avro | Apache Avro is a data serialization system | Serialization library

We are trying to create avro record with confluent schema registry. The same record we want to publish to kafka cluster.

To attach schema id to each records (magic bytes) we need to use--
to_avro(Column data, Column subject, String schemaRegistryAddress)

To automate this we need to build project in pipeline & configure databricks jobs to use that jar.

Now the problem we are facing in notebooks we are able to find a methods with 3 parameters to it.
But the same library when we are using in our build downloaded from https://mvnrepository.com/artifact/org.apache.spark/spark-avro_2.12/3.1.2 its only having 2 overloaded methods of to_avro

Is databricks having some other maven repository for its shaded jars?

NOTEBOOK output

I am using Benthos to read AVRO-encoded messages from Kafka which have the kafka_key metadata field set to also contain an AVRO-encoded payload. The schemas of these AVRO-encoded payloads are stored in Schema Registry and Benthos has a schema_registry_decode processor for decoding them. I'm looking to produce an output JSON message for each Kafka message containing two fields, one called content containing the decoded AVRO message and the other one called metadata containing the various metadata fields collected by Benthos including the decoded kafka_key payload.

Is there a way to publish message to an Apache Pulsar topic using Protobuf schema using pulsar-client package using python?

As per the documentation, it supports only Avro, String, Json and bytes. Any work around for this? https://pulsar.apache.org/docs/ko/2.8.1/client-libraries-python/

I have pretrained model for object detection (Google Colab + TensorFlow) inside Google Colab and I run it two-three times per week for new images I have and everything was fine for the last year till this week. Now when I try to run model I have this message:

I am following this tutorial on migrating data from an oracle database to a Cloud SQL PostreSQL instance.

I am using the Google Provided Streaming Template Datastream to PostgreSQL

At a high level this is what is expected:

1. Datastream exports in Avro format backfill and changed data into the specified Cloud Bucket location from the source Oracle database
2. This triggers the Dataflow job to pickup the Avro files from this cloud storage location and insert into PostgreSQL instance.

When the Avro files are uploaded into the Cloud Storage location, the job is indeed triggered but when I check the target PostgreSQL database the required data has not been populated.

When I check the job logs and worker logs, there are no error logs. When the job is triggered these are the logs that logged:

With org.springframework.kafka:spring-kafka up to version 2.7.9, my Spring-Boot application (consuming/producing Avro from/to Kafka) starts fine, having these environment variables set:

I am trying to bring in JIRA data into Foundry using an external API. When it comes in via Magritte, the data gets stored in AVRO and there is a column called response. The response column has data that looks like this...

I'm trying to use Kafka Streams to perform KTable-KTable foreign key joins on CDC data. The data I will be reading is in Avro format, however it is serialized in a manner that wouldn't be compatible with other industry serializer/deserializers (ex. Confluent schema registry) because the schema identifiers are stored in the headers.

When I setup my KTables' Serdes, my Kafka Streams app runs initially, but ultimately fails because it internally invokes the Serializer method with byte[] serialize(String topic, T data); and not a method with headers (ie. byte[] serialize(String topic, Headers headers, T data) in the wrapping serializer ValueAndTimestampSerializer. The Serdes I'm working with cannot handle this and throw an exception.

First question is, does anyone know a way to implore Kafka Streams to call the method with the right method signature internally?

I'm exploring approaches to get around this, including writing new Serdes that re-serialize with the schema identifiers in the message itself. This may involve recopying the data to a new topic or using interceptors.

However, I understand ValueTransformer has access to headers in the ProcessorContext and I'm wondering if there might there be a faster way using transformValues(). The idea is to first read the value as a byte[] and then deserialize the value to my Avro class in the transformer (see example below). When I do this however, I'm getting an exception.

I tried to run my Spark/Scala code 2.3.0 on a Cloud Dataproc cluster 1.4 where there's Spark 2.4.8 installed. I faced an error concerning the reading of avro files. Here's my code :