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Big Data, let’s charge forward ... move?! Photo courtesy by Leonard To, article by Kelvin To, Founder and President of Data Boiler Technologies, LLC Does that look like your Big Data project ... i.e. not going anywhere? What's wrong with treating Big Data likes any other technology projects? Didn't you just buy the latest elephant hardware for Hadoop and NoSQL database, so what is still missing?  Well, if you want to justify the return on investment (ROI) for Big Data, read on. Companies typically buy-in a technology when they see the new tool can enable them to accomplish a certain task. The ROI justification is straight forward: a positive value in performing the task over the cost of purchasing the new tool. It feels tangible and you can easily anticipate what this new tool can do for you. In fact, Big Data is not like buying another piece of network equipment. It is not about the tools, but the ideas. You may be tempted by the petabyte processing power of Hadoop at less than a fraction of a percent cost to the upgrades quoted by your existing vendor. The temptation is real because you have long been held hostile by the existing vendor who charges premium price for the necessary upgrades without giving you any alternatives. However, do you understand what this Hadoop and open-source revolution is about? You'll be wise to evaluate the fit-for-purposes, rather than give into the temptations to buy unnecessary things. In a nut-shell, Hadoop and other open-source technologies is a rebellion movement to liberalize the super-computer market. Hadoop distributed computing is a creative method to optimize the use of a number of low-end computers to match-up against an ultra-luxury super-computer. It makes Big Data affordable to the mass while open-source technologies enable the crowd to have the power to collectively compete with the prestigious few. To optimize Big Data's performance, creative methods are invented for specialized functions. Various NoSQL databases (column store, document store, key- value, graph DB, etc.) have different purposes. On top of that, there are the mix-and-matches of Flume/ SQOOP, HIVE/ PIG, B-Trees/ LSM, and more. There are relative advantages and compromises for each of these functions. Besides, how ready are you in letting go the structural schema of traditional database to gain efficiency in analyzing Big Data? If your feet are anchored in the old-school approaches, such as traditional ETL (extract, transfer, and load) how would you expect to move forward in the Big Data era? You may ask, what's the point of having the loyal and most trusted friend on top? I did say Big Data is a rebellion movement. You've got to take a little risk, entrust those who don't obey the old structural rules because they've come up with better ways to do things. You've got to admire those hackers, because they have sufficient knowledge about the weakness of the present systems and how to alter them for better results. You've got to see things differently: before you condemn the usefulness of Big Data, try to think of the wooden horse Trojan agents deployed to help you win in fraud detection, business analysis, unveil niche opportunities, and many more use cases.  In conclusion, don't perform a dog and pony show to claim you are doing Big Data by merely buying the tools. To reap the real benefits of Big Data, you have got to discern what it truly is about. To overcome fears of Big Data's chaos, hackers and what-not, you've got to engage with the inspirer who has the big picture and know what to do with these newer methods. At the end of day, Big Data's ROI is all boiled down to the brilliant ideas, not the tools!           
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