17:45 - 18:00 - Pizza outside D2 Room
18:10 - 18:25 - Overview of Hadoop Research at KTH (Prof. Seif Haridi)
18:25 - 19:00 - Hops: Hadoop Open Platform-as-a-Service (http://www.hops.io/) (Dr. Jim Dowling)
19:05 - 19:20 - Break
19:20 - 20:00 - Apache Flink Streaming (https://flink.apache.org/) (Paris Carbone)
About the Topics
Hadoop Open Platform-as-a-Service (Hops) is a new distribution of Apache Hadoop with scalable, highly available, customizable metadata. Hops is easy to install. It supports AWS, Vagrant, Bare-Metal, and other cloud providers.
Apache Flink Streaming is an extension of the batch Flink API for high-throughput, low-latency data stream processing. The system can connect to and process data streams from many data sources like Apache Kafka, RabbitMQ, Apache Flume, Twitter and also from any user defined data source. Data streams can be transformed and modified using high-level functions similar to the ones provided by the batch processing API. Flink Streaming provides native support for iterative stream processing. The processed data can be pushed to different output types
About the Speakers
Professor Seif Haridi is scientific leader at SICS and Professor at KTH. He is also one of the founders of Peerialism (http://www.peerialism.se/), a hot young startup which has drawn some attention since it started in 2007.
Dr. Jim Dowling is an Associate Professor (docent) at KTH and a senior researcher at SICS Swedish ICT. He is a researcher in the area of distributed systems, where his main interests are in the areas of Big Data and large-scale decentralized computer systems. He is the lead architect of the Hadoop-Open-Platform-as-a-Service, a new distribution of Apache Hadoop with scalable, highly available, customizable metadata. He also teaches courses in large-scale distributed systems and operating systems at KTH.
Paris Carbone is a PhD student in distributed computing at KTH and a committer for Apache Flink.. His research is focused on abstractions, domain specific languages and architectures towards scalable, expressive and cost-effective distributed data stream processing. In more detail, he is working on windowing semantics for dataflow systems, low-cost runtime optimisations across streaming applications, the unification of batch and streaming paradigms, high availability and data analytics task pipelining.
** NOTE: THIS MEETUP IS AT KTH (downtown), NOT AT SPOTIFY. SEE "HOW TO FIND US" FOR MORE DETAILS **