In order to scale the use of data across an organization there are a number of challenges related to discovery, governance, and integration that need to be solved. The key to those solutions is a robust and flexible metadata management system. LinkedIn has gone through several iterations on the most maintainable and scalable approach to metadata, leading them to their current work on DataHub. In this episode Mars Lan and Pardhu Gunnam explain how they designed the platform, how it integrates into their data platforms, and how it is being used to power data discovery and analytics at LinkedIn.
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- 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 Pardhu Gunnam and Mars Lan about DataHub, LinkedIn’s metadata management and data catalog platform
- How did you get involved in the area of data management?
- Can you start by giving an overview of what DataHub is and some of its back story?
- What were you using at LinkedIn for metadata management prior to the introduction of DataHub?
- What was lacking in the previous solutions that motivated you to create a new platform?
- There are a large number of other systems available for building data catalogs and tracking metadata, both open source and proprietary. What are the features of DataHub that would lead someone to use it in place of the other options?
- Who is the target audience for DataHub?
- How do the needs of those end users influence or constrain your approach to the design and interfaces provided by DataHub?
- Can you describe how DataHub is architected?
- How has it evolved since you first began working on it?
- What was your motivation for releasing DataHub as an open source project?
- What have been the benefits of that decision?
- What are the challenges that you face in maintaining changes between the public repository and your internally deployed instance?
- What is the workflow for populating metadata into DataHub?
- What are the challenges that you see in managing the format of metadata and establishing consistent models for the information being stored?
- How do you handle discovery of data assets for users of DataHub?
- What are the integration and extension points of the platform?
- What is involved in deploying and maintaining and instance of the DataHub platform?
- What are some of the most interesting or unexpected ways that you have seen DataHub used inside or outside of LinkedIn?
- What are some of the most interesting, unexpected, or challenging lessons that you learned while building and working with DataHub?
- When is DataHub the wrong choice?
- What do you have planned for the future of the project?
- From your perspective, what is the biggest gap in the tooling or technology for data management today?
- 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.
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- Apache Flume
- LinkedIn Blog Post introducing DataHub
- Hive Metastore
- CDC == Change Data Capture
- PDL LinkedIn language
- Apache Pinot
- Apache Gobblin
- Apache Samza
- Open Sourcing DataHub Blog Post