Running Big Data Infrastructure : Five Areas That Need Your Attention

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January 9, 2014 by Updated January 3rd, 2024

big data infrastructure

Many businesses are starting to understand the benefits of Big Data. And those who have fully grasped the potential of gaining valuable business insights from it are scrambling to leverage this potential before competitors do. Unfortunately, some of them are becoming overeager and have consequently overlooked the most important component when embarking on a massive IT investment such as this – the infrastructure.

Big Data Intelligence

Unless your infrastructure has been architected to meet the kind of demands normally associated with a Big Data project, your project is bound to fail.

Here are some of the things that result from a poorly architected Big Data infrastructure.

Lost opportunities

The amount of data you collect to feed a Big Data project can be often overwhelming. And in seasonal industries like retail, marketing, and tourism, the volume of data can swell in a very short period of time.

Unless your infrastructure can rapidly scale in order to capture, process and analyze the surge of data coming from various data sources like social media, POS (Point-Of-Sale), and weblogs, you won’t be able to take full advantage of those valuable insights and gain competitive advantage on time.

Degraded experience for data users

Poor infrastructure can fail when subjected to a deluge of data. This can cause downtime, which would in turn deprive data analysts and other users of the information they need. Some of this information may delay the delivery of certain services, resulting in frustrated users, irate customers, and a damaged reputation.

Drained finances

First of all, an on-premise Hadoop cluster costs a fortune. That alone can carve a gaping hole in your bank account. Sadly, if your infrastructure is poorly designed, that will only be the start of your financial challenges. The cost of maintaining a shoddy Big Data infrastructure can shoot up your operating expenses and seriously hurt your cash flow.

Overloaded data staff

Poor infrastructure will not only fail. It can also cause Big Data systems to churn out inaccurate, unreliable, or totally false information.

And so, to make sure managers and executives get the information they need to make critical business decisions, data scientists, engineers, and analysts have to spend a lot of time firefighting or troubleshooting their systems. This will prevent them from embarking on more productive endeavors; endeavors that could contribute to the organization’s bottom line.

Limited innovation

A poor Big Data infrastructure can be slow to upgrade or incapable of supporting additional tools. This lack of upgradability and extensibility would prohibit data engineers from taking advantage of new features that may greatly improve queries, availability, productivity, and new data-crunching techniques.

What you can do…

We understand the importance of having an auto-scalable, reliable, easily manageable, and upgradeable Big Data infrastructure. We also know what it takes to build and operate one. Our background and experience have enabled us to put together an ebook highlighting key areas characteristic of such an infrastructure.

If your company is looking to leverage Big Data insights, our free ebook, “Running Big Data Infrastructure: Five Areas That Need Your Attention,” will help you get started in the right direction.

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