Confluent

Overview

Confluent provides a suite of products built around Apache Kafka, an open-source distributed streaming platform. The company was founded in 2014 by the creators of Kafka themselves, aiming to commercialize and enhance its capabilities for enterprise use cases. Confluent offers two primary products: Confluent Cloud, a fully managed, cloud-native service, and Confluent Platform, an enterprise distribution for self-managed deployments.

Confluent Cloud abstracts away the operational complexities of Apache Kafka, offering a serverless experience with automatic scaling, patching, and monitoring. This managed service is designed for developers and technical buyers looking to implement real-time data pipelines, build event-driven architectures, and process data streams without managing the underlying infrastructure. It supports various cloud providers including AWS, Google Cloud, and Azure, allowing for multi-cloud or hybrid cloud deployments.

The platform is optimized for scenarios requiring high throughput and low latency, such as capturing clickstream data, processing financial transactions, or integrating data across disparate systems in real time. It extends Apache Kafka with additional features like Schema Registry for data governance, Kafka Connect for data integration, and ksqlDB for stream processing. These components aim to streamline the development and deployment of event streaming applications.

Confluent targets developers and technical buyers in organizations that require robust, scalable, and compliant event streaming capabilities. Its developer experience includes client libraries for multiple programming languages such as Java, Python, and Go, along with a comprehensive documentation portal and a control plane for managing Kafka clusters and resources. The focus on a managed service aims to reduce the operational overhead typically associated with self-hosting and scaling Apache Kafka.

Key features

• Confluent Cloud: A fully managed, serverless Apache Kafka service that handles infrastructure provisioning, scaling, and maintenance across major cloud providers (AWS, Google Cloud, Azure).
• Apache Kafka Compatibility: Built on open-source Apache Kafka, ensuring compatibility with existing Kafka clients and tools, and avoiding vendor lock-in.
• Schema Registry: Centralized management and enforcement of data schemas for Kafka topics, helping maintain data quality and compatibility across applications (Confluent Schema Registry).
• Kafka Connect: A framework for connecting Kafka with external systems such as databases, key-value stores, search indexes, and file systems, with a wide array of pre-built connectors.
• ksqlDB: A SQL streaming database for building real-time event processing applications using standard SQL queries, enabling transformations, aggregations, and joins on data streams (ksqlDB documentation).
• Stream Governance: Tools for data lineage, auditing, and role-based access control to manage and secure data streams (Stream Governance features).
• Multi-Cloud and Hybrid Deployments: Support for deploying Kafka clusters and connecting data across multiple public clouds and on-premises environments.
• Client SDKs: Official client libraries for Java, Python, .NET, Go, Node.js, and C/C++, facilitating application development (Confluent Client SDKs).
• Developer Tools: Command-line interface (CLI), REST APIs, and a web-based UI for managing clusters, topics, and connectors (Confluent Cloud API reference).

Pricing

Confluent Cloud employs a usage-based pricing model, primarily billed on data ingress, egress, and storage. Different tiers (Basic, Standard, Dedicated, Kafka Connect, ksqlDB) offer varying levels of throughput, features, and isolation. Pricing also includes charges for network egress and data retention. A free tier is available, offering $400 in usage for 30 days to new users.

For detailed and up-to-date pricing information, including specific regional rates and enterprise options, refer to the official Confluent pricing page.

Common integrations

• Databases: Integration with relational and NoSQL databases like PostgreSQL, MySQL, MongoDB, Oracle, and Cassandra using Kafka Connectors for databases (e.g., Debezium for change data capture).
• Cloud Storage: Connecting to cloud object storage services such as Amazon S3, Google Cloud Storage, and Azure Blob Storage for archiving and batch processing.
• Data Warehouses & Lakes: Integration with analytical platforms like Snowflake, Google BigQuery, Amazon Redshift, and Databricks.
• Monitoring & Observability Tools: Exporting metrics and logs to systems like Datadog, Prometheus, Grafana, and Splunk for operational insights.
• Event-Driven Microservices: Using Kafka as the central nervous system for microservice communication, with client libraries supporting various programming languages.
• Cloud Services: Native integrations and connectors for various services within AWS, Google Cloud, and Azure ecosystems, including serverless functions and messaging queues.

Alternatives

• Amazon MSK: A fully managed service by AWS for Apache Kafka, offering deep integration with other AWS services.
• Redpanda: A Kafka-compatible streaming data platform written in C++, designed for high performance and lower operational overhead.
• Aiven for Apache Kafka: A managed Kafka service provided by Aiven, offering deployment across multiple cloud providers with additional data platform services.
• Google Cloud Pub/Sub: Google's serverless messaging service, designed for asynchronous messaging and stream delivery, often used as an alternative for event-driven architectures.
• Azure Event Hubs: Microsoft Azure's highly scalable data streaming platform, capable of ingesting millions of events per second.

Getting started

To get started with Confluent Cloud, you typically begin by creating an account, launching a Kafka cluster, and then producing or consuming messages. The following Python example demonstrates how to produce a single message to a Kafka topic using the Confluent Python client library. This assumes you have pip installed and configured your Confluent Cloud API key and secret.

from confluent_kafka import Producer
import json

# Configuration for Confluent Cloud
# Replace with your Confluent Cloud API key and secret
# and your Kafka bootstrap server URL
conf = {
    'bootstrap.servers': 'YOUR_BOOTSTRAP_SERVER_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'
}

producer = Producer(conf)

def delivery_report(err, msg):
    """ Called once for each message produced to indicate delivery result.
        Triggered by poll() or flush(). """
    if err is not None:
        print(f"Message delivery failed: {err}")
    else:
        print(f"Message delivered to {msg.topic()} [{msg.partition()}] @ offset {msg.offset()}")

# Produce a message
topic = "my-first-topic"
key = "my-key"
value = {"message": "Hello, Confluent Cloud!", "timestamp": "2026-05-08T12:00:00Z"}

try:
    producer.produce(topic, key=key.encode('utf-8'), value=json.dumps(value).encode('utf-8'), callback=delivery_report)
    # It is important to call poll() or flush() to ensure messages are sent
    producer.poll(1)

except Exception as e:
    print(f"Error producing message: {e}")
finally:
    # Wait for any outstanding messages to be delivered and delivery reports received
    producer.flush()

Before running this code:

1. Install the Confluent Kafka client: pip install confluent-kafka
2. Create a Kafka cluster in Confluent Cloud (or use an existing one).
3. Create an API key and secret for your cluster under "API keys" in the Confluent Cloud UI (Confluent Cloud API key creation guide).
4. Create a topic named my-first-topic within your cluster.
5. Replace YOUR_BOOTSTRAP_SERVER_HERE, YOUR_API_KEY_HERE, and YOUR_API_SECRET_HERE with your actual cluster details.

After executing, the delivery_report function will confirm message delivery to your Kafka topic. You can then use a consumer client or the Confluent Cloud UI to verify the message.