Q. You’re speaking at Voxxed Days Bucharest in March. Tell us a bit about your session.
The session is about how to use Java 8 Stream API to process and analyse data stored in distributed Infinispan data grid instances. The talk will start with a short presentation about Infinispan and Java 8 Stream API and then it will follow with a demonstration on how to combine both to do distributed data processing.
Q. Why is the subject matter important?
The Java 8 Stream API is a very popular API to get started with processing data in a single node environment. Infinispan has extended the Java 8 Stream API so that lambda functions are shipped to where data is located, rather than bringing all data to a single node, and hence it can take full advantage of the parallel data processing capabilities of multiple nodes.
Q. Who should attend your session?
This is a beginner talk for Java developers and architects interested in data processing and distributed computing. Previous knowledge of Java 8 Streams and Map/Reduce is desirable but not mandatory.
Q. What are the key things attendees will take away from your session?
The key message is that the Java 8 Stream API can be extended to process large data sets in a distributed environment, taking advantage of the parallelism offered by multi-node environments. The attendees will also learn about the differences of Java 8 Stream API and other data processing APIs exposed by Infinispan.
Q. Aside from speaking at Voxxed Days Bucharest, what else are you excited about for 2016?
I’m very excited about enhancements we’ll be doing to a new Functional Map API we designed for Infinispan 8 which takes advantage of lambdas functions to define how data is modified and manipulated. Being able to distributed the functions that lead to a particular state as opposed to distributed latest values is a very powerful concept, enabling strong eventual consistent data structures such as conflict-free replicated data type (CRDT).
I’m also very excited with the work we’ve been doing at Infinispan to integrate with other data processing projects at Apache, such as Hadoop, Spark, etc., so you can use Infinispan as an alternative backend for the data. These integrations expose Infinispan’s in-memory data grid capabilities to a new audience expanding our user base.