Redefining Scalability in the Era of Big Data Analytics
Scalability has long been a concern, but now it's taking on new dimensions. Here are some critical growth considerations for a big data-dominated landscape.
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The Information Age has matured beyond our wildest dreams, and our standards need to evolve with it. Big data analytics is becoming increasingly intertwined with domains like business intelligence, customer relationship management, and even diagnostic medicine. Enterprises that want to expand must incorporate growth-capable IT strategies into their operating plans.
Scalability has long been a concern for corporate decision-makers, but now it's taking on new dimensions. Here are some critical growth considerations for a big data-dominated landscape.
Companies need flexible infrastructures if they want to use Big Data to reduce their operating costs, learn more about consumers, and hone their methodologies. The real question is how to implement IT systems that expand on demand.
Organizations like Oracle and Intel point to the cloud and suggest that firms invest in open-source tools like Hadoop. For many big data users, the fact that you can purchase appliances that have already been configured to work within these frameworks might make it much easier to get started.
It's one thing to implement a data storage or analysis framework that scales. Scaling the vital connections that deliver information to your system is another story.
One potential scalability integration workaround could lie in purchasing a complete system instead of just an appliance. Many business architectures are designed to interface smoothly with third-party tools. For instance, Adobe's Marketing Cloud caters to omnichannel outreach and employs big data to let you work with various experience management tools and monetization platforms. Tools like Salesforce Marketing Cloud use MongoDB to permit scaling natively as you go.
These examples implicitly use big data analytics to deliver personalized content, but there are countless other applications. There are many different ways to create a system that garners insights from big data. As thought leaders like Scott Chow of the Blog Starter point out, however, ensuring that all the parts can grow uniformly is critical to your success.
Not all algorithms are equally proficient at solving the same problems. A programming language that parses limited information with flying colors might crash and burn when it's treated to millions of data sets.
Big data demands a bit more planning foresight and less plug-and-play than some other areas of computer science. For example, the R language is made for statistical computing. When you attempt to develop scalable scripts, however, you run into numerous problems, like its in-memory operation, potentially inefficient data duplication and lack of support for parallelism. To put this arguably powerful tool to use in big data environments, you'll need to adapt your approach and refine your understanding, preferably with the help of data scientists.
Another scalability quandary in big data analytics involves maintaining effective oversight. While it's relatively easy to watch a process to discover some conclusion or result, the genuine control means also understanding what's happening along the way. As you scale up, reporting and feedback systems that let you manage individual processes are critical to ensuring that your projects use resources efficiently.
Are Your Big Data Analytics Truly Scalable?
Big data is getting bigger, and the meaning of scalability is changing at blinding speed. As you move forward, it's going to become increasingly important to build systems that let your problem-solving strategies evolve to match.
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