Confluent

Overview

Confluent offers a comprehensive platform built on Apache Kafka, designed for real-time data streaming and processing. The company provides two primary offerings: Confluent Cloud, a fully managed, cloud-native service, and Confluent Platform, an enterprise distribution for self-managed deployments. Both solutions aim to simplify the deployment, scaling, and management of Kafka-based event streaming architectures.

Confluent Cloud abstracts away the operational complexities of Apache Kafka, providing a scalable and resilient environment for developers and organizations. This managed service includes capabilities such as Kafka Connect for integrating with various data systems, ksqlDB for stream processing with SQL, and Stream Governance for schema management and data quality. It is suitable for organizations seeking to build event-driven applications without the overhead of managing Kafka clusters directly.

For organizations requiring more control over their infrastructure, Confluent Platform extends open-source Apache Kafka with additional features such as enterprise-grade security, advanced monitoring, and disaster recovery mechanisms. This platform is often deployed in private data centers or on self-managed cloud instances. Confluent's offerings are utilized across various industries to enable real-time analytics, power microservices communication, modernize legacy systems, and implement robust data pipelines. The platform supports a range of programming languages through its client SDKs, including Java, Python, and Go, reflecting the native Kafka client libraries.

The developer experience with Confluent Cloud emphasizes ease of use, providing a web UI for cluster management and extensive documentation. For local development, Confluent Platform or Docker-based Kafka environments are supported, allowing for iterative development and testing before deployment to managed services or self-hosted clusters.

Key features

• Managed Apache Kafka: Offers a fully managed, cloud-native service for Apache Kafka (Confluent Cloud), reducing operational burden.
• Stream Processing with ksqlDB: Enables real-time data manipulation and analysis using SQL-like queries directly on Kafka streams.
• Kafka Connect: Provides a framework for connecting Kafka with external systems such as databases, key-value stores, search indexes, and file systems.
• Stream Governance: Includes tools for schema registry, data quality enforcement, and data lineage tracking to maintain data integrity across streams.
• Multi-cloud and Hybrid Deployments: Supports deployments across major cloud providers and on-premises environments, offering deployment flexibility.
• Enterprise Security Features: Provides capabilities like role-based access control, encryption, and audit logging for secure data streaming.
• Scalability and Durability: Designed to handle high-throughput, low-latency data streams with built-in fault tolerance and data durability.

Pricing

Confluent Cloud operates on a pay-as-you-go model, with various tiers designed to accommodate different performance and feature requirements. Pricing is primarily based on data throughput, storage, and the specific features enabled for a given cluster. As of May 2026, the free tier offers 50 GB/month for three months. Detailed pricing information is available on the Confluent Cloud pricing page.

Common integrations

• Databases: Connects with relational databases (e.g., PostgreSQL, MySQL) and NoSQL databases (e.g., MongoDB, Cassandra) using Kafka Connectors. Confluent database integration documentation.
• Cloud Services: Integrates with cloud storage (e.g., Amazon S3, Google Cloud Storage), messaging queues (e.g., AWS SQS), and data warehouses (e.g., Snowflake, Google BigQuery). Confluent cloud service integration documentation.
• Monitoring and Alerting Tools: Exports metrics to monitoring systems like Prometheus, Grafana, and Splunk for operational visibility. Confluent monitoring documentation.
• Stream Processing Frameworks: Integrates with Apache Flink and Spark Streaming for complex event processing beyond ksqlDB.

Alternatives

• Amazon MSK: A fully managed service for Apache Kafka provided by AWS, offering serverless and provisioned options.
• Aiven for Apache Kafka: A managed Kafka service that supports multiple cloud providers and includes database and analytics integrations.
• Redpanda: A Kafka-compatible streaming data platform written in C++, designed for performance and operational simplicity.

Getting started

To produce and consume messages using Confluent Cloud with Python, you first need to install the Confluent Kafka client library:

pip install confluent-kafka

Then, set up your Confluent Cloud environment by creating a cluster and generating API credentials. Replace placeholder values with your actual Confluent Cloud API Key, API Secret, and Bootstrap Servers.

from confluent_kafka import Producer, Consumer, KafkaException, TopicPartition
import sys

# Confluent Cloud configuration
conf = {
    'bootstrap.servers': 'YOUR_BOOTSTRAP_SERVERS_HERE', # e.g., pkc-xxxxx.region.aws.confluent.cloud:9092
    'security.protocol': 'SASL_SSL',
    'sasl.mechanisms': 'PLAIN',
    'sasl.username': 'YOUR_API_KEY_HERE',
    'sasl.password': 'YOUR_API_SECRET_HERE'
}

topic = "my_test_topic"

def produce_messages():
    producer = Producer(conf)
    try:
        for i in range(10):
            message = f"Hello Confluent Kafka message {i}"
            producer.produce(topic, key=str(i), value=message.encode('utf-8'))
            print(f"Produced message: {message}")
        producer.flush()
    except KafkaException as e:
        sys.stderr.write(f'%% Producer Error: {e}\n')
    finally:
        producer.flush()

def consume_messages():
    consumer_conf = {
        'bootstrap.servers': conf['bootstrap.servers'],
        'security.protocol': conf['security.protocol'],
        'sasl.mechanisms': conf['sasl.mechanisms'],
        'sasl.username': conf['sasl.username'],
        'sasl.password': conf['sasl.password'],
        'group.id': 'my_consumer_group',
        'auto.offset.reset': 'earliest' # Start consuming from the beginning if no offset is stored
    }
    consumer = Consumer(consumer_conf)
    consumer.subscribe([topic])

    print(f"\nConsuming messages from topic '{topic}'...")
    try:
        while True:
            msg = consumer.poll(1.0) # Poll for messages for 1 second
            if msg is None:
                continue
            if msg.error():
                if msg.error().code() == KafkaException._PARTITION_EOF:
                    # End of partition event, not an error
                    sys.stderr.write('%% %s [%d] reached end at offset %d\n' % (msg.topic(), msg.partition(), msg.offset()))
                elif msg.error():
                    raise KafkaException(msg.error())
            else:
                # Proper message
                print(f"Consumed message: key={msg.key().decode('utf-8')}, value={msg.value().decode('utf-8')}")
    except KeyboardInterrupt:
        pass
    finally:
        consumer.close()

if __name__ == '__main__':
    produce_messages()
    # Give some time for messages to be available for consumption
    import time
    time.sleep(5)
    consume_messages()

This Python script first defines a producer to send 10 messages to a specified Kafka topic. After a short delay to ensure messages are written, a consumer then subscribes to the same topic and retrieves these messages. This basic example demonstrates the core functionality of sending and receiving data through Confluent Cloud.