Chris Preimesberger recently opined for eWeek, “Enterprises are confronting the reality of big, fast, varied and changing data. It’s no longer about managing a small number of systems, but rather hundreds of systems and petabytes of data. Smart companies know that managing large volumes of structured and unstructured data, known as big data, is crucial to modern business operations and more complex business analysis. There is a problem: Relational databases, the dominant technology for storing and managing data, are not designed to handle big data. In fact, relational databases still look similar to the way they did more than 30 years ago when they were first introduced. Businesses focused on big data no longer can rely on the one-size-fits-all relational model; they must look toward new databases better designed to handle current workloads.”
One reason for this, according to Preimesberger, is that “Relational databases are not designed for change. Data in relational databases is arranged in rows and columns, with each row representing a unique entry and each column describing unique attributes. Data modeling must be done in advance and can take months or even years, depending on the system. Changes after the fact are time- and resource-intensive, and database-modeling projects can take many years and cost millions of dollars. Big data is constantly changing, requiring a database platform that is flexible and forgiving.”