In the past, the collection and storage of big data by business organizations come with lots of challenges but new technologies such as Hadoop has clearly eased many of these problems. However, even though Hadoop has been dubbed as one of the most efficient and scalable methods to make big data work for you, it does also come with its own set of pitfalls. This post will share on how you can avoid these DIY Hadoop mistakes.
A group of inventors has come together to design a revolutionary software framework that can be utilized with basic hardware for big data management. In the layman’s term, we can all now use simple and basic computers to turn them into a big data network. Because it is open sourced, Hadoop is easy-to-use, inexpensive, flexible, and powerful to help process large volumes of data. However, the open source nature of Hadoop also means that businesses are tempted to set up their own DIY Hadoop, opening doors to loads of problems.
Fun fact: Although it sounds like a secret code, Hadoop is actually the name of a toy elephant owned by one of the inventors’ son.
Mistake #1: Thinking DIY Hadoop is Easily Achievable
Well, we did mention that Hadoop is easy-to-use, but it is certainly not as simple as you think. If you have used open sourced software before, you would know that it’s a constant evolution with developers from everywhere working together to deliver new updates. You may have set up your DIY Hadoop before a new update is introduced – which will again force you to re-optimize your setup. You also have to consider the learning curve of using Hadoop, which has a life cycle of three months before resetting itself.
Mistake #2: Buying Cheap Server Hardware
Because Hadoop is often talked about as being low cost due to the free open-source framework and the use of commodity hardware, some businesses think that buying inexpensive hardware will do the trick. This is far from the truth.
Like all things in life, you get what you pay for. Cheap server hardware will probably mean that you will keep experiencing nodes failing frequently. While you don’t suffer a loss in the data, you will incur downtime for trying to fix the node failures, and other associated time-related losses. Don’t skimp on quality hardware; it will pay off itself in the long run.
Mistake #3: Not Being Prepared for Security Concerns
At the beginning, big data used to be processed within closed internal networks so this provided better security (depending on how the business organization sets it up). But since Hadoop is nowadays working on the cloud, you can definitely expect security concerns to arise. Many organizations do not have a solid security preparedness plan in place to deal with possible DIY Hadoop-related security risks, which can mean all your network resources can be in jeopardy.
Although there is a possibility of you making terrible DIY Hadoop mistakes, the good news is that the reactive solutions to mistakes you have made can be easily implemented. All you need to do is to install a Hadoop team that is alert and vigilant so that security breaches can be minimized or handled in the most effective way.