Oracle Updates TimesTen In-Memory Database 11g

Release 2 improves performance, scalability for OLTP and analytics in-memory data management.

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Oracle has released an update to the company’s application-tier in-memory database, Oracle TimesTen In-Memory Database 11g Release 2, designed to run in the application tier to provide both rapid transaction response time and high throughput required by mission-critical applications.

The release incorporates performance and scalability improvements for real-time online transaction processing (OLTP) applications and introduces new functionality to support analytic applications with real-time in-memory data management. The new features include support for more concurrent users, higher volumes of requests, and more sessions.

Now shipping with Oracle Communications Billing and Revenue Management, Oracle TimesTen In-Memory Database enables Oracle BRM to respond faster and achieve higher throughput; it can support more subscribers than prior releases.

Oracle TimesTen In-Memory Database for Exalytics will also be available for Oracle Exalytics In-Memory Machine as an application-tier in-memory database cache. This new release enables Oracle Business Intelligence Foundation Suite to achieve up to 20x response-time improvement for speed-of-thought visualization and up to 5x more data to be stored in-memory with advanced columnar compression capabilities.

With this release, customers can:

  • Increase performance and scalability for real-time OLTP applications via parallel replication between Oracle TimesTen In-Memory Database instances, parallel cache data synchronization with Oracle Database 11g, enhanced data access interfaces for applications, and improved query processing and optimization to boost response time and overall throughput

  • Use Oracle TimesTen In-Memory Database to support analytic workloads through new SQL aggregation and analytic functions as well as optimization for complex query processing

  • -Benefit from improved compatibility with Oracle Database through support for native large object (LOB) data type support (BLOB, CLOB)

More information is available at

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