones. A zone is essentially an independent part of a data center and has its own power, cooling, network, and security infrastructure. Google divides its data centers into at least three independent zones so that if one of them goes down, it won’t affect the other zones.

You can interact with GCP using the web console, command-line tools, including gcloud, gsutil, bq, and kubectl, or by calling the Cloud SDK client libraries from your applications.

To learn more about GCP, please look through our Google Cloud Platform Library. We have courses, labs, and practice exams on nearly every GCP topic, and we have more content coming out all the time.

Please give this course a rating, and if you have any questions or comments, please let us know. Thanks and have fun with Google Cloud Platform!