Spark is one of the most well-known frameworks for data processing, whether for batch or streaming, ETL or ML, and at any scale. Because of its popularity it has been deployed on every kind of platform you can think of. In this episode Jean-Yves Stephan shares the work that he is doing at Data Mechanics to make it sing on Kubernetes. He explains how operating in a cloud-native context simplifies some aspects of running the system while complicating others, how it simplifies the development and experimentation cycle, and how you can get a head start using their pre-built Spark container. This is a great conversation for understanding how new ways of operating systems can have broader impacts on how they are being used.
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Firebolt is the world’s fastest cloud data warehouse, purpose-built for high performance analytics. It provides orders of magnitude faster query performance at a fraction of the cost compared to alternatives. Companies that adopted Firebolt have been able to deploy data warehouses in weeks and deliver sub-second performance at terabyte to petabyte scale for a wide range of interactive, high performance analytics across internal BI as well as customer facing analytics use cases. Visit dataengineeringpodcast.com/firebolt to get started.
Your data platform needs to be scalable, fault tolerant, and performant, which means that you need the same from your cloud provider. Linode has been powering production systems for over 17 years, and now they’ve launched a fully managed Kubernetes platform. With the combined power of the Kubernetes engine for flexible and scalable deployments, and features like dedicated CPU instances, GPU instances, and object storage you’ve got everything you need to build a bulletproof data pipeline. If you go to dataengineeringpodcast.com/linode today you’ll even get a $100 credit to use on building your own cluster, or object storage, or reliable backups, or… And while you’re there don’t forget to thank them for being a long-time supporter of the Data Engineering Podcast!
- Hello and welcome to the Data Engineering Podcast, the show about modern data management
- When you’re ready to build your next pipeline, or want to test out the projects you hear about on the show, you’ll need somewhere to deploy it, so check out our friends at Linode. With their managed Kubernetes platform it’s now even easier to deploy and scale your workflows, or try out the latest Helm charts from tools like Pulsar and Pachyderm. With simple pricing, fast networking, object storage, and worldwide data centers, you’ve got everything you need to run a bulletproof data platform. Go to dataengineeringpodcast.com/linode today and get a $100 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show!
- Firebolt is the fastest cloud data warehouse. Visit dataengineeringpodcast.com/firebolt to get started. The first 25 visitors will receive a Firebolt t-shirt.
- Atlan is a collaborative workspace for data-driven teams, like Github for engineering or Figma for design teams. By acting as a virtual hub for data assets ranging from tables and dashboards to SQL snippets & code, Atlan enables teams to create a single source of truth for all their data assets, and collaborate across the modern data stack through deep integrations with tools like Snowflake, Slack, Looker and more. Go to dataengineeringpodcast.com/atlan today and sign up for a free trial. If you’re a data engineering podcast listener, you get credits worth $3000 on an annual subscription
- Your host is Tobias Macey and today I’m interviewing Jean-Yves Stephan about Data Mechanics, a cloud-native Spark platform for data engineers
- How did you get involved in the area of data management?
- Can you start by giving an overview of what you are building at Data Mechanics and the story behind it?
- What are the operational characteristics of Spark that make it difficult to run in a cloud-optimized environment?
- How do you handle retries, state redistribution, etc. when instances get pre-empted during the middle of a job execution?
- What are some of the tactics that you have found useful when designing jobs to make them more resilient to interruptions?
- What are the customizations that you have had to make to Spark itself?
- What are some of the supporting tools that you have built to allow for running Spark in a Kubernetes environment?
- How is the Data Mechanics platform implemented?
- How have the goals and design of the platform changed or evolved since you first began working on it?
- How does running Spark in a container/Kubernetes environment change the ways that you and your customers think about how and where to use it?
- How does it impact the development workflow for data engineers and data scientists?
- What are some of the most interesting, unexpected, or challenging lessons that you have learned while building the Data Mechanics product?
- When is Spark/Data Mechanics the wrong choice?
- What do you have planned for the future of the platform?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Data Mechanics
- Andrew Ng
- Mining Massive Datasets
- Spot Instances
- Data Mechanics Spark Container Image
- Delight – Spark monitoring utility
- Blue/Green Deployment
- Spark Operator for Kubernetes
- Jupyter Enterprise Gateway