02-23-17 | Blog Post

The importance of infrastructure to Big Data analytics

Blog Posts

computer processing unit closeupEveryone wants in with Big Data, whether it’s to get more insight on patient records, customer behavior, or the college admissions process. But if you don’t have the right tools, you can quickly find yourself in the deep end wading through that much data. How do you stay on top of it?

Big Data starts at the infrastructure level. Once you have the proper hardware in place, then you can move up the stack and focus on your database and applications. To do that, you must first eliminate the bottleneck that infrastructure can create. Today’s infrastructure needs to handle enormous amounts of data, meaning it needs high-performance processing and storage power.

There are three major reasons companies invest in Big Data, and those reasons correlate directly to having the proper infrastructure.

  1. Access: As cloud consumers, we want more visibility into our customers and their behaviors, so we can create better interactions and develop new products and services for them.
  2. Speed: We want to process as much data as possible, as fast as possible, so we have as many insights in real time as possible.
  3. Availability: We want 99.9 percent uptime (or better) so we can quickly make decisions or correct errors when needed without impacting customers.

It is these values that are shaping the way many companies look at their Big Data strategy within their organization. To that effect, there have been three evolutions of infrastructure that have directly created benefits for the above listed values:

  1. Getting rid of mechanical components by moving to all-flash. With fewer moving parts, it’s easier for the data to travel. Faster travel times leads to more efficient data analysis.
  2. Collapsing the distance data has to travel. In the early days of computers, the memory and the CPU used to be across the room from each other. That meant the raw data had to travel quite a distance before being processed, which then took additional time with all those mechanical parts. Now, we have our components within millimeters (or less) of each other. The less distance an object has to go, the shorter time it takes for it to travel, and the faster we can process the information.
  3. Adding more data to be processed. When you can widen the data pipe, it allows for more signals to be processed at the same rate. While we might only be able to make data process so fast, there’s nothing to stop us from processing more of it at the same rate.

It’s up to you to decide how to handle all of your data, but you must have your infrastructure in place first. Once you architect the right hardware to easily handle the large volumes of data Big Data has come to signify, you can move forward with your Big Data strategy. Today’s infrastructure has come remarkably far from the past, and you should look for a solution that’s designed to handle high-performance loads. Learn more about Online Tech’s all-flash cloud infrastructure and how it can help your business fulfill its Big Data needs.

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