Here's a step-by-step guide to connecting Confluent Cloud to Databricks:

Step 1: Set up a Confluent Cloud Cluster

• Sign up for a Confluent Cloud account at https://confluent.cloud/ and create a new cluster if you haven't already.
• Once your cluster is ready, note down the following information:
  • Bootstrap Servers (e.g., kafka-brokers.example.com:9092)
  • API Key and Secret (for authentication)

    Step 2: Configure Databricks

    • In your Databricks workspace, create a new notebook or open an existing one.
    • Set up the necessary configuration using the Databricks secret scope feature. Secrets allow you to securely store sensitive information like credentials. To create a secret scope and add secrets, follow these steps:
      • Open the notebook and run the following command to create a secret

      • scope:dbutils.secrets.createScope(scopeName)

        Configure the Confluent Cloud secrets by adding the bootstrap servers, API Key, and Secret to the created secret scope:

        dbutils.secrets.put(scope = scopeName, key = "kafka.bootstrap.servers", value = "kafka-brokers.example.com:9092")
        dbutils.secrets.put(scope = scopeName, key = "kafka.security.protocol", value = "SASL_SSL")
        dbutils.secrets.put(scope = scopeName, key = "kafka.sasl.mechanism", value = "PLAIN")
        dbutils.secrets.put(scope = scopeName, key = "kafka.sasl.jaas.config", value = "org.apache.kafka.common.security.plain.PlainLoginModule required username=\"<API_KEY>\" password=\"<API_SECRET>\";")

        Step 3: Create a Streaming DataFrame in Databricks

        • In the same Databricks notebook, you can now create a Streaming DataFrame to consume data from Confluent Cloud. Here's an example code snippet:
          python

          from pyspark.sql.functions import from_json, col
          from pyspark.sql.types import StructType, StringType, DoubleType
          
          # Define the schema of the incoming data
          schema = StructType().add("name", StringType()).add("age", DoubleType())
          
          # Read data from Kafka topic
          kafka_bootstrap_servers = dbutils.secrets.get(scope = scopeName, key = "kafka.bootstrap.servers")
          df = spark \
            .readStream \
            .format("kafka") \
            .option("kafka.bootstrap.servers", kafka_bootstrap_servers) \
            .option("subscribe", "topic-name") \
            .option("startingOffsets", "earliest") \
            .load()
          
          # Extract and process the data
          processed_df = df \
            .select(from_json(col("value").cast("string"), schema).alias("data")) \
            .select("data.name", "data.age")
          
          # Start the streaming query
          query = processed_df.writeStream \
            .outputMode("append") \
            .format("console") \
            .start()
          
          query.awaitTermination()

          Step 4: Customize the code as per your requirements

          • Modify the code snippet above to suit your specific use case. Update the schema definition, Kafka topic name, and any data transformations or output sinks as needed