There are several things people discuss when it comes to Hadoop and there are some wrong discussions. First, there is a small number of people believing that Hadoop is a hype that will end at some point in time. They often come from a strong DWH background and won’t accept (or simply ignore) the new normal. But there are also some people that basically coin two major sayings: the first group of people states that Hadoop is cheap because it is open source and the second group of people states that Hadoop is expensive because it is very complicated. (Info: by Hadoop, I also include Spark and alike)
Neither the one nor the other is true.
First, you can download it for free and install it on your system. This makes it basically free in terms of licenses, but not in terms of running it. When you get a vanilla Hadoop, you will have to think about hotfixes, updates, services, integration and many more tasks that will get very complicated. This ends up in spending many dollars on Hadoop experts to solve your problems. Remember: you didn’t solve any business problem/question so far, as you are busy running the system! You spend dollars and dollars on expensive operational topics instead of spending them on creating value for your business.
Now, we have the opposite. Hadoop is expensive. Is it? In the past years I saw a lot of Hadoop projects the went more or less bad. Costs were always higher than expected and the project timeframe was never kept. Hadoop experts have a high income as well, which makes consulting hours even more expensive. Plus: you probably won’t find them on the market, as they can select what projects to make. So you have two major problems: high implementation cost and low ressource availability.
The pain of cluster sizing
Another factor that is relevant to the cost discussion is the cluster utilization. In many projects I could see one trend: when the discussion about cluster sizing is on, there are two main decisions: (a) sizing the cluster to the highest expected utilization or (b) making the cluster smaller than the highest expected utilization. If you select (a), you have another problem: the cluster might be under-utilized. What I could see and what my clients often have, is the following: 20% of the time, they have full utilization on the cluster, but 80% of the time the cluster utilization is below 20%. This basically means that your cluster is very expensive when it comes to business case calculation. If you select (b), you will loose business agility and your projects/analytics might require long compute times.
At the beginning of this article, I promised to explain that Hadoop is still cost-effective. So far, I only stated that it might be expensive, but this would mean that it isn’t cost effective. Hadoop is still cost effective but I will give you a solution in my next blog post on that, so stay tuned 😉
This post is part of the “Big Data for Business” tutorial. In this tutorial, I explain various aspects of handling data right within a company
Tend to disagree. Building Hadoop or using Hadoop not from a commercial distributor does make sense in some cases (only you need a skilled team for it). I made the experience that using distributions you often not really get what you want and very often setting up Hadoop with distributions get more complex than plain vanilla.
However, I do agree that on-premise is out of date. 🙂