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3 Reasons to Avoid These DIY Hadoop Pitfalls

Megan Ray Nichols

Big data has very quickly become an industry buzzword, and with it has come new hardware and software solutions to make it a viable tool for just about every industry. If you’re looking for a new, more efficient way to make big data work for you, Hadoop might be the tool for you. While it might sound like an escapee from the Lewis Carroll poem “Jabberwocky,” Hadoop is actually turning into one of the best ways to keep your company’s big data in check.

Like any new technology, though, it does have its pitfalls. What is Hadoop and how can you avoid the three primary pitfalls of its implementation?

What Is Hadoop?

hadoop pitfalls

Hadoop is the name given to a new software framework designed to use basic hardware to manage big data. It is open sourced, meaning users can change, tweak or improve the software as needed. Basically, it provides the software to turn large pieces of relatively simplistic hardware into a big data network.

You don’t have to spend thousands of dollars on servers or server hardware with Hadoop. Instead, you can link basic computer hard drives to run in parallel. It’s the same concept that allows researchers to turn a group of linked gaming consoles into a supercomputer, only now it’s being applied to big data.

A well designed, fully functional Hadoop can be a game changer for anyone who utilizes big data. It’s scalable, making it easy to upgrade or downgrade depending on the amount of data being analyzed, and is eminently flexible as well. If properly set up, Hadoop can also be much more cost-effective than traditional big data hardware or software.

Those are just a few benefits of a well-run Hadoop system. It can, if used properly, totally change the game. Because it’s open-sourced software, companies are often tempted to set up their own do-it-yourself Hadoop, which can lead to tons of problems if not done correctly. The three most common pitfalls DIY Hadoops face are cost, underestimated complexity and security challenges.

Pitfall 1: Cost

With an easy-to-use, open-sourced piece of software like Hadoop, it’s easy to assume you can pick up the cheapest hardware to go with it. After all, the software itself is nearly failure proof. If a node goes down, you’re not going to lose any information because it’s available in multiple nodes.

pitfalls in hadoop

Unfortunately, if you choose the cheapest hardware to go with your new software, you will inevitably have nodes failing all the time. Failing nodes means downtime, which potentially means monetary losses as well as additional costs to repair them.

Good server hardware might seem like quite an investment, but when paired with Hadoop, it could easily pay for itself. As with any technology, you get what you pay for. Don’t sacrifice productivity in favor of cheap hardware.

Pitfall 2: Underestimated Complexity

While we did just call Hadoop easy to use, it isn’t as easy as plugging in a new hard drive – there is definitely a learning curve when it comes to this software. Because its open sourced, new updates and upgrades are being pushed through constantly as developers from around the world find new and more efficient ways to process data.

A new update could turn your entire operation on its head, so you need to be prepared for the ways in which the program could change in the future. That learning curve we mentioned for discovering how to use Hadoop resets itself every three months, and it gets even more complex with each new subproject that merges with Hadoop.

Pitfall 3: Security Challenges

Anytime you set up a networked system, there are always going to be security challenges. Initially, big data and programs like Hadoop were used primarily in closed internal networks. This allowed the data to be processed in-house while still providing an extra layer of security against breaches. Newer applications of the software, however, have moved to the cloud and to external networks, creating many new security concerns.

architecture of hadoop

These challenges can be handled without too much trouble, but it does require your Hadoop team to remain vigilant and to have a plan in place for each new software upgrade. Tools like proper user authentication, limits on the data that can be accessed and histories for each user who accesses the data are good ways to prevent data breaches and keep security challenges to a minimum.

Overall, Hadoop is a great tool that can change the way your business uses big data, but it is definitely not something you want to jump into unprepared. Proper preparation prevents poor performance, as the old saying goes, and preparing your business to adapt a program like Hadoop should be your first priority before you start installing software or plugging in servers.

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