Jean-Yves Stephan

CEO and Co-Founder, Data Mechanics

Jean-Yves is the Co-Founder & CEO of Data Mechanics, a cloud-native spark platform available on AWS, GCP, and Azure. Their mission is to make Spark more developer friendly and cost-effective for data engineering teams. They are active contributors to open-source projects such as the Spark-on-Kubernetes operator and Data Mechanics Delight.

Prior to Data Mechanics, Jean-Yves was a software engineer at Databricks, where he led the Spark infrastructure team.

Past sessions

Delight ( is a free & cross-platform monitoring dashboard for Apache Spark, which display system metrics (CPU Usage, Memory Usage) along with Spark information (jobs, stages, tasks) on the same timeline. Delight is a great complement to the Spark UI when it comes to troubleshooting your Spark application and understanding its performance bottleneck. It works freely on top of any Spark platform (whether it's open-source or commercial, in the cloud or on-premise). You can install it using an open-sourced Spark agent (


In this session, the co-founders of Data Mechanics will take you through performance troubleshooting sessions with Delight on real-world data engineering pipelines. You will see how Delight and the Spark UI can jointly help you spot the performance bottleneck of your applications, and how you can use these insights to make your applications more cost-effective and stable.

In this session watch:
Jean-Yves Stephan, CEO and Co-Founder, Data Mechanics
Julien Dumazert, CTO and Co-Founder, Data Mechanics


Summit Europe 2020 Getting Started with Apache Spark on Kubernetes

November 17, 2020 04:00 PM PT

Community adoption of Kubernetes (instead of YARN) as a scheduler for Apache Spark has been accelerating since the major improvements from Spark 3.0 release. Companies choose to run Spark on Kubernetes to use a single cloud-agnostic technology across their entire stack, and to benefit from improved isolation and resource sharing for concurrent workloads. In this talk, the founders of Data Mechanics, a serverless Spark platform powered by Kubernetes, will show how to easily get started with Spark on Kubernetes.

We will go through an end-to-end example of building, deploying and maintaining an end-to-end data pipeline. This will be a code-heavy session with many tips to help beginners and intermediate Spark developers be successful with Spark on Kubernetes, and live demos running on the Data Mechanics platform.

Included topics:
- Setting up your environment (data access, node pools)
- Sizing your applications (pod sizes, dynamic allocation)
- Boosting your performance through critical disk and I/O optimizations
- Monitoring your application logs and metrics for debugging and reporting

Speakers: Jean-Yves Stephan and Julien Dumazert

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
Summit Europe 2019 How to Automate Performance Tuning for Apache Spark

October 15, 2019 05:00 PM PT

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.