• 19:00 Welcome, Networking
• 19:25 Intro
• 19:30 Elasticsearch to support vehicles and mobile resources tracking software by Nicolas Zieglé & Valentin Fournier
• 20:00 ELK-MS - ElasticSearch/Logstash/Kibana - Mesos/Spark - a lightweight and efficient alternative to the Hadoop stack by Jérôme Kehrli
• 20:30 Networking, Beer, Snacks
Elasticsearch to support vehicles and mobile resources tracking software by Nicolas Zieglé & Valentin Fournier
Logifleet helps companies managing, planning and optimizing their mobile resources (workers, cars, trucks, assets, containers, fridge-trucks, etc...) by offering a real-time SaaS solution for thousands of concurrent users.
There are many use cases: "What did my vehicle do between Monday and Tuesday?", "Is my fridge-truck always in the EU-norm temperature?", "Which worker is the closest to an intervention point?", "How much time do I have to bill for an intervention?", etc...
Using Elasticsearch as a sole database coupled with percolators, Logstash, and a few lines of code, we will present how Logifleet created a geofencing and alert solution for thousands of connected resources sending millions of data every day!
ELK-MS - ElasticSearch/Logstash/Kibana - Mesos/Spark - a lightweight and efficient alternative to the Hadoop stack by Jérôme Kehrli
In my current company, we implement heavy Data Analytics algorithms and use cases for our customers. Historically, these heavy computations were taking a whole lot of different forms, mostly custom computation scripts in python or else using RDBMS databases to store data and results. A few years ago, we started to hit the limits of what we were able to achieve using traditional architectures and had to move both our storage and processing layers to NoSQL / Big Data technologies.
We considered a whole lot of different approaches, but eventually, and contrary to what I expected first, we didn't settle for a standard Hadoop stack. We are using Elasticsearch as key storage backend and Apache Spark as processing backend. Now of course we were initially still considering a Hadoop stack for the single purpose of using YARN as resource management layer for Spark ... until we discovered Apache Mesos.
Today this state of the art ELK-MS stack - for Elasticsearch/Logstash/Kibana - Mesos/Spark performs amazingly and I believe it to be a really lightweight, efficient, low latency and performing alternative to a plain old Hadoop Stack. Again, I am not saying that Cloudera and HortonWorks' Hadoops distributions are not good. Au contraire, they are awesome and really simplifies the overwhelming burden of configuring and maintaining the set of software components they provide. But there is definitely room for something lighter and simpler in terms of deployment and complexity.