11:00 - 13:00 - November 12nd
Apache Spark is the de facto framework choice for big data processing, but learning it can be a little intimidating, due to its complexity. The main role of Spark SQL is to reduce this complexity and to allow you to run queries on big data with a minimum learning effort. All you need to know is to write SQL queries ! In this workshop you will have a short introduction to Apache Spark, its architecture, data structures and after that we will focus on Spark SQL
I'm passionate about Big Data/Machine Learning technologies and startups. I heard for the first time about Apache Hadoop when I was at my master courses and from that time I was fascinated about Big Data world.
16:30 - 18:30 - November 12nd
This workshop is an extension of the Understanding Stream Processing talk, a set of practical, hands-on examples of the discussed concepts, written in Java, by using Hazelcast Jet.
I have been a Java developer for 16 years. The first 10 were spent on various proprietary real-time market data systems in the financial industry. Later I met Peter Lawrey and got involved with ultra-low latency Java applications. It was amazing to see what's possible to achieve with Java applications, in particular high percentile latencies in the order of microsecond on commodity hardware. I'm currently working for Hazelcast, on their stream-processing product called Jet and I'm more and more involved with this interesting domain of processing global-scale, infinite datasets.
11:00 - 13:00 - November 13nd
As you maybe know, Kafka is a massively scalable streaming platform architected as a distributed transaction log, making it highly valuable for enterprise infrastructures to process streaming data. It aims to provide a unified, high-throughput, low-latency platform for handling real-time data feeds. Kafka is used in production by 60% of the top 500 fortune companies. You can think of people like Uber, Yelp, IBM, Netflix, Tesla and many others.
The goal of this workshop is to introduce you the main Kafka characteristics tried by practical examples. We are gonna play with twitter stream datasets, do SQL on Kafka and other streaming and connect examples.
Examples will be explained and written using Java language.
Passionate about emerging technologies, Big Data and the microservices world. I lead an awesome team that delivers great results by turning research work into products. Most of my work is technical and it involves design, architecture and implementation of high-end scalable solutions to challenging problems at the intersection of Artificial Intelligence and Software Engineering. I like working with the newest technologies and I adopted the microservices architecture since 2014 and started working with cool stuff like Kafka since its 0.7 version