This chapter will explore how to build a network and configure network services for your workloads in Google Cloud. We are going to cover the following topics: Networking is the foundation of every system architecture. However, connecting internal cloud workloads across projects, exposing services to the internet, or building a hybrid network between an on-premises…
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In-memory datastore – Memorystore
Applications designed for real-time banking, online interactive gaming with player scores and profiles, or geospatial processing all need the fastest possible response times. Databases such as Cloud SQL or Spanner still rely on disk operations, although they provide high throughput. To reduce its response latency to an absolute minimum, a database could be stored in…
Warehouse and analytics – BigQuery
As data grows out of datasheets, it needs a more efficient system for its analytics – a data warehouse. However, scaling and managing such a platform on-premises can be challenging, especially when data grows from gigabytes to terabytes and petabytes. Such challenges are addressed by the Google-managed serverless data warehouse: BigQuery. This is a query…
Firestore – Google Exams Guide
Firestore is a serverless document database with all its underlying infrastructure components and complexity hidden from users. Compared to Cloud Bigtable, where we deploy an instance with nodes that define the performance, or Cloud SQL, where we configure CPU, RAM, and storage resources for an instance, there is no node provisioning and resource planning in…
Cloud Bigtable – Google Exams Guide
Imagine you need to design an application that continuously scans the state of millions of IoT sensors, or that the service you are designing will be responsible for keeping track of a few million users’ behaviors and offering them recommendations based on their preferences. In such scenarios, you will have to store data in a…
Cloud Spanner – Google Exams Guide
There are limits to how much a relational database can expand. In most cases, it can only expand vertically. For example, you can grow Cloud SQL instances (described in the previous sections) by adding more storage or replicating to a larger instance up to the largest available type (vertical scaling), but to some point only,…
Working with a PostgreSQL database
Suppose you were asked to create a small but critical PostgreSQL database for an application that would run on a Compute Engine Linux VM, VM-a, in a my-subnet subnet in the europe-central2 region. The owner of the application is concerned about a potential latency between the database and their application. Also, although lightweight, the database…
Databases – Google Exams Guide
Block, file, or object storage are not the only solutions to store data. Data can also be stored in databases: relational SQL databases and non-relational NoSQL databases. Both types will be covered in the next sections. Also, we will look into a data warehouse and in-memory database: While looking for a database that will be…
Creating a file share in practice
Suppose you received a task to create a share so that the owner of vm-a and the owner of vm-b can work together on documents in a folder structure. Both VMs are deployed as Compute Engine VMs that run in the europe-central2 region with an NFS client already installed. The files take around 500 GB…
Block storage – local and persistent disks
At Google Cloud, block storage in the form of disks emulating physical drives and attached to a compute layer is used by Google Kubernetes Engine (GKE) and Google Compute Engine (GCE). An operating system recognizes block storage as a volume that can be formatted so applications can use it. Compute Engine instance has, by default,…