How SQL-on-Hadoop engine satisfies Microsoft Business Intelligence workloads

Have you heard on three popular SQL-on-Hadoop engines i.e. Impala, Hive, and Spark? All of three engines have their unique strengths and weaknesses that make hem particularly suitable for heavy Microsoft Business Intelligence workloads. These remarkable engines enable BI on Hadoop platform where data processing or data handling is much easier and meaningful too.

This is the task of technology evaluators to select best out of all three engines. They presents wonderful data layout where information is organized across tables and easy to access with the help of keywords. A table may have millions of rows together where similar data is organized together. Further we will discuss how SQL-on-Hadoop engine satisfies BI workloads.

  1. Works with big data
SQL-on-Hadoop engines work directly with big data. It analyzes million or billion of rows together without making any errors in least possible time. Response time is also negligible that was considered almost impossible few years back.

2. Performs on small data faster

SQL-on-Hadoop engines give interactive performance for small data fields and gives results faster in mili seconds only. The engines recognize query patterns and start working on them immediately. In this way, small sets of data would be handled or worked upon quickly.

3. Stable solution for users

The engine base consists of hundreds or thousands of data workers that perform concurrently under heavy BI workloads.

Reputed business owners believe that three engines are basic requirements when switching BI on Hadoop. This would be significantly beneficial for healthcare, medical, financial or telecom industries that have to handle billions of data files every day. Microsoft business intelligence software assures real time picture of your business that can be used by almost any technical evaluator.

After continuous tests, this has been proved that SQL-on-Hadoop engines can handle almost any workloads as needed by popular industries. But one engine doesn’t meet all the requirements sadly. So, business owners have to use all of them in conjunction to enjoy maximum benefits from it. Blended use of all three SQL-on-Hadoop engines will surely result into suit global demands of industries in best possible way.