5 reasons for using Hadoop-as-a-servce

Following are the reasons because of which you should be using Hadoop as a Service :

1. Cheap Infrastructure : As per Forrester, organizations save 20-60% in Big Data projects as compared to on-premise infrastructure. Even if the you have your own infrastructure, the biggest question is, is it ready for the big data project ? How much money, time and resource you will still have to be deployed to get the project started ?

2. Enable faster advanced Analytics : Most the organization have their current infrastructure supporting BI- which is just about descriptive analytics, the real challenge is to embrace the predictive analytics, machine learning, text analytics etc. A lot of data has to be churned and make it ready so that actionable insights can be drawn from it. Organization still don’t realize that they have actually tripled the data sources, a lot of data is flowing around which when fed in to advanced analytics system, can help them to solve a lot of business problems.

3. Scale to unlimited resources in minutes : Hadoop Projects usually done  in cluster environment because of MapReduce. How can set you up those clusters quickly and get started with he analytics ? A cluster requires a lot of upfront cost to be set up and if you are a startup, or business which doesn’t have IT resources, you will not be able to acquire those resources.

And the other side of the coin is, you might not need those clusters 24X7. Why would then you want to waste your time and resources which are not required for your business ?

4. Self-service analytics platforms : Consider that you have acquired the infra, and have relevant sill set, would you not prefer something which is already been tried and tested and have given results ? Why would you want to write the algorithms from the scratch when you can customize them and quickly make them ready for your requirement ?

5. Faster deployment:  There are services available which can get you started with in hours. Few of them are :

  • Elastic MapReduce from AWS
  • BigInsights from IBM
  • HDInsights from Microsoft


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