In Memory Database | In Memory Analytics Engine | In Memory Engine
In-Memory Database is a memory-optimized relational database that provides applications with the instant responsiveness and very high through put required by today's real-time enterprises in a wide range of industries. A Database resides entirely in memory at run time and is persisted to disk storage for the ability to recover and restart. Applications access the database using the JDBC, ODBC, OCI, ODP.NET and/or Pro*C/C++ interfaces. It is fully transactional, persistent, and highly available with transactional replication. And is typically deployed in the middle-tier with the applications; it can be run as a stand-alone database or as an in-memory cache database for an Oracle database. Using in-memory cache database provides applications the power of SQL, the speed of RAM and the reliability of a proven product with automatic data synchronization between the in-memory cache tables and the backend Oracle database.
At the Oracle OpenWorld annual user conference last week, Oracle announced enhancements to the Oracle Database 12c platform, including a new in-memory option for both analytics and transactional applications and an extension of its engineered systems portfolio to cover backup and logging. The new Oracle Database In-Memory Option is targeted at improving performance with analytics and OLTP (online transaction processing).
The in-memory enhancement is in line with overall trends that Ovum has identified with data platforms adding the capability to handle more diverse workloads. Oracle also announced a new appliance with a long – but very descriptive – name: the “Oracle Database Backup Logging Recovery Appliance.” It is designed to make backup and recovery more manageable, reliable, and up to date.
The latest addition to Oracle’s engineered systems line does not add new capabilities beyond those available from software-only solutions from providers like Informatica. But it fills an important gap for Oracle’s engineered systems platforms to make them a more complete, self-reliant platform environment.
In-memory a natural step in data platform evolution:
As enterprises face snowballing demands, not only to process larger or more diverse data sets, but also to more readily blend analytics into operations, transactional data platforms are adding new analytic capabilities that are increasingly set apart from the OLTP system. Oracle has joined IBM in appending capabilities to its core database platform that increase the performance and capability of analytics with separate analytic engines.
Killing two birds with one stone:
The obvious benefit is that it accelerates analytic processing compared to conventional disk storage. Oracle claims OLTP processing that is twice as fast, and analytic processing that is up to 100 xs faster over the initial release of the 12c database generation.
The key to these benefits is that incorporating in-memory storage allows database designers to reduce or even eliminate analytic indexes. That, in turn, can greatly reduce database footprint, because indexes can multiply storage requirements geometrically (depending on the range of queries for which the database is modeled). And, while analytic indexes are used to speed query performance, burdening a database with multiple indexes typically slows OLTP performance.
In-memory is the latest refinement on the established practice of data tie ring, where the most frequently used data is placed on the fastest storage. Traditionally that was on disk, but with the pricing of memory having dropped (until recently), it has become affordable to add memory as the fastest data storage tier.
Oracle is not the first to exploit in-memory; for instance, IBM, Tera data, and SAP HANA support it to varying degrees. Oracle is unique in that it is pairing it with a disk- and in-memory-based row store that will instantly replicate data to columnar tables. By comparison, SAP HANA (which offers a full in-memory store) requires the user to make an either/or decision of storing the data in row or in columnar format. By comparison, Teradata is only an analytic platform and IBM does not automatically treat in-memory columnar data as replicated from the transaction row store side.
It reduces overhead and adds currency by conducting backups continually, with automatic checks to ensure backups are valid. Compared to software-based solutions, the appliance promises speed: backups are completed within sub seconds. But there is one major difference between Oracle’s appliance-based backup and generic software solutions: the Oracle appliance only supports the Oracle database; that makes it a tight fit for all-Oracle installations, but not for mixed database environments.
The new appliance fills an important gap in Oracle’s strategy for high manageability in its engineered systems by extending the umbrella to the often-overlooked processes of backup and restore.
About Luxon:
At Luxon, we believe that data is emerging as the world's newest resource for competitive advantage, and analytics is the key to make sense of it. To make Enterprise Analytics work effective, organizations need best in class hardware to run its business applications. Luxon is a Universal In-Memory Analytics engine, designed and developed by technology experts to serve the needs of global organizations in small and medium sector (or) SME’s. Luxon aims to cut down the costs for running your organizations analytics by 50 percent compared to existing analytics engines like HANA, Exalytics. Luxon supports all the traditional databases including latest innovations like SAP HANA, Oracle 12C. Luxon runs your reports 100X faster compared to your traditional engines. Luxon is built on reliable and proven best in class Hardware system architecture. Follow Luxon Analytics on Twitter @LuxonIn.
For more information about Luxon In-Memory Engine visit www.LuxonInMemory.com