Kevin Stumpf

Summary The Data industry is changing rapidly, and one of the most active areas of growth is automation of data workflows. Taking cues from the DevOps movement of the past decade data professionals are orienting around the concept of DataOps. More than just a collection of tools, there are a number of organizational and conceptual changes that a proper DataOps approach depends on. In this episode Kevin Stumpf, CTO of Tecton, Maxime Beauchemin, CEO of Preset, and Lior Gavish, CTO of Monte Carlo, discuss the grand vision and present realities of DataOps. They explain how to think about your data systems in a holistic and maintainable fashion, the security challenges that threaten to derail your efforts, and the power of using metadata as the foundation of everything that you do. If you are wondering how to get control of your data platforms and bring all of your stakeholders onto the same page then this conversation is for you. Announcements 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! Modern Data teams are dealing with a lot of complexity in their data pipelines and analytical code. Monitoring data quality, tracing incidents, and testing changes can be daunting and often takes hours to days. Datafold helps Data teams gain visibility and confidence in the quality of their analytical data through data profiling, column-level lineage and intelligent anomaly detection. Datafold also helps automate regression testing of ETL code with its Data Diff feature that instantly shows how a change in ETL or BI code affects the produced data, both on a statistical level and down to individual rows and values. Datafold integrates with all major data warehouses as well as frameworks such as Airflow & dbt and seamlessly plugs into CI workflows. Go to dataengineeringpodcast.com/datafold today to start a 30-day trial of Datafold. Once you sign up and create an alert in Datafold for your company data, they will send you a cool water flask. RudderStack’s smart customer data pipeline is warehouse-first. It builds your customer data warehouse and your identity graph on your data warehouse, with support for Snowflake, Google BigQuery, Amazon Redshift, and more. Their SDKs and plugins make event streaming easy, and their integrations with cloud applications like Salesforce and ZenDesk help you go beyond event streaming. With RudderStack you can use all of your customer data to answer more difficult questions and then send those insights to your whole customer data stack. Sign up free at dataengineeringpodcast.com/rudder today. Your host is Tobias Macey and today I’m interviewing Max Beauchemin, Lior Gavish, and Kevin Stumpf about the real world challenges of embracing DataOps practices and systems, and how to keep things secure as you scale Interview Introduction How did you get involved in the area of data management? Before we get started, can you each give your definition of what "DataOps" means to you? How does this differ from "business as usual" in the data industry? What are some of the things that DataOps isn’t (despite what marketers might say)? What are the biggest difficulties that you have faced in going from concept to production with a workflow or system intended to power self-serve access to other members of the organization? What are the weak points in the current state of the industry, whether technological or social, that contribute to your greatest sense of unease from a security perspective? As founders of companies that aim to facilitate adoption of various aspects of DataOps, how are you applying the products that you are building to your own internal systems? How does security factor into the design of robust DataOps systems? What are some of the biggest challenges related to security when it comes to putting these systems into production? What are the biggest differences between DevOps and DataOps, particularly when it concerns designing distributed systems? What areas of the DataOps landscape do you think are ripe for innovation? Nowadays, it seems like new DataOps companies are cropping up every day to try and solve some of these problems. Why do you think DataOps is becoming such an important component of the modern data stack? There’s been a lot of conversation recently around the "rise of the data engineer" versus other roles in the data ecosystem (i.e. data scientist or data analyst). Why do you think that is? What are some of the most valuable lessons that you have learned from working with your customers about how to apply DataOps principles? What are some of the most interesting, unexpected, or challenging lessons that you have learned while building your respective platforms and businesses? What are the industry trends that you are each keeping an eye on to inform you future product direction? Contact Info Kevin LinkedIn kevinstumpf on GitHub @kevinstumpf on Twitter Maxime LinkedIn @mistercrunch on Twitter mistercrunch on GitHub Lior LinkedIn @lgavish on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story. 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 Links Tecton Monte Carlo Superset Preset Barracuda Networks Feature Store DataOps DevOps Data Catalog Amundsen OpenLineage The Downfall of the Data Engineer Hashicorp Vault Reverse ELT The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Kevin Stumpf

