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6 artículos que hay que leer

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"Configuration library for JVM languages. Overview implemented in plain Java with no dependencies extensive test coverage supports files in three formats: Java properties, JSON, and a human-friendly JSON superset merges multiple files across all formats can load from files, URLs, or classpath good support for "nesting" (treat any subtree of the config the same as the whole config) users can override the config with Java system properties, java -Dmyapp.foo.bar=10 supports configuring an app, with its framework and libraries, all from a single file such as application.conf parses duration and size settings, "512k" or "10 seconds" converts types, so if you ask for a boolean and the value is the string "yes", or you ask for a float and the value is an int, it will figure it out. JSON superset features: comments includes substitutions ("foo" : ${bar}, "foo" : Hello ${who}) properties-like notation (a.b=c) less noisy, more lenient syntax substitute environment variables This library limits itself to config files. If you want to load config from a database or something, you would need to write some custom code. The library has nice support for merging configurations so if you build one from a custom source it's easy to merge it in."

"HPaste unlocks the rich functionality of HBase for a Scala audience. In so doing, it attempts to achieve the following goals: Provide a strong, clear syntax for querying and filtration Perform as fast as possible while maintaining idiomatic Scala client code -- the abstractions should not show up in a profiler! Re-articulate HBase's data structures rather than force it into an ORM-style atmosphere. A rich set of base classes for writing MapReduce jobs in hadoop against HBase tables. Provide a maximum amount of code re-use between general Hbase client usage, and operation from within a MapReduce job. Use Scala's type system to its advantage--the compiler should verify the integrity of the schema. Be a verbose DSL--minimize boilerplate code, but be human readable!"

"Internet Routing: It's a program (well, a daemon, as you are going to discover in a moment) which works as a dynamic router in an Internet type network (that is, in a network running either the IPv4 or the IPv6 protocol). Routers are devices which forward packets between interconnected networks in order to allow hosts not connected directly to the same local area network to communicate with each other. They also communicate with the other routers in the Internet to discover the topology of the network which allows them to find optimal (in terms of some metric) rules for forwarding of packets (which are called routing tables) and to adapt themselves to the changing conditions such as outages of network links, building of new connections and so on. Most of these routers are costly dedicated devices running obscure firmware which is hard to configure and not open to any changes (on the other hand, their special hardware design allows them to keep up with lots of high-speed network interfaces, better than general-purpose computer does). Fortunately, most operating systems of the UNIX family allow an ordinary computer to act as a router and forward packets belonging to the other hosts, but only according to a statically configured table."

"1. The Government has already made a commitment to Big Data by classifying it as one of the 'Eight Great Technologies' which will propel the UK to future growth and help it stay ahead in the global race. The 'Information Economy Strategy' reports on the increase in data being generated and the importance of new types of computing power in order to reap the economic value of the data. 2. This paper sets out to cover the following areas: a) Defining Big Data b) High-level trends in Big Data c) Opportunities for Big Data applications 3. In developing this paper, a 'community of interest' has been established comprising policy leads and analysts from across government with an interest in Big Data. This paper draws on their insights, insights from the private sector, academics, and the extensive open source literature on the Big Data topic."

Una explicación bastante concisa de varianza, covarianza y contravarianza en el sistema de tipado de scala.