Real-time data processing
Many use cases across various domains require real-time data processing for faster decision making: credit card fraud analytics, network fault prediction from sensor data, security threat prediction, etc. Data Stream Processing or stream computing is the new computing paradigm for processing streaming data in real-time without storing them in secondary storage. Twitter’s Storm project is a distributed real-time computation system, designed to be scalable, fault tolerant and programming language agnostic. Although it’s at an early stage (currently in incubation at The Apache Software Foundation), Storm is used by well-known companies with significant volumes of streaming data, such as The Weather Channel, Spotify, Twitter, and Rocket Fuel.
Workshop Date: Wednesday May 14th from 10:00 to 14:00, room ‘TV 401’
The ICCLab is pleased to invite you to the upcoming Workshop on Scientific Computing in the ICCLab Cloud. This workshop will focus on how to leverage the ICCLab Cloud infrastructures for executing scientific applications in a distributed, high performance environment.
The workshop’s agenda will include several talks describing applications from different areas of science (physics, mathematics, machine learning, etc.), highlighting their requirements from the ICT perspective. The workshop will also include a comprehensive overview of Hadoop and a tutorial on how to deploy, configure and use a Hadoop cluster on the ICCLab Cloud through the Savanna OpenStack project.
The workshop date and the full program are to be announced.
To register send an email to Diana Moise <firstname.lastname@example.org>
We look forward to your attendance.
Diana Moise is a researcher in ZHAW InIT Cloud Computing Lab.
Diana received her PhD degree in Computer Science from École Normale Supérieure de Cachan, France. In the past, she worked as a research engineer at INRIA Rennes – Bretagne Atlantique research center. The focus of her PhD was on optimizations of MapReduce applications on large-scale distributed infrastructures (including storage optimization, application and platform-aware optimizations). Her research interests include distributed computing, cloud computing, large-scale distributed data management, MapReduce paradigm, Hadoop.