Why Google Cloud on Data Analytics?
Data analytics is one of the well-known needs in organizations and has become essential, at least for “digital” companies.
There are a lot of business advantages with data analytics; however, to ensure that we can deliver useful insights, on time, with quality, confidence and in a cost-effective way, the base layer, namely the infrastructure/provider, the tech stack, processes, and the approaches are very important points.
Assuming we already know the advantages of using cloud (if you don’t know yet, read this article), we must decide which cloud provider we should use.
This decision is one of the most important ones. Cloud providers offer you different kinds of services, so it’s necessary to perform an assessment to find the “best fit” provider according to our specific needs. This choice will directly impact in time, cost, complexity and success of your Data Analytics Journey.
This article will focus on some facts about using Google Cloud as a cloud provider for Data analytics projects.
Before getting into details in Google Cloud Platform(GCP), it is important to know the mission of the creator company: Google! Google’s mission is:
With this in mind, we can easily understand that since the creation of Google, their challenges have always been about Big Data. Google has dealt with all the main challenges of Big Data since its creation:
So, how did Google solve it? How much has Google influenced the world of Big Data as we know it today?
Google has a long history of innovating in the data space and contributing to the Hadoop OSS echo system.
Even before the launch of Google Cloud Platform in April 2008, Papers on GFS and MapReduce are widely credited for creating the first versions of Hadoop.
Continued papers have resulted in projects such as HBase, Crunch, Drill, Beam, and TensorFlow. Google has been democratizing these internal technologies through GCP projects since 2011, starting with BigQuery.
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Nowadays, Google has a wide range of products, all of them with a large number of users. Looking only for products with over 1 billion users, they have Android, Drive, Google Play, Chrome, Google Maps, Youtube, Gmail, Search and Google Photos.
Among other characteristics, something common in all of these products is that all of these products run on Google Cloud Platform.
These products process a lot of information, requiring smart analytics and AI functionalities. Some examples: Traffic suggestions on Maps, recommendations on Youtube, generated albums on Google Photos, etc.
These facts show us daily that GCP is a powerful platform for addressing the complexity of data challenges.
Regarding the data processing inside the organizations, we can always find some specific needs according to the organizations.
For example, depending on which employee profile will interact with data, we can have business users, data analysts, data engineers, data scientists, and machine learning engineers, among others… It results in different needs regarding the toolset to interact with data.
As a very mature company dealing with Data, Google offers a wide range of products envisioned to answer the different needs of organizations. The Smart Analytics suite is also widely integrated with the AI suite – Vertex AI.
The image below shows the diversity of services for each data lifecycle phase, the user profiles that could interact with these services, and the support and governance services, for instance, Dataplex.
In summary, we can conclude that Google Cloud Platform is a very powerful and stable platform, offering a wide range of Smart Analytics and AI services, with different options that can certainly address your organization's custom needs.
If you would like to know more about Google Cloud or even how can Google Cloud Platform address the custom needs of your organization, feel free to reach us.
BI4ALL has a Center of Excellence in GCP, which can help you in your Data Journey in GCP.
Written by Vasco Silva , Manager / Architect BI4ALL