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Datawarehouse Trends – In-memory

What does the emergence and growth of In-memory databases mean for  Datawarehouse? 

 1)  In-Memory database server systems are  fast.  Consideration should be given to hardware purchases, configuration and consolidation. I’ve been working with solidDB over the last few months and the speed differences are obvious. The Query response and commit times are fast.

 2)   Traditionally analytics\DSS databases are maintained separately from transactional systems. Characterised by different memory, storage requirements and IO patterns. DBAs maintain Datawarehouses based on established principles.

In-Memory databases encourage analytics and transactional to use the same     database , on the same IO subsystem.  DBAs will need to consider this approach and  analyse the implications. For example, can Resource Governor manage cap resource usage per different type of activity? How  can this be managed?

3)  Databases will tend towards  the middle tier. A mixture of DSS models, master data framework and database as a service may become a favoured architecture.  Consolidation models may change.

4)   Row based versions of RDBMS (OLAP\DSS) may migrate to a column based approach and in-memory architecture. Initially I can see benefits for read-only data sets such as OLAP. Mainly because OLAP is characterised by complicated queries over large data sets.

 One benefit of column based approach is data compression. If the column data is of the same data type , are the compression ratios more effective?

 5)    BI solutions will exploit IMDB database systems. The increased performance will make the BI solutions more attractive. Investment may increase, leading to more development , mainstream products.

I’d like to hear your thoughts .

Author: Jack Vamvas (http://www.sqlserver-dba.com)


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