Netflix has 238 million subscribers.
Spotify has 626 million listeners.
Both companies run some of the most complex data operations on the planet — and both juggernauts arrived at the same conclusion some years ago: metadata isn't just some fancy word for "documentation."
It's vital infrastructure.
While most enterprises treat metadata as an afterthought (something, say, you capture in a spreadsheet or tag when you get around to it) these companies engineered entire platforms around it.
The results speak for themselves: self-serve data cultures that scale, compliance that doesn't require an army, and engineering teams that build instead of firefight.
After doing some digging, here's what we found out what they figured out — and what most Salesforce and RevOps orgs are still missing.
Netflix: When a "Technical Bridge" Wasn't Enough
Netflix's metadata journey started with a tool called Metacat. It was a federated metadata service — essentially a unified API that let teams access metadata from various data stores like Hive, Druid, and Redshift. Think of it more like a translator that helped different systems talk to each other.
For a while, it worked. But as Netflix scaled, Metacat hit its limits.
The problem wasn't technical capability. It was that Metacat was designed as a bridge, not a backbone.
When GDPR arrived, Netflix needed to answer questions like: Where does this customer's data live? Who has access? What's the consent status? Metacat couldn't tell them this stuff.
When finance wanted to understand the ROI of stored data — for example what was actually being used versus what was just accumulating storage cost s— Metacat couldn't help there either.
So, smartly, Netflix evolved. They built the Netflix Data Catalog, which shifted metadata from a technical convenience to an operational system. NDC doesn't just tell you where data lives. It handles:
- Data discovery: Helping teams find the right dataset without asking five people
- Governance: Enforcing privacy and access rules automatically
- Cost reporting: Showing which data is worth keeping and which is just burning money
The big insight? Netflix stopped thinking of metadata as something that describes their data and started treating it as something that governs their data.
That shift enabled them to maintain a self-serve culture — where thousands of engineers can access what they need without bottlenecks — while still meeting the most rigorous compliance requirements.
Spotify: Curing Microservice Chaos
According to numerous blogs from tech experts there, Spotify faced a different problem, but funny enough... landed on the same solution.
By the mid-2010s, Spotify had hundreds of engineering teams building thousands of microservices.
The architecture was decentralized by design — small, autonomous teams shipping fast. But decentralization has a shadow side: nobody knew who owned what.
A new engineer would join and spend their first two weeks just figuring out where things lived. Need to understand how a service works? Good luck finding the documentation — if it exists, it's probably in a wiki that hasn't been updated in two years. Want to spin up a new service? Hope you enjoy navigating twelve different tools and asking around on Slack.
Spotify called this "microservice chaos." Their solution was Backstage.
Backstage is an internal developer portal — now open-source — that functions as a metadata engine for software assets. Every service, every component, every API gets a catalog-info.yaml file that lives alongside the code.
That file defines:
- Ownership: Who's responsible for this service?
- Lifecycle: Is it production, deprecated, experimental?
- Dependencies: What does it connect to?
- Documentation: Where do I learn more?
The metadata isn't stored in some separate system that engineers forget to update. It's in the repo, versioned with the code, part of the development workflow.
The results were quite dramatic:
- New-hire ramp-up dropped from 14 days to just 5 days
- Scaffolding a new service went from weeks to minutes
- Engineers stopped spending half their time on "discovery" and started spending it on building
Yes, the investment was real. Spotify estimates a dedicated team and nearly $1M/year in resources. But the ROI in developer velocity and reduced cognitive load paid it back many, many times over.
The Pattern: Metadata as Infrastructure, Not Afterthought
Netflix and Spotify arrived at metadata-first architectures from different directions — one through compliance pressure, the other through engineering chaos.
But they landed on the same core principles:
1. Metadata lives where the work happens. Netflix embedded governance into the data catalog. Spotify embedded service metadata into the codebase. Neither company asks people to go somewhere else to update a spreadsheet. The metadata is part of the system, not a parallel track.
2. Metadata is active, not passive. A traditional data catalog is like a library index — it tells you where something is. Netflix and Spotify built systems that do things with metadata: enforce policies, trigger alerts, calculate costs, automate onboarding. The metadata isn't just descriptive. It's alive. It's... operational.
3. Metadata enables self-service at scale. The whole point of investing in metadata infrastructure is to remove bottlenecks. When every dataset has discoverable, trustworthy metadata, you don't need a data team gatekeeper for every request. When every service has documented ownership, new engineers don't need to interrupt ten people to get oriented.
The Gap: What Most Salesforce Orgs Are Missing
Now translate this to the average Salesforce environment.
Most RevOps teams are running the equivalent of Metacat circa 2015.
They know the data exists — umm, somewhere. They might have a spreadsheet documenting fields, if someone remembers to update it. When leadership asks "what data do we have on this customer?" or "why did this automation break?", the answer involves digging through multiple systems and asking the one person who remembers how things were set up.
Meanwhile, the AI era is arriving. Or rather, already here.
Companies are deploying Agentforce, building RAG applications, trying to make their CRM data actually do things. But here's the catch: AI systems are only as good as the metadata that guides them.
This will always be the case.
Without metadata, a retrieval system can't distinguish between a quote from 2019 and a quote from yesterday. Without lineage, you can't trace why an automation fired incorrectly. Without ownership data, every broken flow becomes a (not-very-fun) game of "who touched this last?"
Netflix and Spotify figured this out years ago.
They built the metadata infrastructure before they needed it to be perfect— because they knew the cost of not having it compounds over time.
The Takeaway
The companies winning at data (whether that's streaming recommendations or developer productivity) are the ones with the most intelligent data.
And intelligence, in a data system, is just another word for metadata.
Netflix didn't get governance right by hiring more compliance people. Spotify didn't solve microservice chaos by writing more documentation. They built systems where metadata is infrastructure — always on, always current, always working.
That's the shift that's coming to every data-intensive operation, including Salesforce.
The question is whether you build the metadata backbone now, or scramble to retrofit it later.
Shameless Plug for Sweep
At Sweep, we're building the metadata layer that makes Salesforce systems AI-ready — the kind of always-on intelligence that Netflix and Spotify engineered for their domains.
If your team is spending more time searching for answers than acting on them, that's the gap metadata infrastructure closes.
Want to see what metadata-first looks like for Salesforce? Talk to us about an Agentforce Assessment.