For machine learning, Hadoop offers new performance capabilities, but not the intrusion of Hadoop's accompanying tradeoffs--performance, resource consumption, and data management. Machine learning users should consider Hadoop as a portion of a solution, but not the end-all. Alternatives such as dedicated servers, in-database deployment, and memory-based alternatives like Apache Spark can be combined with Hadoop to address a far broader array of opportunities. Fortunately for Revolution R users, Revolution R Enterprise (RRE) enables analytical scripts and models built in RRE to port between platforms with relative ease. In this session, we'll review the considerations for R developers, including performance, resource management, and data handling for deployment on Hadoop, individual servers, clusters and grids, in-database, and in-memory, including Apache Spark. We'll also dive briefly into the internals RRE on Hadoop to deepen awareness of some of the tradeoffs.
DAX is not only an expression language, but also a query language and, when it comes to performance, the xVelocity in-memory engine is second to none. Scanning fact tables and performing leaf-level computation happens in a matter of milliseconds. Nevertheless, as with any other language, you can write good DAX or bad DAX, depending on your understanding of the engine internals. This session introduces DAX as a query language, showing the different ways of querying with DAX using real-world data. Some queries will be fast, others will need optimizations. Many practical examples based on common patterns and an analysis of the query plans will show how to get the best out of DAX.
When designing cloud applications, you should take into account the fact that a failure or malfunction of any component of the system is possible. This template is called Designing for Failure. This design approach helps minimize the negative consequences of failure of any component of the internal or external system. Sometimes, it is not so simple to use the Designing for Failure template, but the Azure platform greatly facilitates this task due to the fact that some of the functions are implemented by cloud services—Azure Web Sites, Traffic Manager, CDN, RA—Geo Redundant Storage. During this session, we shall talk about how to use the functionality provided by Azure to ensure the accessibility of a website, even in the event of failure of one of the regions of the platform.