How In-Memory computing can accelerate your SQL RDBMS

How In-Memory computing can accelerate your SQL RDBMS

The promises of In-Memory computing are great in theory, but does it work in the real world?

This talk looks at three use cases :
- SQL analytics acceleration using a columnar data store with examples from Walgreens, General Mills and Gilead Sciences
- SQL transaction acceleration using an In-Memory SQL data cache with examples from eBay, T-Mobile and China Mobile
- SQL storage acceleration using Intel Optane DC Persistent Memory




Regency Ballroom C


Oracle TimesTen Product Manager
I now live in the Cloud, but I used to integrate with MVS, AS/400, Tuxedo, CORBA, SOAP, sockets and REST.
I started using RDBMS with DEC Rdb/VMS and progressed to Oracle 5, 6, 7, 8i, 9i, 10g, 11g and 12c. I fell in love with Oracle TimesTen In-Memory Database as it is a really fast, simple and highly available RDBMS.
I have worked as a consultant, developer and product manager at Oracle over the years. My technical interests are making things go fast [OCI, ODBC and PLSQL] and getting things to work together. I am a fan of Golang and I am looking for a good PowerPoint LLVM compiler ...
I am now focused on Oracle TimesTen Velocity Scale. Velocity Scale is a shared nothing, scale out, In-Memory RDBMS based on TimesTen.

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