Normally simple tasks like running a program or storing and retrieving data become much more complicated when we start to do them on collections of computers, rather than single machines. Distributed systems has become a key architectural concern, and affects everything a program would normally do—giving us enormous power, but at the cost of increased complexity as well.
Using a series of examples all set in a coffee shop, we’ll explore topics like distributed storage, computation, timing, messaging, and consensus. You'll leave with a good grasp of each of these problems, and a solid understanding of the ecosystem of open-source tools in the space.
So you’re a JVM developer, you understand Cassandra’s architecture, and you’re on your way to knowing its data model well enough to build descriptive data models that perform well. What you need now is to know the Java Driver.
What seems like an inconsequential library that proxies your application’s queries to your Cassandra cluster is actually a sophisticated piece of code that solves a lot of problems for you that early Cassandra developers had to code by hand. Come to this session to see features you might be missing and examples of how to use the Java driver in real applications.
Apache Cassandra is a leading open-source distributed database capable of amazing feats of scale, but its data model requires a bit of planning for it to perform well. Of course, the nature of ad-hoc data exploration and analysis requires that we be able to ask questions we hadn’t planned on asking—and get an answer fast. Enter Apache Spark.
Spark is a distributed computation framework optimized to work in-memory, and heavily influenced by concepts from functional programming languages. In this workshop, we’ll explore Spark and see how it works together with the Cassandra database to deliver a powerful open-source big data analytic solution.
Apache Cassandra is a leading open-source distributed database capable of amazing feats of scale, but its data model requires a bit of planning for it to perform well. Of course, the nature of ad-hoc data exploration and analysis requires that we be able to ask questions we hadn’t planned on asking—and get an answer fast. Enter Apache Spark.
Spark is a distributed computation framework optimized to work in-memory, and heavily influenced by concepts from functional programming languages. In this workshop, we’ll explore Spark and see how it works together with the Cassandra database to deliver a powerful open-source big data analytic solution.
Build and test software written in Java and many other languages with Gradle, the open source project automation tool that’s getting a lot of attention. This concise introduction provides numerous code examples to help you explore Gradle, both as a build tool and as a complete solution for automating the compilation, test, and release process of simple and enterprise-level applications.
Discover how Gradle improves on the best ideas of Ant, Maven, and other build tools, with standards for developers who want them and lots of flexibility for those who prefer less structure.