How Airbnb Achieved Metric Consistency at Scale

Following up on the "metrics layer" / headless BI post from last issue, it just so happens that Airbnb has published the first major update on their progress towards this architecture in (I think?) four years..! It's a fantastic post, and if this is a topic you're interested in I highly recommend reading it.

The one thing that I think is not reckoned with in this post is that Airbnb has essentially built the entire beginning-to-end data stack on their own using internal engineering resources. I'm certainly not saying that this is a bad decision for Airbnb, but it is noteworthy insofar as not every company is Airbnb, and software maintenance is expensive. In the build-vs-buy question, I think adopting best-of-breed modular solutions (open source or commercial) is the way to go.

Which gets to what is so hard here. You can build a data catalog that's aware of your internally-built metric system and integrates with it tightly—same for an A/B testing tool, BI tool, etc. But you can't really buy one off the shelf with this same property. And since the "metrics layer" sits right in the middle of everything (see the "Minerva Data API" box on the diagram above), integrations with the rest of the stack are critical. If we wanted to solve this in a more off-the-shelf way, how do we get an entire ecosystem to consolidate around a standard?


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