Data Science

Introduction One of the biggest challenges for developers to build mobile applications is data synchronization. It's the foundation for many different types of mobile applications, but it's very complicated and very hard to implement. This can be even harder for enterprise developers, as often they have to make sure the data is not only synchronized to the server side of their mobile apps but also synchronized to the database backends of their enterprises, as demonstrated in this diagram: That's why...

Red Hat JBoss Data Virtualization (JDV) provides several capabilities for caching data including: materialized views, result set caching, and code table caching. These techniques can be used to significantly improve performance in many situations. With the exception of external materialized views, the cached data is accessed through the BufferManager. For better performance, the BufferManager setting should be adjusted to the memory constraints of your installation. See the Admin Guide for more on parameter tuning. JDV supports two kinds of caching...

We are happy to announce the availability of Red Hat JBoss Data Virtualization (JDV) 6.3 GA. JDV 6.3 release focuses on three areas: In-memory technologies for Big Data processing Development and deployment productivity Big Data and Cloud data sources The following new features and data source integration were added in support of these themes: In-memory technologies for Big Data processing Apache Spark - Apache Spark is an open source big data processing framework built around speed, ease of use, and...