GCP's initial release was on October 6, 2011. Since then it has become one of the most used public cloud platforms and is continuing to grow. GCP offers a suite of cloud services that run on the same infrastructure that Google uses to host their end-user products such as Google search, Gmail, and YouTube. This makes it important because Google not only continues to innovate for its customers but also benefits from its own investment into the platform. Google began operations by launching its Google App engine back in 2008. Since then we have seen multiple other services introduced and the list keeps on growing.

While GCP services are many, we can broadly categorize them into four different services. They are compute services, storage services, networking services, and big data services. Apart from these, there are other cloud services such as identity and security management, management tools, data transfer, and machine learning.

GCP offers you a wide variety of computing services that allow you complete flexibility as to how you want to manage your computing assets. Depending on your application and its requirements, you can choose to deploy a traditional custom virtual machine or use Google's App Engine to run the application:

• Compute engine: Allows you to deploy and run high-performance virtual machines in Google data centers. You can deploy either a pre-configured virtual machine or customize the resources as per your requirements.
• Apps engine: Allows you to deploy your application on a fully managed platform which is completely supported by Google. This allows you to simply deploy your application and have it running without you having to worry about the underlying infrastructure.
• Kubernetes engine: This service allows you to run containers on GCP. This means your containerized applications can be deployed on GCP using the Kubernetes engine service without you having to manage the underlying cluster yourself. Google's Site Reliability Engineers (SREs) constantly monitor the cluster, which relieves you of that responsibility.
• Cloud Functions: This service allows you to run code and respond to events on the fly in a true serverless model. This means allowing code to respond to events is determined by you. This also means you will be billed only if your code runs, making it very cost effective.

The following are the types of storage services:

• Cloud storage: An object storage that can be used for a variety of use cases and is accessible via a REST API. This offering allows geo-redundancy with its multi-regional capability and can be used for both high performance storage requirements to archival storage.
• Cloud SQL: A fully managed (replicated and backed-up) database service that allows you to easily get started with your MySQL and PostgreSQL databases in the cloud. The offering also comes with a standard API and also built-in migration tools to migrate your current databases to the cloud.
• Cloud BigTable: Cloud BigTable is the database for all your NoSQL database requirements. The service can scale to hundreds of petabytes easily, which makes it suitable for enterprise data analysis. BigTable also integrates easily with other big data tools such as Hadoop.
• Cloud Spanner: Cloud Spanner is a relational database service that aims at providing highly scalable and strongly consistent database service for the cloud. This is a fully managed service that can offer transactional consistency and synchronous replication of databases across multiple geographies.
• Cloud Datastore: Cloud Datastore is another service set apart from Cloud BigTable that is suitable for your key-value pair NoSQL database requirements. The services comes with other features such as sharding and replication.
• Persistent Disk: Persistent Disk is persistent high performance block storage that can be attached to your Google compute engine instance or Google Kubernetes engine. The service allows you to resize storage without any downtime and is offered in both HDD and SSD formats. You can also mount one disk on multiple machine instances allowing multi-reader capability.

These are the networking services:

• Virtual Private Cloud (VPC): Virtual private cloud allows you to connect multiple GCP resources together or create internal isolated resources that can be managed easily. You can also deploy firewalls, Virtual Private Networks (VPNs), routes, and custom IP ranges.
• Cloud load balancing: This service allows you to distribute your incoming traffic across multiple Google Compute Engines. Cloud load balancing also lets you do autoscaling and can scale your backend instances depending on the incoming traffic load.
• Cloud CDN: Google's cloud delivery network allows you to distribute your content for lower latency and faster access. Google has over 90 edge points globally spread across multiple continents that make it easy for you to decrease your serving costs.
• Cloud interconnect: This service allows you to directly connect your on-premises data center to Google's network. You can either peer with Google or interconnect depending on your bandwidth requirements and peering capabilities.
• Cloud DNS: This is Google's highly available global DNS network and comes with an API to allow management of records and zones.

The following are the big data services:

• BigQuery: BigQuery is an enterprise data warehouse that allows you to store and query massive datasets by enabling fast SQL queries using Google's underlying infrastructure.
• Cloud dataflow: A fully managed service that allows real-time batch and stream data processing. The service also integrates with Stackdriver, Google's unified logging and monitoring solution, letting you monitor and troubleshoot issues as they happen.
• Cloud dataproc: Cloud dataproc is a fully managed cloud service to run Apache spark and Apache Hadoop clusters.
• Cloud datalab: A powerful tool that allows you to explore and visualize large datasets.
• Cloud dataprep: A service that helps in structured and unstructured data analysis by means of visually exploring and cleaning it.
• Cloud pub/sub: A service built for stream analytics that allows you to publish and subscribe to data streams for big data analysis.
• Google genomics: A service that allows you to query the genomic information of large research projects.
• Google DataStudio: Allows you to turn your data into informative dashboards.

We will look at all services in greater detail in the following chapters.