Making the Case for Google Cloud: What do you need to know?

When considering each of the three major cloud providers, Datalere is often asked, “Why would I switch to Google Cloud?

What benefits does the Google Cloud platform provide that are different from what’s offered by AWS or Microsoft Azure?

The truth is that while Microsoft has been a proven enterprise solution for many years and AWS continues to hold the greatest market share of each of the cloud providers, Google Cloud is a comparatively newer cloud offering, and as a result, many organizations are not familiar with Google’s cloud products. Despite this, Google Cloud provides many capabilities that are unique to Google’s own way of operating and further, Google’s unique approach to cloud data management may actually be a better solution for many organizations.

See below for four reasons to consider Google Cloud for your data cloud migration.

  1. You can take advantage of what Google uses internally

Most of the cloud services offered by Google were originally developed for internal use by Google. This means that by using Google Cloud, you’re able to take advantage of many of the same technologies that are partially responsible for Google’s success. It’s widely accepted that Google excels at processing, indexing, and searching through large datasets gathered from and stored around the world, developing and deploying code in a dynamic 24/7 environment, and developing the cutting-edge machine learning that powers their self-driving cars, extremely fast search engine, image recognition, and more. When you use Google Cloud, you’re able to take advantage of these same tools, designed to power organizations of all sizes.

  1. Google’s solutions are unique

In a traditional cloud environment, there are traditionally two distinct ways to work in the cloud:

  • Spin up a virtual machine to deploy your own licensed software and scripts, or
  • Utilize one of the cloud providers’ managed services, which the cloud provider partially or fully manages on your behalf.

In a cloud platform such as AWS, for example, it’s easy to spin up a virtual machine using their EC2 service to deploy your own tools and scripts. This is nothing more than using one of data center computers to power your own software, scripts, and tools. When using one of AWS’ managed services, you’re typically using a repackaged AWS-managed version of an open source tool.

While the Google platform also offers both of these capabilities, Google Cloud also offers a greater proportion of highly managed services. BigQuery, for example, is a pay-as-you-query distributed data warehouse engine built on top of Google’s distributed technology and with a fast network that provides a different experience than using AWS Redshift or Azure SQL Data Warehouse, via a different pricing model and a more managed experience. In other words, BigQuery uses machine learning to optimize certain aspects of storage and performance, which means that there are fewer decisions and less management required from organizations who opt to store data in Google’s BigQuery offering.

  1. Fast access to storage

Google touts their fast 1 petabyte network as a differentiator between how they provide big data services compared to other cloud providers. For example, in a traditional big-data environment, a Hadoop server is spun up using local storage, and that server only processes its own storage. Using the Google Cloud platform and Google’s ETL offering, Dataproc, a set of Hadoop servers can be spun up and data can be processed without the need to resort to local storage. Because the network is so fast, each machine can process part of the data locally by moving it to its own memory space, which means that all of the compute power is used simultaneously.

This might sound complicated, but the key takeaway here is that the Google Cloud platform allows you to store and compute using big data without the need to own or manage your own local data center, which are typically expensive, and Google’s platform allows you to take advantage of lots and lots of compute power when working in their distributed big data offering.

  1. Leading via machine learning

Google built much of their technology on top of machine learning from the ground up. This is evident in their Google Cloud product offerings, developed with the thinking that you will use machine learning everywhere, in any of your organization’s applications, processes, or reports. Google Cloud truly makes it easy to deploy your own organization’s machine learnings algorithms as Google is an expert in this area. Google has deployed their own machine learning algorithms into almost every one of their product offerings, including their flagship database offering, BigQuery.

If you’d like to learn more about Google Cloud or any of the other cloud providers, feel free to contact Dalatere for a free consultation.

2019-01-07T09:43:18+00:00

About the Author:

Carlos Bossy is one of Datalere's two managing partners. Carlos delivers knowledge and skills from 25+ years of technology experience. Throughout Carlos’ career, on each and every project, he has demonstrated professionalism and skilled tactics within this complex systems industry that have yielded a very high degree of successful implementations.