In this chapter, we'll discover some use cases for Google's Cloud Storage offerings. Storage is one of the main pillars of any workload to be hosted on Google Cloud Platform (GCP). GCP offers various options for data to be stored. The nature of the data, nature of the workload, scalability and latency needs to dictate the choice of the storage. We'll cover a few examples on Google Storage, Cloud Spanner, Cloud Bigtable, BigQuery and Cloud Datastore.

In addition to Node.js, we'll use Python for a few recipes in this chapter.

Google Storage offers a way of hosting static web applications using buckets. All client-side technologies (HTML, CSS, and JavaScript) can be hosted in a bucket. This option is very inexpensive to host and can scale to a great extent. The costs incurred will be on the assets stored, outbound network traffic and retrieval from storage. We'll use Hugo (https://gohugo.io), a static website generator and host the generated code in a storage bucket. In addition to hosting a static website, the static contents (CSS, JavaScript, and images) of a dynamic website hosted on Google Storage is also a widely accepted use case.

The following are the initial setup...

Google Storage is extensively used to store static images to be served by different platforms. One of the needs of the modern-day digital world is to have images created for different form factors—desktop, mobile and tablet screens. In this recipe, we'll develop a Cloud Function to resize the images uploaded to a bucket:

The following are the initial setup verification steps to be carried out before the recipe can be executed:

1. Create or select a GCP project
2. Enable billing and enable the default APIs (some APIs such as BigQuery, storage, monitoring, and a few others are enabled automatically)
3. Verify that Node.js and Google Cloud SDK are...

Google Cloud Spanner is a relational database service which is highly consistent, available and scalable horizontally. It is a fully managed service suitable for OLTP workloads and low-latency/high-throughput environments. In this recipe, we'll understand the features of Spanner in relation to a popular RDBMS, MariaDB. We'll take a simple MariaDB database and move the schema and the data to Cloud Spanner. At the time of writing, there is no automated import/export tool for Cloud Spanner, the manual method gives us a glimpse of the way Spanner is built and its strengths/weaknesses. Firstly, we'll export the data from our MariaDB database using the mysqldump utility. Then, we'll create the database and tables via the Google Console. Finally, we'll insert the data into the table via a Python script.

Google Bigtable is a fully managed NoSQL service with a focus on low latency and high throughput. It is well suited for storing data for large analytical processing, operation data from IoT devices, other time-series data, and so on. Cloud Bigtable is designed for performance, for huge databases (~1 TB) and it optimizes your data over time. In this recipe, we'll have a look at a small database generated by an IoT temperature sensor. We'll write a Python script to simulate a sensor sending temperature data located in a garden to the  Bigtable. Then, we'll use BigQuery to connect to the Bigtable database to query and make meaning out of the data store.

Google Datastore is a highly scalable, NoSQL document database similar to Amazon's DynamoDB and MongoDB. Individual data objects in the Datastore are called entities. An entity has one or more properties and a category of objects is called a kind. Each object is uniquely identified by a key. In this recipe, we would simulate a storage drive environment. Employees of a company are given the ability to upload files to Google Storage. The metadata is stored in Cloud Datastore to avoid overhead to the Storage API and for efficiency. The file uploaded to the Storage follows this file format:

<5-digit hash>/<[year][month][day][hour][minute][second]>/<Employee ID>/<file name>

After the data is uploaded to Storage, the metadata collected should enable us to query:

• List all objects uploaded by an employee
• List...