The rapid adoption of Hadoop across the enterprise has created a shockwave that’s put many Big Data and analytics professionals on their heels. As a result of the maturation of Big Data technologies like our favorite yellow elephant, a large majority of the IT vendors in this vertical have released Hadoop capabilities on top of their existing software solutions to meet growing demand. Given the explosion of data volumes, types and sources over the last decade, it’s no surprise that Hadoop is being adopted at this rate. As a result, traditional data warehousing technologies are getting pushed to the back burner. Does this spell their demise?
While data warehouses traditionally utilize a single relational database that serves as a central store, Hadoop’s file system is designed to work across many machines to handle very large data volumes that cannot be encompassed by one machine. Thus, Hadoop has become the perfect solution for businesses that are expanding their data footprint, which essentially represents every company in every vertical.
None of this is to say that Hadoop will become the market-norm overnight. Many companies who currently utilize traditional data warehouses simply don’t yet see the cost-benefits to changing over their entire data architecture. Others don’t have the upfront capital to start collecting every bit of data for analysis and will continue using legacy techniques for the time-being.