Some problems in data are well defined and benefit from a ready-made set of tools. For everything else, there’s Pachyderm, the platform for data science that is built to scale. In this episode Joe Doliner, CEO and co-founder, explains how Pachyderm started as an attempt to make data provenance easier to track, how the platform is architected and used today, and examples of how the underlying principles manifest in the workflows of data engineers and data scientists as they collaborate on data projects. In addition to all of that he also shares his thoughts on their recent round of fund-raising and where the future will take them. If you are looking for a set of tools for building your data science workflows then Pachyderm is a solid choice, featuring data versioning, first class tracking of data lineage, and language agnostic data pipelines.
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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!
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- Your host is Tobias Macey and today I’m interviewing Joe Doliner about Pachyderm, a platform that lets you deploy and manage multi-stage, language-agnostic data pipelines while maintaining complete reproducibility and provenance
- How did you get involved in the area of data management?
- Can you start by explaining what Pachyderm is and how it got started?
- What is new in the last two years since I talked to Dan Whitenack in episode 1?
- How have the changes and additional features in Kubernetes impacted your work on Pachyderm?
- A recent development in the Kubernetes space is the Kubeflow project. How do its capabilities compare with or complement what you are doing in Pachyderm?
- Can you walk through the overall workflow for someone building an analysis pipeline in Pachyderm?
- How does that break down across different roles and responsibilities (e.g. data scientist vs data engineer)?
- There are a lot of concepts and moving parts in Pachyderm, from getting a Kubernetes cluster set up, to understanding the file system and processing pipeline, to understanding best practices. What are some of the common challenges or points of confusion that new users encounter?
- Data provenance is critical for understanding the end results of an analysis or ML model. Can you explain how the tracking in Pachyderm is implemented?
- What is the interface for exposing and exploring that provenance data?
- What are some of the advanced capabilities of Pachyderm that you would like to call out?
- With your recent round of fundraising I’m assuming there is new pressure to grow and scale your product and business. How are you approaching that and what are some of the challenges you are facing?
- What have been some of the most challenging/useful/unexpected lessons that you have learned in the process of building, maintaining, and growing the Pachyderm project and company?
- What do you have planned for the future of Pachyderm?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- Data Provenance
- Stateful Sets
- CI == Continuous Integration
- CD == Continuous Delivery
- Object Storage
- FUSE == File System In User Space