Jean-Yves Stephan

CEO and Co-Founder, Data Mechanics

JY is the CEO and Co-Founder of Data Mechanics, a hassle-free containerized data platform that abstracts away the complexities of Spark and infrastructure management. Prior to that, he was a software engineer and Spark infrastructure team lead at Databricks, growing their cluster-management capabilities from early days to the scale of launching hundreds of thousands of nodes in the cloud every day. JY is passionate about making distributed data technologies 10x more accessible and resource-efficient through automation.

UPCOMING SESSIONS

PAST SESSIONS

Running Apache Spark on Kubernetes: Best Practices and PitfallsSummit 2020

Since initial support was added in Apache Spark 2.3, running Spark on Kubernetes has been growing in popularity. Reasons include the improved isolation and resource sharing of concurrent Spark applications on Kubernetes, as well as the benefit to use an homogeneous and cloud native infrastructure for the entire tech stack of a company. But running Spark on Kubernetes in a stable, performant, cost-efficient and secure manner also presents specific challenges. In this talk, JY and Julien will go over lessons learned while building Data Mechanics, a serverless Spark platform powered by Kubernetes.

Topics include:
  • Core concepts and setup of Spark on Kubernetes
  • Configuration tips for performance and efficient resource sharing
  • Spark-app level dynamic allocation and cluster level autoscaling
  • Specificities of Kubernetes for data I/O performance
  • Monitoring and security best practices
  • Limitations and planned future works

How to Automate Performance Tuning for Apache SparkSummit Europe 2019

Spark has made writing big data pipelines much easier than before. But a lot of effort is required to maintain performant and stable data pipelines in production over time. Did I choose the right type of infrastructure for my application? Did I set the Spark configurations correctly? Can my application keep running smoothly as the volume of ingested data grows over time? How to make sure that my pipeline always finishes on time and meets its SLA?

These questions are not easy to answer even for a handful of jobs, and this maintenance work can become a real burden as you scale to dozens, hundreds, or thousands of jobs. This talk will review what we found to be the most useful piece of information and parameters to look at for manual tuning, and the different options available to engineers who want to automate this work, from open-source tools to managed services provided by the data platform or third parties like the Data Mechanics platform.