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Why SADA Systems Uses Google Compute Engine

 

[Editor’s Note]Today’s guest blogger is Simon Margolis, Senior Solutions Engineer at SADA. Simon works to design business solutions, focusing on the Google ecosystem.

Of all the Google technologies we’ve used at SADA Systems to deploy Google Apps for our clients, none of them has had quite as significant an impact as Google Compute Engine (GCE), which is an Infrastructure-as-a-Service (IaaS) solution and part of Google Cloud Platform. As a Google Cloud Platform Partner, we’re constantly looking for ways to help our clients leverage what the solution stack has to offer, from application development, to big data or in the case of GCE, large scale computing.

SADA’s own use of Compute Engine (GCE) has served as inspiration for finding new ways to deploy the product both internally and externally.  When short-term projects require significant machine resources, specifically high compute power, GCE serves as a low-cost alternative to physical hardware procurement.  Not only does the platform allow for a ‘rental’ model, allowing the business to pay for only the computing power needed, but the ability to spin up instances near instantly provides extreme flexibility for project teams.

In my role as Senior Solutions Engineer at SADA, I often work with SADA developers as well as our clients’ developers to provide adequate systems on which they can perform their tasks.  As a result of working with such a wide variety of developers, I’ve had the unique privilege of seeing many various use cases around development and deployment systems.  As someone who previously worked exclusively with physical hardware, the flexibility and power of working with GCE has been truly night and day.  Not only am I able to provide better solutions to the developers I work with, I am also able to dynamically work with the development team, altering our environment as the project progresses.

Compute engine diagram

One recent example of the benefit of leveraging GCE came when SADA’s development team was asked to execute a project for one of the largest names on the Internet.  In delivering this project, massive quantities of data needed to be processed and stored.  This type of task would typically require significant machine time to execute as well as tens of thousands of dollars in computing hardware and storage.  SADA was able to leverage GCE in this case by providing on demand, high power computing at a fraction of the cost compared to a hardware solution.  Beyond this, as developers would hypothesize on methodologies around processing this data, I was able to quickly, easily and cheaply spin up test environments to determine the best virtual environment to execute the project in.  In a hardware solution, the systems engineer is often forced be one step ahead of the project timeline and make their best guess at designing a solution which will fit all of the project’s needs.  Additionally, as development needs change, developers are either forced to ‘make do’ with what systems they have, or require the business to invest even greater capital into additional hardware.  With GCE, the systems engineer as well as the development team are given greater flexibility and control.

And as all of these benefits become apparent from the use of GCE over traditional solutions, I have to step back and realize that this platform only came out of beta in May (2013).  As a user of GCE beta, I can attest to the great changes and additional features integrated into the platform since it’s inception.  This leads me to be optimistic for the future of Compute Engine as there seem to be limitless uses for this type of technology in many diverse business use cases.  Not only is the cost, speed, reliability, and power of the platform valuable, but also its proximity to other Google services as well as the Internet backbone are huge benefits.  This network proximity means incredibly rapid transit times between GCE instances, as well as between other Google services (Cloud Storage, Cloud SQL, etc) and GCE.  The sheer speed, power, and flexibility have convinced me to utilize Google Compute Engine for as many projects as possible in the future.

Which product in Google Cloud Platform are you most interested in leveraging? What workflow are you looking to transform? Share your story, we would love to get your perspective!

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