awesome-jsonschema VS schema-registry

Truth. Zod is comparable to JSON Schema plus AJV, and it doesn't compare well at all. Your Zod code is all locked inside TypeScript so not only can it not be shared to any other language in your stack but it also cannot be serialized, which introduces many limitations. You also miss out on all the JSON Schema ecosystem tooling. (1, 2) For example the intellisense you get in VS Code for config files is powered by JSON Schema and schemastore.

The very first line of text below the header on the json-schema.org homepage is:

The framework's official website mentions a number of pros of FastAPI. In my opinion, the most useful features from a microservice perspective are: the simplicity of code (easy to use and avoid boilerplate), high operational capacity thanks to Starlette and Pydantic and compatibility with industry standards - OpenAPI and JSON Schema.

I've used Felte to reduce form boilerplate. Felte supports several different validation libraries like Zod. I actually used a custom validation function with ajv (which uses JSON schema).

(There is already a JSON Schema definition at https://json-schema.org/)

Like you said - standard XML isn't terrible. Adding on an XSD isn't terrible, because now you can enforce structure and datatypes on files provided by outside parties. Creating an XSLT is much more of a mental challenge, and probably should be left to tools to define.

Anything beyond those technologies is someone polishing up their resume.

While the aws-sdk is being installed to simulate DynamoDB locally, let me explain a few things about this command. First Comment will be the name of the model the scaffold creates. This model will be codified under app/models/schemas/comment.mjs as a JSON Schema object. Each of the parameters after Comment will be split into a property name and type (e.g. property name “subject”, property type “string”). This JSON Schema document will be used to validate the form data both on the client and server sides.

What you are looking is called Json-schema. Have a look at the implementations page, which will give you an idea of what you can do with json-schema, which also includes UI rendering.

So, is JR yet another faking library written in Go? Yes and no. JR indeed implements most of the APIs in fakerjs and Go fake it, but it's also able to stream data directly to stdout, Kafka, Redis and more (Elastic and MongoDB coming). JR can talk directly to Confluent Schema Registry, manage json-schema and Avro schemas, easily maintain coherence and referential integrity. If you need more than what is OOTB in JR, you can also easily pipe your data streams to other cli tools like kcat thanks to its flexibility.

Check here for more details. https://github.com/confluentinc/schema-registry

Confluent Schema Registry for Apache Kafka [GitHub]

So that means we can configure the Kafka producer and consumer with an imaginary schema registry url, that only needs to start with “mock://” and you automatically get to work with the MockSchemaRegistryClient. This way you don't need to explicitly initiate the MockSchemaRegistryClient and configure everything accordingly. That also eradicates the need for the Confluent Schema Registry Container. Running the Kafka Testcontainer with the embedded Zookeeper, we no longer need an extra Zookeeper container and we are down to one Testcontainer for the messaging. This way I ended up with only two Testcontainers: Kafka and the database.

Confluent is a company founded by the creators of Apache Kafka. They are providing the Confluent Platform which consists of several components, all based on Kafka. The license for these components vary. The Schema Registry has the community-license, which basically means it's free to use as long as you don't offer the Schema Registry itself as a SaaS solution. The source code can be found on Github.

Schema Registry from Confluent (GitHub) => http://localhost:8081/