Evolving And Scaling The Data Platform at Yotpo
Data Engineering Podcast
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Airflow - I'm Not Offended Airflow.
Prefect is a software company based in London, UK. Prefect has been developing its own cloud-based data lake for the past year. The platform uses an auto generated air flow system to manage quarry performance and injestion speed. It was originally built using spark loaders that were pulled from operational data bases on a daily or hourly basis. But prefect decided they didn't work well enough so started over with dagster.
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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
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- 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
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