Charting the Path of Riskified's Data Platform Journey
Data Engineering Podcast
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Data Braks and Snowflake Data Mining
Redshift, tamasand redshift used to be ething we need to do in big data. But then it became too expensive and not very cost performant. And since we initiate it, working with spar for the entire organization, only make sense that we have a platform that allows non engineers to touse spark for data access. This is why data braks is there. We encourage everyone data from the data lake to use data briaks for any kind of data exploration - building data pipe lines or whatever. It's not like awus, go to snowflake or go to data brecks. It depends on the data that you need."
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Summary
Building a data platform is a journey, not a destination. Beyond the work of assembling a set of technologies and building integrations across them, there is also the work of growing and organizing a team that can support and benefit from that platform. In this episode Inbar Yogev and Lior Winner share the journey that they and their teams at Riskified have been on for their data platform. They also discuss how they have established a guild system for training and supporting data professionals in the organization.
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 Inbar Yogev and Lior Winner about the data platform that the team at Riskified are building to power their fraud management service
Interview
- Introduction
- How did you get involved in the area of data management?
- What does Riskified do?
- Can you describe the role of data at Riskified?
- What are some of the core types and sources of information that you are dealing with?
- Who/what are the primary consumers of the data that you are responsible for?
- What are the team structures that you have tested for your data professionals?
- What is the composition of your data roles? (e.g. ML engineers, data engineers, data scientists, data product managers, etc.)
- What are the organizational constraints that have the biggest impact on the design and usage of your data systems?
- Can you describe the current architecture of your data platform?
- What are some of the most notable evolutions/redesigns that you have gone through?
- What is your process for establishing and evaluating selection criteria for any new technologies that you adopt?
- How do you facilitate knowledge sharing between data professionals?
- What have you found to be the most challenging technological and organizational complexities that you have had to address on the path to your current state?
- What are the methods that you use for staying up to date with the data ecosystem? (opportunity to discuss Haya Data conference)
- In your role as organizers of the Haya Data conference, what are some of the insights that you have gained into the present state and future trajectory of the data community?
- What are the most interesting, innovative, or unexpected ways that you have seen the Riskified data platform used?
- What are the most interesting, unexpected, or challenging lessons that you have learned while working on the data platform for Riskified?
- What do you have planned for the future of your data platform?
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 shows. Podcast.__init__ covers the Python language, its community, and the innovative ways it is being used. The Machine Learning Podcast helps you go from idea to production with machine learning.
- 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 Apple Podcasts and tell your friends and co-workers
Links
- Riskified
- ADABAS
- Aerospike
- Neo4J
- Kafka
- Delta Lake
- Databricks
- Snowflake
- Tableau
- Looker
- Redshift
- Event Sourcing
- Avro
- hayaData Conference
- Data Mesh
- Data Catalog
- Data Governance
- MLOps
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA
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