Co-Founder and CTO

Tecton

Maxime Beauchemin

Founder & CEO

PresetApache Airflow / Apache Superset

Lior Gavish

Co-founder

Monte Carlo

Summary As more organizations are gaining experience with data management and incorporating analytics into their decision making, their next move is to adopt machine learning. In order to make those efforts sustainable, the core capability they need is for data scientists and analysts to be able to build and deploy features in a self service manner. As a result the feature store is becoming a required piece of the data platform. To fill that need Kevin Stumpf and the team at Tecton are building an enterprise feature store as a service. In this episode he explains how his experience building the Michelanagelo platform at Uber has informed the design and architecture of Tecton, how it integrates with your existing data systems, and the elements that are required for well engineered feature store. Announcements 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 $60 credit to try out a Kubernetes cluster of your own. And don’t forget to thank them for their continued support of this show! Do you want to get better at Python? Now is an excellent time to take an online course. Whether you’re just learning Python or you’re looking for deep dives on topics like APIs, memory mangement, async and await, and more, our friends at Talk Python Training have a top-notch course for you. If you’re just getting started, be sure to check out the Python for Absolute Beginners course. It’s like the first year of computer science that you never took compressed into 10 fun hours of Python coding and problem solving. Go to dataengineeringpodcast.com/talkpython today and get 10% off the course that will help you find your next level. That’s dataengineeringpodcast.com/talkpython, and don’t forget to thank them for supporting the show. You invest so much in your data infrastructure – you simply can’t afford to settle for unreliable data. Fortunately, there’s hope: in the same way that New Relic, DataDog, and other Application Performance Management solutions ensure reliable software and keep application downtime at bay, Monte Carlo solves the costly problem of broken data pipelines. Monte Carlo’s end-to-end Data Observability Platform monitors and alerts for data issues across your data warehouses, data lakes, ETL, and business intelligence. The platform uses machine learning to infer and learn your data, proactively identify data issues, assess its impact through lineage, and notify those who need to know before it impacts the business. By empowering data teams with end-to-end data reliability, Monte Carlo helps organizations save time, increase revenue, and restore trust in their data. Visit dataengineeringpodcast.com/montecarlo today to request a demo and see how Monte Carlo delivers data observability across your data infrastructure. The first 25 will receive a free, limited edition Monte Carlo hat! Your host is Tobias Macey and today I’m interviewing Kevin Stumpf about Tecton and the role that the feature store plays in a modern MLOps platform Interview Introduction How did you get involved in the area of data management? Can you start by describing what you are building at Tecton and your motivation for starting the business? For anyone who isn’t familiar with the concept, what is an example of a feature? How do you define what a feature store is? What role does a feature store play in the overall lifecycle of a machine learning project? How would you characterize the current landscape of feature stores? What are the other components that are necessary for a complete ML operations platform? At what points in the lifecycle of data does the feature store get integrated? What types of data can feature stores manage? (e.g. text vs. image/binary vs. spatial, etc.) How is the Tecton platform implemented? How has the design evolved since you first began building it? How did your work on Uber’s Michelangelo inform your work on Tecton? What is the workflow and lifecycle of developing, testing, and deploying a feature to a feature store? What aspects of a feature do you monitor to determine whether it has drifted? How do you define drift in the context of a feature? How does that differ from drift in an ML model? How does Tecton handle versioning of features and associating those different versions with the models that are using them? What are some of the most interesting, innovative, or unexpected projects that you have seen built with Tecton? When is Tecton the wrong choice? What do you have planned for the future of the product? Contact Info LinkedIn kevinstumpf on GitHub @kevinstumpf on Twitter Parting Question From your perspective, what is the biggest gap in the tooling or technology for data management today? Closing Announcements Thank you for listening! Don’t forget to check out our other show, Podcast.__init__ to learn about the Python language, its community, and the innovative ways it is being used. Visit the site to subscribe to the show, sign up for the mailing list, and read the show notes. If you’ve learned something or tried out a project from the show then tell us about it! Email hosts@dataengineeringpodcast.com) with your story. 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 Links Tecton Uber Michelangelo MLOps Feature Store Blog: What Is A Feature Store StreamSQL Podcast Episode AWS Feature Store Logical Clocks EMR Kotlin DynamoDB scikit-learn Tensorflow MLFlow Algorithmia SageMaker Feast open source feature store Jaeger OpenTelemetry The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA Support Data Engineering Podcast

Kevin Stumpf

Co-Founder and CTO

Tecton

In this special episode of the podcast, we're joined by Kevin Stumpf, Co-Founder and CTO of Tecton, Willem Pienaar, an engineering lead at Gojek and founder of the Feast Project, and Maxime Beauchemin, Founder & CEO of Preset, for a discussion on Feature Stores for Accelerating AI Development. In this panel discussion, Sam and our guests explored how organizations can increase value and decrease time-to-market for machine learning using feature stores, MLOps, and open source. We also discuss the main data challenges of AI/ML, and the role of the feature store in solving those challenges. The complete show notes for this episode can be found at twimlai.com/go/432.

Kevin Stumpf

Co-Founder and CTO

Tecton

Willem Pienaar

Engineering Lead and Founder of Feast

Gojek

Maxime Beauchemin

Founder & CEO

PresetApache Airflow / Apache Superset