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.
Segment provides the reliable data infrastructure companies need to easily collect, clean, and control their customer data. Once you try it, you’ll understand why Segment is one of the hottest companies coming out of Silicon Valley. Segment recently launched a Startup Program so that early-stage startups can get a Segment account totally free up to $25k, plus exclusive deals from some favorite vendors and other resources to become data experts. Go to dataengineeringpodcast.com/segmentio today and see if you or a startup you know qualify for the program today.
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 $60 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 200Gbit private networking, scalable shared block storage, and a 40Gbit public network, you’ve got everything you need to run a fast, reliable, and bullet-proof data platform. If you need global distribution, they’ve got that covered too with world-wide datacenters including new ones in Toronto and Mumbai. And for your machine learning workloads, they just announced dedicated CPU instances. Go to dataengineeringpodcast.com/linode today to get a $20 credit and launch a new server in under a minute. And don’t forget to thank them for their continued support of this show!
- Alluxio is an open source, distributed data orchestration layer that makes it easier to scale your compute and your storage independently. By transparently pulling data from underlying silos, Alluxio unlocks the value of your data and allows for modern computation-intensive workloads to become truly elastic and flexible for the cloud. With Alluxio, companies like Barclays, JD.com, Tencent, and Two Sigma can manage data efficiently, accelerate business analytics, and ease the adoption of any cloud. Go to dataengineeringpodcast.com/alluxio today to learn more and thank them for their support.
- Understanding how your customers are using your product is critical for businesses of any size. To make it easier for startups to focus on delivering useful features Segment offers a flexible and reliable data infrastructure for your customer analytics and custom events. You only need to maintain one integration to instrument your code and get a future-proof way to send data to over 250 services with the flip of a switch. Not only does it free up your engineers’ time, it lets your business users decide what data they want where. Go to dataengineeringpodcast.com/segmentio today to sign up for their startup plan and get $25,000 in Segment credits and $1 million in free software from marketing and analytics companies like AWS, Google, and Intercom. On top of that you’ll get access to Analytics Academy for the educational resources you need to become an expert in data analytics for measuring product-market fit.
- You listen to this show to learn and stay up to date with what’s happening in databases, streaming platforms, big data, and everything else you need to know about modern data management. For even more opportunities to meet, listen, and learn from your peers you don’t want to miss out on this year’s conference season. We have partnered with organizations such as O’Reilly Media, Dataversity, and the Open Data Science Conference. Go to dataengineeringpodcast.com/conferences to learn more and take advantage of our partner discounts when you register.
- Go to dataengineeringpodcast.com to subscribe to the show, sign up for the mailing list, read the show notes, and get in touch.
- To help other people find the show please leave a review on iTunes and tell your friends and co-workers
- Join the community in the new Zulip chat workspace at dataengineeringpodcast.com/chat
- 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