iomete / kafka-streaming-job

Kafka streaming job from iomete. This streaming job copies data from Kafka to Iceberg.

iomete: Kafka Streaming Job

This is a collection of data movement capabilities. This streaming job copies data from Kafka to Iceberg.

Table of Contents

• Deserialization
• Job creation
• Tests

Deserialization

Currently, two deserialization format supported.

1. JSON
2. AVRO

JSON

In the Spark configuration, a user-defined reference json schema can be defined, and the system processes the binary data accordingly. Otherwise, It considers the schema of the first row and assumes the rest of the rows is compatible.

Avro

Converts binary data according to the schema defined by the user or retrieves the schema from the schema registry.

Job creation

• Go to Spark Jobs.
• Click on Create New.

Specify the following parameters (these are examples, you can change them based on your preference):

• Name: kafka-streaming-job
• Docker Image: iomete/iomete_kafka_streaming_job:0.2.1
• Main application file: local:///app/driver.py
• Environment Variables: LOG_LEVEL: INFO or ERROR
• Config file:

{
  kafka: {
      bootstrap_servers: "localhost:9092",
      topic: "usage.spark.0",
      serialization_format: json,
      group_id: group_1,
      starting_offsets: latest,
      trigger: {
        interval: 5
        unit: seconds # minutes
      },
      schema_registry_url: "http://127.0.0.1:8081"
  },
  database: {
    schema: default,
    table: spark_usage_20
  }
}

Configuration properties

Property	Description
kafka	Required properties to connect and configure. bootstrap_servers Kafka broker server. topic Kafka topic name. serialization_format Value data serialization format. group_id Consumer group id. starting_offsets Specify where to start instead. trigger interval Processing trigger interval. unit Processing trigger unit: seconds, minutes
database	Destination database properties. schema Specify the schema (database) to store into. table Specify the table.

Create Spark Job

Create Spark Job - Instance

You can use Environment Variables to store your sensitive data like password, secrets, etc. Then you can use these variables in your config file using the ${ENV_NAME} syntax.

Create Spark Job - Application Environment

Create Spark Job - Application dependencies

Tests

Prepare the dev environment

virtualenv .env #or python3 -m venv .env
source .env/bin/activate

pip install -e ."[dev]"

Run test

pytest