Connecting confluent to databricks.

Mbinyala — Mon, 03 Jul 2023 12:46:45 GMT

Hi!!

Can someone tell me how to connect the confluent cloud to Databricks? I am new to this so please elaborate on your answer.

Re: Connecting confluent to databricks.

Anonymous — Tue, 04 Jul 2023 09:02:13 GMT

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

Step 1: Set up a Confluent Cloud Cluster

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

topic Re: Connecting confluent to databricks. in Data Engineering

Connecting confluent to databricks.

Re: Connecting confluent to databricks.

Re: Connecting confluent to databricks.