With a powerful AI engine and storage and 100s of Google product integration, the google cloud platform AKA GCP is the go-to platform. Specially if someone is into AI or Google-based customer-facing application development.
In this Quick article, we’ll jolt down wide range of Google Cloud products into categories and explain what you can use them for.
For the introductory level, I’ll be dividing products into four categories which are
- Big Data
Let’s begin with each point of Google cloud products. We’ll look into the details of related services inside each category and what service it provides along with what those service offers.
The capability to run your application/code/program is solved by various computing services of Google Cloud. Let’s see what are compute services and what they can solve for customers.
Compute Engine (Virtual machines)
Google Cloud’s Compute Engine provides us with virtual machines. These virtual machines can be run on the cloud and used to host a variety of solutions. They are capable of hosting websites, services, and running applications on the cloud.
Compute engine comes in various size based on specific need like number of cores or Amount of RAM/Memory you need for your computing resource.
Kubernetes Engine (Run containers)
Google has their own engine for Kubernetes, Kubernetes is used for running containerised application on cloud.
For application running in micro-service architecture, kubernetes helps to run many container at once and manage those container, its life-cycle and scalablity.
App engine ( Serverless application )
Unlike Compute engine, where you have to orchestrate a machine, setup executables and then programming language followed by your application, Google cloud’s App engine provide you a big abstraction from managing infrastructure. Customer just needed to upload their code and rest all the provision of infrastructure is taken care by app engine.
When you should not use AWS Lambda?
Cloud functions (Serverless functions)
You can consider it as lighter version of app engine, where you can run entire application on App engine, cloud functions as name suggests, can be used for specific services of your application or say micro services.
Its well integrated with other services within GCP, so you can use it to trigger processing data, storing to database or in format of object using cloud functions.
Google cloud service has one of big advantage over other cloud providers for storage and the processing & classes it provides for storage. Lets understand in deep what storage services Google cloud platform offers.
Big Table (No SQL)
Google cloud platform’s Big table offer us Non-relational AKA no-sql data storage service. Many scalable app these days prefer unstructured data and big-table provide efficient and nearly infinite scalable service for the same.
Cloud storage (Drive)
Google storage offers object storage for our application and services. Object storage means file storage mainly, you can store any sort of files on Cloud storage like, images, csv, videos, audio, word file and so on.
Cloud storage contains bucket which logically helps to categories data just like folders.
CloudSQL (Relational database service)
Google cloud platform offers powerful relational database storage in form of CloudSQL, we can use this to store our data which in form of table and relations.
For beginners and mid size application, cloudSQL provides mysql 5.6, 5.7, which have upto 416G of memory and 30T of storage.
Cloud spanner (Relational database at scale)
We can consider it a scalable and bigger version of cloud spanner which means that cloud Spanner provides scalable relational database capability with big data like performance.
When requirement includes huge amount of data processing, upload and analytics for relational databases, cloud spanner is goto solution for it.
Cloud data storage (NoSQL database at scale)
Just like cloud spanner is to cloud SQL, cloud data storage is scalable intense performance for Big table. Cloud data storage provides big no-sql data processing, storage and operations at scale like big tables.
This category is specially named as big data because this deals with terabyte and petabyte scale of data, lets dig deeper into 4 of its services and what it offers
BigQuery (Store, search and analyse big variety of data)
BigQuery provides serverless data warehouse service. This can be use to ingest data from various application and sources. It can be stored in realtime, later can be analysed, and queried.
While pricing model is based on storage we use, streaming and search, and query we make.
Cloud Pub/sub (Queue service for event driven architecture)
Pub/sub also known as Publisher and subscriber model. Cloud Pub/sub provides you async and scalable messaging service for event driven application(publisher). This act as middleware and provides decoupling from application producing huge amount of data and service that needs to process those data.
For example, an application producing log at massive rate for the usage which needs to be stored but storage process could be slower but Cloud pub/sub will hold those data in realtime. This will serve to subscriber like storage service to store data at its speed thus keeping the process in sync at scale.
Cloud dataflow (Realtime data analytics service)
Google cloud data flow offer a unified service to stream data into, transform data and analyse using various tools, language and run processing into various VMs.
Entire infrastructure providing is fully automated, data flow provide scalable solution to perform real-time AI services, stream analysis and much more.
Cloud data catalog (Manage meta-data)
Data catalog is also known as scalable metadata management service, Data catalog provides you overview of the data lake that we have, in form of various meta-data related to our diverse and large data asset within Google cloud platform.
Data catalog provides searching data entires which one has access too, can do data tagging for better organisation of data and provide column level security for bigQuery tables.
Cloud Natural Language API (Understand text, sentiment analysis)
Be it search or providing customer service using your bot, cloud natural language API gives you various context from the customer’s interaction or sentiments from the bunch of text say tweets which can be used for various purpose.
Cloud vision API(Read Images)
Google Cloud vision API helps you to extract data from images, what you see in images and what are the keywords that an image can be tagged with. We’ve seen live usage of it in Google photos, by searching person name, landscape, land, file, rain, sky, google photos gives you photos based on search result and those photos are containing context of your keyword.
Here is an example
Speech API(Speech to text, 120+ Languages)
Google assistant or Gboard keyboard, we use speech to text almost everywhere, Google Speech API provides you text output of the voice fed into it, currently it supports 120+ languages which is wide range to serve your customer of diverse demographic range.
Translate API(French to English)
Google translate we all are aware of, Google translate API is service in Google cloud which helps you providing realtime translation of the text fed in, current being used in various over the top applications to translate voice/text in realtime.
With ever expanding service of GCP specially in search and AI space, Google cloud have many unique services which other cloud platform like AWS is still struggling to reach nearby
And there are much more to go ahead and signup for Google cloud platform and earn upto 300 USD of credit for the first year.