Hadoop

Parallel DBMSs system which improves performance through parallelization of various operations, such as loading data, building indexes and evaluating queries. Vertica and Oracle 11g-r2 fall into the parallel DBMS trend.

Hadoop has been touted for big data analysis but it is well suited for small data analysis as well.

We help clients choose the best tools for their data analysis and data mining requirements. If your company is small and is expected to have less than 100 nodes traditional RDBMS with Parallel processing capabilities seem to be justified.

If, however with large data of the order of 300-400 TB, MapReduce framework built on Hadoop gives best results both in terms of simplicity and cost effectiveness.

Our experience with many clients has led us to believe that with the development of load balancer and data distribution schemes Hadoop can outperform parallel DBMSs even on 100 nodes. MapReduce framework especially excels in the reduced amount of work that is lost when a hardware failure occurs compared to the parallel DBMSs.

MapReduce framework also tends to be much easier to scale and fault tolerant compared to the Parallel DBMSs. Considering the ROI, Hadoop ecosystem is being implemented at a faster rate than any other technology in the data driven enterprises. Hadoop has established itself as an enterprise-scope data management platform for multiple data types and domains. Irrespective of your use of Parallel DBMSs in your company or MapReduce framework, if you need help with your small or big data analysis, our team of experts can help and support in achieving your goal.