Connect Apache Avro
One governed reading of the schema that currently lives in four places.
How CoreModels works with Apache Avro
Avro is precise about structure and compatibility. It is not a catalog of business meaning. Field docs rot, enums fork, and the same record is copied into Spark, the registry, and a downstream SQL table by hand.
CoreModels decodes Avro into the same internal model used for JSON Schema, Protobuf, and SQL. Names, types, null unions, defaults, and symbols become elements and taxonomies. You can then audit a newer schema against the governed baseline, or encode the governed model back out to Avro with an honest ledger of anything the round trip cannot preserve.
Use it when the schema is the contract consumers actually compile against — and you need that contract to survive the other three copies.
Recipes
No recipes yet for Apache Avro. The articles below still describe the problem this connector is built to close.
Uses
Problems this connector fixes
Articles that explain the gap, then point at the recipe that closes it.
The Four Places Your Avro Schema Isn't
Someone posts a question in a team channel: *what are the legal values of `status` on the orders stream?*
OutcomesThe Day After Import: What a Team Can Do Once Avro Is a Projection
Four requests land on a platform team in one week. None is unusual, and each is, underneath, the same request wearing a different hat.
GovernanceNothing Silent: The Rules Behind Every Avro Round Trip
The dangerous transformation is not the one that fails.
EcosystemAvro Won a Layer, Not the Estate
Every durable format is built around one decision, and Avro's is easy to name: the schema travels with the data. A reader does not guess; it is handed the writer's schema and reconciles it with its own. That single choice produced the compact encoding, the evolution rules, the registries, and the reason Avro is still a default schema language of event streaming.
AgentsStop Guessing the Union Branch: Avro for AI Agents
An agent is asked to produce test records for an orders topic. It has seen ten sample messages, and it writes what those samples look like: a JSON object with an `age` of `30`.
QuickstartYour First Avro Transform: One Call, Three Things to Read
You have an `.avsc` file. Somewhere downstream, somebody needs the same shape as JSON Schema, or as a table, or as a data contract — and they need to know what the conversion cost. This article gets you from that file to a converted schema in a single HTTP call, and then teaches you to read the three parts of the answer: the schema, the plan, and the lossiness ledger.
APIAvro Over HTTP: Four Endpoints and Their Honest Limits
Every transform route in CoreModels answers with the same envelope, so learning the contract once buys you the whole surface:
MCPHanding Avro to an Agent: `transform_schema` End to End
Ask a language model to "convert this Avro schema to LinkML" and it will improvise — plausible YAML on a good day, invented field names on a bad one. Connect it to CoreModels over MCP and the same sentence becomes a tool call: typed arguments, a deterministic engine, and a machine-readable account of what the conversion cost. This article is the whole loop for Avro — connection, exact arguments, a real conversion, and what a well-behaved agent does with the answer.
AutomationShip the Plan, Not the Script: Avro Pipelines That Replay
Here is a job every streaming platform eventually has. An internal topic carries a rich record; a partner, a public catalog, or another business unit gets a redacted projection of it. Somebody writes a script. The script knows which fields to drop and which to rename, and that knowledge lives nowhere else. Six months later the source schema gains a field, the script silently passes it through, and the first person to notice is on the other side of the boundary.
Deep diveEvery Avro Construct, and Where It Lands in the IR
Does an `int` come back as an `int`?