Although Hadoop has emerged as the panacea for all types of computing challenges faced by modern businesses when it comes to predictive analytics, certain roadblocks still remain. The platform remains poorly understood in itself, and enterprises are struggling to get a handle on scalability. This results in below-par performance and in worst cases, data loss. What can be done to avoid these problems? Well, there’s no quick solution, but the point is to realize that Hadoop is an entire ecosystem that needs to find its own purpose.
Here are some pointers:
- Interconnections: A Hadoop system is not much use if it can’t connect with your legacy systems and read/write large amounts of data. Sure the integration means extra work, but that’s the only way to extract more from the technology.
- Data strategy: The use of Hadoop should not be a knee-jerk reaction to the need of predictive analytics, but needs to be woven into a comprehensive data strategy. At the end of the day, there should be a single central repository of data, and not multiple clusters of technologies.
- Deploy use cases: The best approach for getting Hadoop right is by starting small and working your way up. A single use case should be enough at the start, while others can be connected as work progresses.
Scalability is everything when we talk about big data, and it’s so much easy to achieve that through Hadoop.