Oracle Magazine, Jan/Feb 2018
ORACLE MAGAZINE JANUARY FEBRUARY 2018 50 to make groundbreaking research possible A number of Oracle solutions have been tested through CERN openlab with CERNs input helping to guide the development of new database features The organization runs a combination of Oracle Database 12c Release 1 121 and Oracle Database 11g and is planning to take advantage of Oracle Database 12c Release 2 122 One feature that CERN uses is Oracle Database In Memory which accelerates key database operations and uses a unique dual column and row datastore to support analytics and production workloads on a single database The big breakthrough with the in memory technology in Oracle Database 12c is that you can do high performance analytics against your live transactional data Mendelsohn says For CERN one key advantage of the in memory capability is transactional integrity for specific scale out applications Grancher says Rather than maintain data caches at the application level the entire database can be stored in memory ensuring a single source of consistent information and guaranteeing transactional integrity Grancher says one project has already changed its plans to take advantage of the new capability This can really change the way people think about computing architectures he says The in memory enhancements are one of the features were going to be deploying in Release 122 as soon as possible CERN is also looking at the possibility of using in memory capabilities together with Oracle Active Data Guard to flow data workloads from primary LHC databases to secondary stores for analysis With Oracle Database 12c says Mendelsohn you can now create an Oracle Active Data Guard standby for your transaction system and create only the in memory column store technology that supports those high performance analytics on the standby database You can send the transactional users to the primary database he says and you can send your business analytic users to the Oracle Active Data Guard standby where the data might only be a few seconds behind the data in the production system Now you have the best of both worlds where you can completely isolate the transactional users from the analytic users but still deliver incredible high performance analytics against data thats almost real time Big data analytics will support an expanded range of use cases across the laboratory says
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