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5 Common Problems in Hadoop along with Tips to Fix Them
We have had heard a lot about Hadoop, the buzzing solution around. Although it is synonymous with the world of big data, it’s not easy to adopt.
Yes, like the movie “Spiderman’s” famous dialogue, “With great powers come great responsibilities”, with Big Data Hadoop come greater challenges. While it’s the best solution to store and process huge amounts of data, there are common problems in it that must be fixed before adopting technologies in its ecosystem.
Here Madrid Software Training Solutions has complied five common problems you may face and how to fix these…
Complexity that makes Hadoop Confusion: There is a loophole which has earned it a little bad reputation and that’s its complexity. While there are many companies which are focusing on their own distribution system to make it easy to use, still there remain some complications.
Picking the right distribution is a real challenge, specifically as each of them has different Hadoop components.
If you want to solve these challenges, then you need the knowledge of its ecosystem, vendors and their various offerings. This may sound like an impossible task, however, by reading articles that give insights of comparing different distributions, speaking to the consultants and running a roof of concept can help you regarding the right distribution to suit your business needs.
Finding the purpose, the use case: Before your business has encountered the confusion phase of the Hadoop ecosystem, there should be raised the serious question about why use this technology in the first place.
Should the company be dedicating time and its resources to this solution if the problems that have risen and are trying to be resolved can be done some other way? Of course, not!
The analyst firm Gartner highlighted this problem, which according to a 2015 study, almost half of the 284 Gartner Research Circle members found it hard to adapt to the technology as they were not sure how it would provide business value.
In order to solve this issue, businesses have to decide how much data they have, pay attention to the business issues they are dealing with and consider whether it’s the right technology to adapt. Having a strategy in the first place is very important and if any company is not sure about the use cases then most of the vendors have many examples to list on their site.
Gap of Skills: Well, this is something which doesn’t seem like new for big data Hadoop. This problem is persistent across all technology sector, however, it has been extended in Hadoop world.
As we said earlier, it’s a complex solution, and in the same research where Gartner underlined the issue of finding a use case, the skill gap turned out to be the biggest challenge in its adoption.
It is required to have skills in big data Hadoop. It’s not possible for developers to just click on the download, install and start working on it. In fact, it needs at least four servers to work exclusively for it.
To get rid of this problem isn’t easy and there isn’t any cheat sheet or quick fix. As we have mentioned that the vendors are working on it to fix the challenges, but it’s gonna take time.
Businesses, however, can train their manpower internally to be able to get familiar to this technology. The second thing you can do is to find the right software. This is where having an understanding of use case and various vendor distributions can lend you a hand.
Integration & Management: This is the area, which one should cover when working over the strategy with the technology. In particular, it should be figured out who is going to maintain it and will it substitute the existing system like the analytics tools or existing database.
Hadoop is basically used in sync with other technologies, however, it becomes important to find out what it will work together and what it won’t. Fixing this problem as soon as possible will help you avoid problems later.
Same like other issues, vendors have been focusing on it. Most vendors now provide tools and guides for its integration and management.
Accessing Data: Eventually, everything has been done and hurdles to Hadoop have almost been fixed, but still there is something left and that’s the transformation of data into meaningful management information.
For the storage and processing of data, Hadoop is excellent. It is at its most fundamental step a batch processing tool, but as far as analytics is concerned it doesn’t offer a lot of things.
Accessing data from multiple data source is easy, but it lacks interactive features means there remains the skill gap issue and the problem of delivering value to the business.
With more and more vendors providing support in this area in their own offerings, there have been made big moves. For example, IBM intends to build most of its analytics tools featuring Aache Spark, on the other hand, SAP has also integrated the technology to its S4 HANA platform.
At some extent this problems can’t be fixed by the businesses all alone, but vendors need to make the technology easily accessible to every level of business.
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