2016 marked the 10-year anniversary of Hadoop, a name closely associated with “Big Data”. Prior to the advent of Big Data, companies invested in solutions that were not forward-looking; they could only address the immediate needs of businesses. These traditional solutions were way too expensive, especially considering their very limited capabilities.The data landscape then was quite different from what it is today. Significant upfront investments were required to handle just a few dozens terabytes. Scaling was an issue, as most solutions incorporated specialised hardware and were built with a scale-up rather than a scale-out approach. Things started changing with the emergence of multi-core processors, distributed storage and the rise of social media. Organisations which were driven purely by use cases, now started looking at things from the other end, “the Data.”
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