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Schemas are used when reading and writing data. Schemas must be shared between the services that send the data and the one that consumes them and these need a centralized resource where to govern the information about the schema.
Within the Kafka world, there are different protocols you can use for serialization (e.g. JSON or ProtoBuf) but Apache Avro is by far the most widely used serialization protocol. Apache Avro is a data serialization system that combined with Kafka provides robust and fast schema-based binary serialization.
The schema can be versioned and distributed as another source. It must be shared between projects. Because from the schema it must be compiled (generate the sources) for the language with which the consumer or producer is going to program.
Let’s create a simple application that receives HTTP requests, writing messages in Kafka, and reading them from Kafka. For simplicity, the same application will write to the Kafka broker and read from it, but in the real world they would be different applications.
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An Avro schema specifies the structure of an Avro data block. Like JSON schemas, Avro schemas specify how JSON data is organized in a JSON document. They specify which data fields should be expected and how the values are represented. Information about Avro schemas and their specification can be found here.
-An Avro schema is created in JSON format.-An Avro schema can be a JSON string, a JSON object, or a JSON array.-An Avro schema can contain four attributes: name, namespace, type, and fields.-There are eight primitive data types: null, boolean, int, long, float, double, bytes, and string.-There are six complex data types: records, enums, arrays, maps, unions, and fixed.-Primitive types have no attributes. Each complex type has its own set of attributes.
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Apache Avro is a frequently used data serialization system in the streaming world. A typical solution is to place Avro-formatted data in Apache Kafka, metadata in the Confluenty Schema Registry, then run queries with a streaming framework that connects to both Kafka and Schema Registry.
Azure Databricks supports the functions and to compile streaming pipelines with Avro data in from_avroto_avrofrom_avro Kafka and metadata in schema registry. The function encodes a column as binary in Avro format and decodes the binary data from to_avrofrom_avro Avro into a column. Both functions transform a column into another column and the input/output SQL data type can be a complex or primitive type.
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In event-driven architectures the data contained in those events also needs to be governed. In this post, we are going to see how using data schemas, we can achieve data management in Kafka-based architectures, thanks to Apache Avro.
Nowadays the volume of information that is produced and consumed does not stop growing, for this reason, it is important to become aware of the use of data schemas. The objective is to centralize and offer more control over the data.
By using a data schema, the data owner is responsible for defining the compatibility (backward or forward) of the schema, relieving the consumers and producers that use the data of validation tasks.
For example, when JSON is used to share information, the compatibility of that data is “backward” by default. Because when a new field is introduced, old consumers ignore it. But in my opinion this is not quite correct, because those consumers should fail to exist a new field that is not recognized. This is a possible reason to start using schemas and their validation process.