IEEE Big Data 2018: a summary
At the beginning of the month, our R&D director Sabri Skirhi and our R&D engineer Syrine Ferjaoui travelled to Seattle to attend IEEE Big Data. The conference is one of
Category: Engineering
Spark+AI Summit: a summary
A few weeks ago, Sabri Skhiri and Florian Demesmaeker were in London to attend the Spark+AI summit. They came back with a lot to say about the new features of
Flink Forward 2018: What You Want to Know and What You (Will) Need to Know.
Early September 2018, 8 EURA NOVA engineers travelled to Berlin to attend the Flink Forward Conference, dedicated to Apache Flink users and stream processing communities. They came back with a
The Next Activities of our R&D Centre in Marseille
The French branch of EURA NOVA will take part in two great tech events in the following days and weeks. On the 22nd of February, data scientist Thomas Peel
Second Workshop on Real-Time and Stream Analytics in Big Data
EURA NOVA is thrilled to share the news with you: we are organizing our second workshop collocated with the 2017 IEEE International Conference on Big Data. The workshop will take
EURA NOVA R&D has a new rallying cry : Join The Pack!
After launching our first bootcamp, we are organising our first workshop colocated with IEEE conference. The workshop will take place in December in Washington D.C. and will bring together industrial and
My internship at EURA NOVA
Renaud Vilmart (Mines de Nancy) did an Engineering Internship at EURA NOVA from June to September. In the article, Renaud describes his experience as an intern.
Flink Forward 2015 – Slides & video
The first edition of Flink Forward took place past October 12th and 13th in Berlin. Flink Forward is two-day conference exclusively dedicated to Apache Flink, the distributed pipelined batch and
IEEE Big Data 2015
This year we had the opportunity to publish a paper, DISTRIBUTED FRANK-WOLFE UNDER PIPELINED STALE SYNCHRONOUS PARALLELISM, at the IEEE Big Data conference at Santa Clara, CA. This was an
Distributed Frank-Wolfe under pipelined stale synchronous parallelism
Iterative-convergent algorithms represent an im-portant family of applications in big data analytics. These aretypically run on distributed processing frameworks deployed on a cluster of machines. On the other hand, we