InterSystems Caché is a multi-model DBMS and application server. See more details here.
Introduction
Impedance mismatch is a term commonly used to describe the problem of an object-oriented (OO) application housing its data in legacy relational databases (RDBMS). C++ programmers have dealt with it for years, and it is now a familiar problem to Java and other OO programmers.
InterSystems Caché 2015.1 soars from 6 million to more than 21 million end-user database accesses per second on the Intel® Xeon® processor E7 v2 family compared to Caché 2013.1 on the Intel® Xeon® processor E5 family
Overview
Providing a reliable infrastructure for rapid, unattended, automated failover
Technology Overview
Traditional availability and replication solutions often require substantial capital investments in infrastructure, deployment, configuration, software licensing, and planning. Caché Database Mirroring (Mirroring) is designed to provide an economical solution for rapid, reliable, robust, automatic failover between two Caché systems, making mirroring the ideal automatic failover high-availability solution for the enterprise.
Abstract
A global provider of mobile telecommunications software tested the performance of InterSystems Caché and Oracle as the database in a simulated data mart application. They found Caché to be 41% faster than Oracle at building a data mart. When testing the response time to SQL queries of the data mart, Caché's performance ranged from 1.8 times to 513 times faster than Oracle.
Introduction
Introduction
With the maturation and wide acceptance of Java, object-oriented programming has moved to the foreground of the application development landscape. Because of their rich data models and support for productivity-enhancing concepts such as encapsulation, inheritance, and polymorphism, object technologies like Java, C++, and COM, are favored by today's application developers.
Introduction
Experts estimate that 85% of all data exists in unstructured formats – held in e-mails, documents (contracts, memos, clinical notes, legal briefs), social media feeds, etc. Where structured data typically accounts for quantitative facts, the more interesting and potentially more valuable expert opinions and conclusions are often hidden in these unstructured formats. And with massive volumes of text being generated at unprecedented speed, there’s very little chance this information can be made useful without some process of synthesis or automation.