Evolving And Scaling The Data Platform at Yotpo
Data Engineering Podcast
00:00
Data Breaks - The Biggest Influence in the Architecture
We run our own spark clusters, like, from very, very, very early on. But right now we're actually thinking about stopping all that and just using data bricks. We've been with data bricks for many years now, and it's just working well. I think depet is going to be a really going to change the way we do data modelling. And also delta like format, which is, after our experience with hoodi, we don't want to stop doing that at this point in time. At the same time, by the way, test source version of Delta was not mature enough when we first used it. So i think probably, working in data lake and spark base
Play episode from 37:16
Transcript
Transcript
Episode notes
Summary
Building a data platform is an iterative and evolutionary process that requires collaboration with internal stakeholders to ensure that their needs are being met. Yotpo has been on a journey to evolve and scale their data platform to continue serving the needs of their organization as it increases the scale and sophistication of data usage. In this episode Doron Porat and Liran Yogev explain how they arrived at their current architecture, the capabilities that they are optimizing for, and the complex process of identifying and evaluating new components to integrate into their systems. This is an excellent exploration of the decisions and tradeoffs that need to be made while building such a complex system.
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!
- This episode is brought to you by Acryl Data, the company behind DataHub, the leading developer-friendly data catalog for the modern data stack. Open Source DataHub is running in production at several companies like Peloton, Optum, Udemy, Zynga and others. Acryl Data provides DataHub as an easy to consume SaaS product which has been adopted by several companies. Signup for the SaaS product at dataengineeringpodcast.com/acryl
- RudderStack helps you build a customer data platform on your warehouse or data lake. Instead of trapping data in a black box, they enable you to easily collect customer data from the entire stack and build an identity graph on your warehouse, giving you full visibility and control. Their SDKs make event streaming from any app or website easy, and their state-of-the-art reverse ETL pipelines enable you to send enriched data to any cloud tool. Sign up free… or just get the free t-shirt for being a listener of the Data Engineering Podcast at dataengineeringpodcast.com/rudder.
- The most important piece of any data project is the data itself, which is why it is critical that your data source is high quality. PostHog is your all-in-one product analytics suite including product analysis, user funnels, feature flags, experimentation, and it’s open source so you can host it yourself or let them do it for you! You have full control over your data and their plugin system lets you integrate with all of your other data tools, including data warehouses and SaaS platforms. Give it a try today with their generous free tier at dataengineeringpodcast.com/posthog
- Your host is Tobias Macey and today I’m interviewing Doron Porat and Liran Yogev about their experiences designing and implementing a self-serve data platform at Yotpo
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you describe what Yotpo is and the role that data plays in the organization?
- What are the core data types and sources that you are working with?
- What kinds of data assets are being produced and how do those get consumed and re-integrated into the business?
- What are the user personas that you are supporting and what are the interfaces that they are comfortable interacting with?
- What is the size of your team and how is it structured?
- You recently posted about the current architecture of your data platform. What was the starting point on your platform journey?
- What did the early stages of feature and platform evolution look like?
- What was the catalyst for making a concerted effort to integrate your systems into a cohesive platform?
- What was the scope and directive of the project for building a platform?
- What are the metrics and capabilities that you are optimizing for in the structure of your data platform?
- What are the organizational or regulatory constraints that you needed to account for?
- What are some of the early decisions that affected your available choices in later stages of the project?
- What does the current state of your architecture look like?
- How long did it take to get to where you are today?
- What were the factors that you considered in the various build vs. buy decisions?
- How did you manage cost modeling to understand the true savings on either side of that decision?
- If you were to start from scratch on a new data platform today what might you do differently?
- What are the decisions that proved helpful in the later stages of your platform development?
- What are the most interesting, innovative, or unexpected ways that you have seen your platform used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on designing and implementing your platform?
- What do you have planned for the future of your platform infrastructure?
Contact Info
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
Links
- Yotpo
- Greenplum
- Databricks
- Metorikku
- Apache Hive
- CDC == Change Data Capture
- Debezium
- Apache Hudi
- Upsolver
- Spark
- PrestoDB
- Snowflake
- Druid
- Rockset
- dbt
- Acryl
- Atlan
- OpenLineage
- Okera
- Shopify Data Warehouse Episode
- Redshift
- Delta Lake
- Iceberg
- Outbox Pattern
- Backstage
- Roadie
- Nomad
- Kubernetes
- Deequ
- Great Expectations
- LakeFS
- 2021 Recap Episode
- Monte Carlo
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
The AI-powered Podcast Player
Save insights by tapping your headphones, chat with episodes, discover the best highlights - and more!
