I am not yet an H2 user, but I’m thinking H2 IMDB may be an excellent 
solution in the following context:

 

   - 
   
   Populate an H2 IMDB from the results of a set of queries against a 
   conventional database (typically SQL Server) on the *on-premise server*. 
   Note that the amount of data generated will never be more than about a 100 
   Kb.
   - 
   
   Programmatically use H2’s SCRIPT command to create a string 
   representation of the entire database.
   - 
   
   Send that string via Web Service to our cloud server , which sends that 
   string to a message queue (ActiveMQ).
   - 
   
   Our platform retrieves the string and uses H2’S RUNSCRIPT command to 
   recreate the H2 IMDB on our cloud server.
   - 
   
   Execute queries against the H2 IMDB.
   
 

Note the following:

   - 
   
   While the sub-database is generated from a potentially very large parent 
   database, the sub-database itself will be small (never more than 100K), as 
   it’s just representing the information from a single insurance policy. 
   - 
   
   The sequence above will be executed repeatedly as we retrieve, 
   distribute, queue, and convert a set of policies over time. 
   - 
   
   The overall throughput is moderate, and it is a single-threaded scenario 
   as we are never trying to convert more than one policy at a time.
   
 

Is this approach to aynchronous distribution of a *sub-database* (subset of 
a much larger database generated using a series of SQL queries) reasonable 
using H2? Is there a better way to do this, perhaps not using an IMDB? I 
have thought about trying to retrieve the data as XML (not hard to do in 
SQL Server), but it would be very difficult to build up a single, unified 
representation of a sub-database that has multiple tables with complex 
relationships, etc. If we were doing that, we my as well convert the source 
data to ACORD (an insurance data standard)—something we do in other 
circumstances but not appropriate here. We basically need an optimal way to 
create, serialize, distribute, and deserialize a sub-database with minimum 
fuss.

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