Data Mesh Radio #37 Building a Self-Serve Platform Developers Will Actually Use - Interview w/ Audun Fauchald Strand and Gøran Berntsen
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Gøran's Twitter: @gorzan / https://twitter.com/gorzan
Audun's Twitter: @audunstrand / https://twitter.com/audunstrand
Gøran's LinkedIn: https://www.linkedin.com/in/g%C3%B8ran-berntsen-66066517/
Audun's LinkedIn: https://www.linkedin.com/in/audunstrand/
NAIS Platform Website: https://nais.io/
In this episode, Scott interviews Audun Fauchald Strand and Gøran Berntsen of NAV. Audun is the Principal Engineer and Gøran is the Product Manager for NAV's NAIS application platform as well as their emerging self-serve data platform for data mesh called NADA.
They covered a lot of different topics including: 1) building out the platform; 2) working with consumers to set expectations for common data products; 3) definition of a data product - and how it will evolve; 4) setting the frameworks for producer teams and allowing them to own the production; 5) communicating across teams; and 6) Cake! No really, a secret to success is cake.
While NAV is early days in building out their data platform for data mesh, they are taking an interesting approach: work with the developers to set data product expectations and then see how the developers would go about creating those data products. Then, the data platform team will build the platform out to make developer workflows much easier. While Gøran, with a background as a data person, feels the pull to make the self-serve platform as data-centric as possible, he understands the need to make it developer friendly from his time building the application platform with those from a developer background like Audun.
They both talked about reducing friction, including via sensible defaults, as a big part of their path forward. Stop trying to make developers come up with everything themselves. While they are still early days on developing those defaults, they are comfortable in their process to get there. And working with developers along the way is key.
To start, NAV's definition of a data product is a single table or view. It will probably evolve to be more of a data set focus but they don't see a need to prematurely optimize or overcomplicate.
Gøran emphasized the need to have empathy for data producers, to build that into the platform. Teams, whatever the strategic direction, can choose where they focus their time. Don't try to force them to spend it on data, spend the time to really work with them. As Brian McMillan said: find the opportunistic data folks.
NAV tried to put analytics or data engineers into the domains but saw them sitting next to the team, not as part of the team. So they decided to rethink. Those data product developers were likely to become overly crucial to serving the data and thus were a likely single point of failure if they moved on.
Okay, the most important aspect: Cake! For each team that puts a data product onto the mesh, they give that team cake. As in, an actual cake. It might seem silly but it really does work. It makes it feel less daunting to publish a data product and a bit like you are just having fun. It also means the team can show off a bit when they get their picture out there with their cake in the company Slack. And then people can use that cake picture as a jumping off point for learning more about the data product they just shared. It really is a fun community-building hack.
This is a must-listen for anyone involved in building a self-serve platform for the application developers/data product developers.
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