Ask five data engineers how they prevent schema drift and you will get five different answers built around three different tools: data contracts, schema registries, and some kind of runtime monitoring layer. Vendors selling each one will tell you it solves the problem. None of them, on their own, actually does. Each covers a different phase of the pipeline, and each has a specific point where it stops working. Understanding where those boundaries sit is the difference between a stack that catches drift and one that just feels like it does.

What a Data Contract Actually Guarantees

A data contract is an explicit agreement between the team that produces a piece of data and the teams that consume it: field names, types, nullability, and often service level expectations like update frequency. Tools like Buf for protobuf, Confluent's Schema Registry compatibility modes, or open source frameworks built around a data contract CLI let teams enforce that agreement in CI. If a producer tries to ship a breaking change, the build fails before it ever reaches production.

This works well when both sides of the contract are internal, and both sides are willing to sign one. It works less well the moment one side of the integration is a system you do not control.

The Vendor Problem: Nobody Signs a Contract With Salesforce

SAP does not submit its Enhancement Package release notes for your approval. Salesforce does not open a pull request against your schema before its Spring release ships. Workday does not run your CI pipeline before an EIB template changes. These are the systems most enterprise integrations actually depend on, and none of them participate in a data contract, because a data contract requires both sides to agree to be bound by it.

That is the first hard boundary. Contracts govern the systems your team builds. They have no leverage over the systems your company buys.

Schema Registries Catch Structure, Not Meaning

Schema registries solve a narrower, more mechanical problem: they enforce compatibility rules on the shape of a message. Add a field, and backward compatibility mode lets old consumers keep reading. Remove a required field, and the registry blocks the write. This is genuinely useful for event streams built on Avro or Protobuf, and it catches a meaningful share of the drift that happens inside a well governed Kafka deployment.

What it does not catch is a field that keeps its name, its type, and its required status, but quietly changes what it means. A currency field that switches from cents to dollars. A status enum that keeps the same string values but now represents a different point in the workflow. A revenue field that used to mean recognized revenue and now includes deferred revenue after a policy change. Structurally, none of this trips a compatibility check. A message with a valid schema sails right through the registry. The registry did its job. The data is still wrong.

Runtime Monitoring Is the Layer Both of Them Miss

Contracts and registries both operate at build time or deploy time, on data you control the schema of. Neither one is watching the data actually moving through your integrations in production, in real time, including the parts that come from systems you did not build and cannot put a contract in front of.

That is the gap a runtime layer is built to close: continuous comparison of the values actually flowing through an integration against expected ranges, distributions, and semantic baselines, not just against a schema definition. It has to run after deployment, not just before it, because drift introduced by a third party vendor release does not check your CI pipeline's schedule first. A Salesforce field rename can land on a Tuesday afternoon with no warning. The only thing watching for it has to already be running.

A Practical Way to Combine All Three

None of this is an argument against contracts or registries. Use a data contract at every internal service boundary your team owns. Use a schema registry for structured event streams where Avro or Protobuf compatibility modes apply. Both are cheap, mature, and catch real problems early, before they cost anything.

Data contracts enforce structural agreements between systems you control, checked in CI before deploy.
Schema registries enforce compatibility rules on structured event data, checked at write time.
Runtime semantic monitoring watches live data for meaning changes, value range shifts, and drift from systems you do not control, checked continuously in production.

Then add a layer above all of it that assumes drift will happen anyway, because a real slice of your integration estate runs on systems that will never sign your contract or write to your registry. That layer needs to watch business meaning, not just structure, and it needs to run continuously against production traffic, not just against a build.

Where This Actually Shows Up

The teams that feel this gap first are usually the ones who already did the hard work of adopting contracts internally, then watched a Salesforce release or a SAP Enhancement Package quietly break a downstream ERP sync that no contract ever covered. They go looking for the tool that should have caught it, and find that their contract tests all passed and their schema registry logged zero compatibility violations, because neither one was ever positioned to see the problem.

The fix is not a better contract or a stricter registry. It is accepting that both have a real, permanent boundary, and building the monitoring layer that starts where that boundary ends. For most enterprises, that boundary sits exactly at the edge of the systems they do not own the release calendar for.

mmune runs as a read-only overlay above your existing integration stack, including the SaaS and ERP systems that will never sign a data contract or write to your schema registry. It watches for schema and semantic drift in real time and heals what it can before your team opens a ticket. If you want to see exactly where your own stack's contract and registry coverage stops, find your blind spots.

Data Contracts, Schema Registries, and Runtime Monitoring: Where Each One Stops Working