Dear Drill Community,

I am working of evaluating Drill as an OLAP query engine for a new open source 
product in the education domain.  Just to give you some background: the 
solution we are building is a reporting tool for the student assessments 
outcomes. When a student takes an assessment test, it gets scored and then the 
outcome of the scoring along with the assessment details is loaded into the 
reporting warehouse.  We are building that warehouse and the reporting tool on 
top of it. As part of the reporting tool we need to report on individual tests, 
as well as the aggregated data. The solution has to have an OLTP data store for 
the individual test reporting, and we are considering Drill for the analytical 
reports. As you could see our data is pretty well structured and the volume is 
not ‘big’ data, but is estimates at about 150+ million assessments outcomes.  I 
see that Drill performs the best with Parquet file store. And I have two 
questions:
1.     What is the best way to create Parquet files as the data flow in. Please 
consider that we need to support data update and deletes. Also the volume of 
data flow may vary. I see that some people capture data for a day and then 
convert them into Parquet. Is this the way to go?  I am concerned that:
a.     we may end up with a lot of small size Parquet files
b.     for delete/updates we will need to delete the existing files with the 
changed records and recreate them. How to handle the system being up while we 
are doing this change?
2.     Would it be better for us instead of creating Parquet files, point Drill 
to our OLTP data store? We know that OLTP data store by itself does not perform 
the queries we need within the desired speed (under 10 sec.). Would Drill be 
able to imrpove performance in this case?

Thank you in advance for your help!
Alla Gorina

Principal Developer

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