With Hadoop becoming more versatile and useful to businesses of nearly any type and size, more organizations than ever before have started using this helpful big data tool. Hadoop has been around for more than a decade now, but the rise of big data analytics has placed the magnifying lens squarely over the platform. Its open source nature has allowed Hadoop to evolve over that timeframe, giving it more capabilities and solving some of the issues that plagued it from its early years. Having said that, Hadoop is not a perfect tool. Far from it, in fact. There are still improvements to be made. These arent the type of improvements that require wholesale changes to Hadoop. Consider these adjustments to be more of a tuneup — slight modifications that can make Hadoop a sleeker, faster-performing tool able to give businesses the results theyre looking for without some of the headaches they experience now.
One issue, often considered one of the biggest ones, is the need to improve Hadoops quality of service. Workloads in the Hadoop environment can often grow to be complex, especially as the number of data businesses collects multiplies. The more jobs that are run in Hadoop, the more chaotic the environment can become, affecting performance in various ways. With improved quality of service, businesses can ensure applications are running smoothly. This can be done in several steps, particularly through eliminating performance bottlenecks and freeing up resources so all jobs can be run simultaneously without interruption.
The quality of service issue speaks to an even broader point of contention regarding overall Hadoop performance. While many companies will speak to the merits of Hadoops performance early on, problems can still arise as more workloads are added. If Hadoop performance becomes unreliable, that can lead to clusters that are overbuilt, investment in hardware thats rarely used, and jobs going past set deadlines. As changes are made to Hadoop clusters with the addition of new data and jobs, they can often run straight into a performance wall. Jobs contend for the same resources. As mentioned before, the entire environment becomes predictable.
With this in mind, one area where Hadoop could use a noticeable tuneup is in its ability to handle mixed workloads and multi-tenancy environments. This issue is one that has seen definite improvements as Hadoop has evolved over time, but problems still remain. The more varied the jobs, the tighter the competition for resources within the Hadoop environment, affecting the overall work.
As problems crop up, most businesses will want to spend time troubleshooting in order to solve them, but this is another area where Hadoop needs some changes. Cluster health can be monitored, but administrators often still have an incomplete picture of what variables are actually affecting that health. Administrators have employed several methods to troubleshoot problems as they happen, but these techniques are often time-consuming and have varying degrees of accuracy.
One of the ongoing issues that perhaps needs the most attention is the need for better security in the Hadoop environment. This has long been one of the most talked about details that need improvement, especially as more businesses are storing sensitive data within the Hadoop environment. Using a single framework like MapReduce tends to keep things simple, but once more frameworks are added, the problem of security only grows from there. The happens in a number of ways, including the general absence of encryption for data at rest as well as data in motion. Each framework also usually comes with its own authorization features, making it harder for businesses to ensure that only the right people have access to the sensitive data they store in Hadoop. While there have been a number of solutions floated, including those involving what is flash storage, no permanent fix has been agreed upon with so many different variable involved.
These issues shouldnt take away from how valuable Hadoop can be. If anything, they show that the open source community takes problems like these seriously and is working hard to fix them. Hadoop has changed over the years, and it will continue to change as more organizations tap into its potential. As enough time passes, the issues will be addressed and better fixes will be implemented, turning Hadoop into an even more useful big data tool.
