Summary
Data is only valuable if you use it for something, and the first step is knowing that it is available. As organizations grow and data sources proliferate it becomes difficult to keep track of everything, particularly for analysts and data scientists who are not involved with the collection and management of that information. Lyft has build the Amundsen platform to address the problem of data discovery and in this episode Tao Feng and Mark Grover explain how it works, why they built it, and how it has impacted the workflow of data professionals in their organization. If you are struggling to realize the value of your information because you don’t know what you have or where it is then give this a listen and then try out Amundsen for yourself.
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- Finding the data that you need is tricky, and Amundsen will help you solve that problem. And as your data grows in volume and complexity, there are foundational principles that you can follow to keep data workflows streamlined. Mode – the advanced analytics platform that Lyft trusts – has compiled 3 reasons to rethink data discovery. Read them at dataengineeringpodcast.com/mode-lyft.
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- Your host is Tobias Macey and today I’m interviewing Mark Grover and Tao Feng about Amundsen, the data discovery platform and metadata engine that powers self service data access at Lyft
Interview
- Introduction
- How did you get involved in the area of data management?
- Can you start by explaining what Amundsen is and the problems that it was designed to address?
- What was lacking in the existing projects at the time that led you to building a new platform from the ground up?
- How does Amundsen fit in the larger ecosystem of data tools?
- How does it compare to what WeWork is building with Marquez?
- Can you describe the overall architecture of Amundsen and how it has evolved since you began working on it?
- What were the main assumptions that you had going into this project and how have they been challenged or updated in the process of building and using it?
- What has been the impact of Amundsen on the workflows of data teams at Lyft?
- Can you talk through an example workflow for someone using Amundsen?
- Once a dataset has been located, how does Amundsen simplify the process of accessing that data for analysis or further processing?
- How does the information in Amundsen get populated and what is the process for keeping it up to date?
- What was your motivation for releasing it as open source and how much effort was involved in cleaning up the code for the public?
- What are some of the capabilities that you have intentionally decided not to implement yet?
- For someone who wants to run their own instance of Amundsen what is involved in getting it deployed and integrated?
- What have you found to be the most challenging aspects of building, using and maintaining Amundsen?
- What do you have planned for the future of Amundsen?
Contact Info
Parting Question
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
Links
- Amundsen
- Lyft
- Airflow
- Slack
- Marquez
- S3
- Hive
- Presto
- Spark
- PostgreSQL
- Google BigQuery
- Neo4J
- Apache Atlas
- Tableau
- Superset
- Alation
- Cloudera Navigator
- DynamoDB
- MongoDB
- Druid
The intro and outro music is from The Hug by The Freak Fandango Orchestra / CC BY-SA