When new software comes, then a lot of madness continues after it. This happened with Hadoop, too, and the fever is still on. For a lot of people, just the information that Hadoop is the best framework to work with big data is a big statement that inclined them towards it. But people still know very little about it. If you are taking an interest, let the reasons behind it be well researched and understood. Hence, if you are considering Hadoop for your big data-based project, then here are some of the things you must know about it. This will tell you when Hadoop is good to work with and where it’s falling short.

Here are the scenarios when Hadoop should not be used

1. Don't use Hadoop for real-time analytics. When you want to run a program to get processed data quickly, then Hadoop should be avoided. Hadoop is good for batch processing of data. It’s not great for real-time processing because of the time the program takes in processing batches. To put it, the response time of the software is high makes it slow for real-time analytics.

2. If you already have an existing data infrastructure, then do not replace it with Hadoop. Hadoop is not meant to be a replacement for the existing data processing system. It can be used along with the system, though. If you want to use it, you should use it parallelly with your existing data infrastructure.

3. You should not plan Hadoop used for data sets which are small in size. There are many efficient tools, which are great for real-time analytics and cost-effective too compared to Hadoop if you are considering small data sets. MS Excel and RDBMS are good examples. Hadoop is for big data, and the costing put in implementing Hadoop gets justified when you have loads of data to handle.

4. A lot of hard work and determination goes into handling Hadoop. If you are an amateur and are just experimenting with Hadoop, then you would end up messing with things. Also, where to use the tools and eco-systems of Hadoop as per project type is another important decision making which a novice cannot make well.

5. If you are handling very sensitive data in your project, then making a quick shift to Hadoop may be a difficult task for you, even if you have Big Data to handle.

When the use of Hadoop is correct and justified

Above is mentioned situations when using Hadoop doesn’t make sense. Now here are listed some situations where you should use Hadoop.

1. Diversity and size of data

If you are dealing with huge volumes of data, and the data is coming in various formats, and from various sources, all making a big pool of chaotic types and formats, then Hadoop is the right platform to use. This kind of data referred to as Big Data, get managed the best by Hadoop.

2. Use Hadoop based on your plans of handling data in the future

If you are realizing and anticipating the collection of huge amounts of data daily in your industry, and that too in all types, formats, sizes and from various sources, then in simple words you are dealing with Big Data daily. In that case, you must employ Hadoop with this understanding that you will be using this in the future too to handle data. Hence you must go actively into the development of data clusters. For this, you would need that kind of data infrastructure. You will have to start building clusters of data in small to medium size at the beginning with plans and scopes of the future expansion of the clusters.

3. Integration of multiple Big Data frameworks

If you have plans to use Hadoop in combination with other Big Data frameworks, then you can very well do this. Hadoop can be combined with the use of Mahout, Python, R, Spark, Hbase, MongoDB, BI, and Pentaho, etc. That's why, with such combinations, many complex analytical jobs can be done.

4. Data for life

One of the biggest advantages of using Hadoop is the maintenance and security of data for the lifetime. When you are using this framework, data stays on running and live for life. The cluster sizes have no limitations in size. You may keep on increasing clusters and their sizes. Simply adding data nodes to a cluster helps you increase the cluster size.

Now you have an idea of when to use Hadoop and in what kind of projects. Now you will be able to take a better decision in choosing your data processing and analytics platform.

Who to rely on for your Hadoop based database making and maintenance

Many people are claiming to know Hadoop. But most are amateurs and have hardly worked on a few serious projects actually to test their limits and take on challenges. Therefore if you are in search of a good Hadoop expert to help you the best with the complexities of the framework applied aptly in data processing and storing, then you need the best. The combination of cloud storage and remote data handling combines to give you something really interesting, which you must grasp while looking at its various benefits and future use. A load of data is taken by the cloud storage, and the maintenance and handling of the database are done round the clock by a team of experts when you allow remote handling of the database. For this, you can look for remote database administration experts like RemoteDBA.com.

Finally

Hadoop is a great technology which, when applied at the right place, can give you great returns. You need to prepare for the application of Hadoop with appropriate and planned database storage infrastructure. Also, your choice of project for Hadoop use should be apt. Things click best when they are compatible. Hence Hadoop must be compatible with project and data type both to yield you great result and satisfaction.