Challenging Big Data: Using Less To Achieve More

In a picture perfect “Big Data world” useful analytics would easily be achievable with less. For instance, wouldn’t it be great to use only a single server instead of huge racks of servers to analyze data? Or, what if you could achieve Big Data analytics without having to hire tons of PHDs or data scientists? Better yet, what if The Internet of Things becomes so advanced that a Big Data database connected to the internet would be able to do all the dirty work for you. Now, that’s an idea!
Clearly, producing more while using less is the real challenge confronting Big Data today – a daunting task which SQream Technologies has set out to conquer since 2010.

Bigger Isn’t Always Better

Believe it or not, bigger isn’t always better, especially when it comes to Big Data. Yet, everything related to Big Data seems to require more. More hardware, more space, more data scientists, more coding, more maintenance… the list can go on forever.
A survey was recently conducted by CIO Magazine which concluded that 65% of the 369 companies surveyed had rejected using a Big Data technology simply due to the expense. And, just to give you an idea, the average total hardware cost for a standard rack of servers ranges around hundreds of thousands of dollars. Not to mention the space required to accommodate this database investment -entire rooms filled with floor to ceiling servers are often needed in the Big Data analysis process. The cost of this only continues to increase once you throw PHDs and data scientists into the mix. All of these elements are making it nearly impossible for SMBs and even enterprises to get their foot in the Big Data door.

Finding a Solution

The problem that most people are struggling to realize is that less actually is more when it comes to Big Data. Less hardware, for example, saves space which in turn saves costs and requires far less maintenance than huge floor to ceiling servers. This is obvious though – what isn’t so clear is understanding how to make sense of data without having to hire data scientists or PHDs who claim to know what to do with Big Data.
So, what have we done at SQream Technologies to ease the Big Data process? First, we took a minimalists’ approach to the problem and decided that less is more. Second, we created a Big Data software that requires very little – only a single server capable of producing super-fast, user-friendly insights.

Too Good To Be True?

While all of the amazing things that the SQream team has accomplished may seem too good to be true, it really isn’t, and this is why:
1)      We are using GPUs (graphic processing units) as the computing power behind our Big Data analytics database. Using GPUs has allowed us to create a Big Data technology which proves that less actually is more. Our software requires far less equipment – a single standard 2U server to be exact – and is capable of producing up to 100X faster insights from Big Data (compared with leading market competitors).
2)      We support ANSI SQL, which means you can use the same ETL and visualization tools as before, without having to hire a whole team of data professionals. No new coding is required with our software, making it easy to use and extremely user-friendly.
The Results:
Less cost, less space requirements, less people to hire and far less hassle.

Less Means More

I recently read a phrase in a Big Data article that mentioned something along the lines of, “The benefits of levering Big Data will outweigh the IT investments that come along with it.” This phrase sums up the mindset of many individuals which are currently utilizing Big Data.
Now, think back to the picture perfect “Big Data world” previously mentioned and start considering the possibilities of how less can be more when it comes to Big Data. Take a minimalists’ perspective and start adopting the mindset that the raw benefits of Big Data will outweigh nothing else because so little is required in the process.
 
Learn more about getting more for less when it comes to Big Data! Follow SQream on twitter for daily Big Data tweets, like our Facebook page, join us on LinkedIn and find us on Google+.  Leave your comments below too – we can’t wait to hear what you have to say!