Confluent

Confluent is the enterprise data streaming platform built on Apache Kafka by its original creators, providing fully managed Kafka (Confluent Cloud), 120+ pre-built connectors, and stream processing capabilities. In this Confluent review, we examine how the platform extends Kafka for enterprise production use with managed infrastructure, governance, and real-time data integration.

Overview

Confluent (confluent.io) was founded in 2014 by Jay Kreps, Neha Narkhede, and Jun Rao — the original creators of Apache Kafka at LinkedIn. The company went public in June 2021 (NASDAQ: CFLT) and generates $800M+ in annual revenue. Confluent serves 5,000+ customers including 75% of the Fortune 500.

The platform provides three deployment options: Confluent Cloud (fully managed, serverless Kafka), Confluent Platform (self-managed enterprise Kafka distribution), and Confluent for Apache Flink (managed stream processing). Confluent extends open-source Kafka with enterprise features: Schema Registry for data governance, 120+ pre-built connectors, ksqlDB for stream processing with SQL, and Cluster Linking for multi-region replication.

Key Features and Architecture

Confluent Cloud (Managed Kafka)

Fully managed, serverless Apache Kafka that eliminates cluster provisioning, scaling, and maintenance. Available on AWS, GCP, and Azure across 70+ regions. Confluent Cloud handles broker management, partition rebalancing, security patching, and scaling — teams focus on producing and consuming data.

120+ Pre-Built Connectors

Kafka Connect connectors for databases (PostgreSQL, MySQL, MongoDB, Oracle), cloud services (S3, BigQuery, Snowflake), SaaS applications (Salesforce, ServiceNow), and messaging systems (RabbitMQ, JMS). Fully managed connectors run on Confluent's infrastructure without requiring dedicated Connect workers.

Schema Registry

A centralized schema management service that enforces data contracts between producers and consumers. Schema Registry supports Avro, Protobuf, and JSON Schema formats with compatibility checking (backward, forward, full) that prevents breaking changes from reaching consumers.

Stream Processing (ksqlDB and Flink)

ksqlDB enables stream processing with SQL syntax — filtering, joining, aggregating, and transforming streaming data without writing Java/Scala code. Confluent for Apache Flink provides more powerful stream processing for complex event processing, windowed aggregations, and stateful computations.

Cluster Linking

Replicates data between Kafka clusters across regions, clouds, or between on-premises and cloud environments. Cluster Linking maintains consumer offsets and topic configurations, enabling disaster recovery, cloud migration, and multi-region architectures.

Stream Governance

Data lineage tracking, data quality rules, and business metadata tagging for streaming data. Stream Governance provides visibility into data flows — who produces data, who consumes it, and how it transforms through the pipeline.

Ideal Use Cases

Real-Time Data Integration

The primary use case: connecting systems in real time. Database changes (CDC from PostgreSQL, MySQL) flow through Kafka to data warehouses (Snowflake, BigQuery), search engines (Elasticsearch), and caches (Redis) with sub-second latency. Confluent's managed connectors handle this without custom code.

Event-Driven Microservices

Organizations building microservices architectures use Kafka as the event backbone — services communicate through events rather than synchronous API calls. Confluent provides the managed infrastructure, schema governance, and monitoring that production microservices require.

Real-Time Analytics Pipelines

Data teams building real-time analytics use Confluent to stream events from applications through processing (ksqlDB/Flink) to analytics platforms (Pinot, Druid, ClickHouse). This enables dashboards and alerts that reflect data from seconds ago, not hours.

Hybrid and Multi-Cloud Data Streaming

Enterprises with data across on-premises systems and multiple clouds use Cluster Linking to replicate data between environments, enabling cloud migration, disaster recovery, and multi-region data availability.

Pricing and Licensing

Confluent uses pay-as-you-go pricing:

Self-managed Confluent Platform pricing is per-broker licensing (contact sales). For comparison: Amazon MSK (managed Kafka) starts at $0.21/broker-hour (~$150/month minimum), Redpanda Cloud starts at $0.115/partition-hour, and self-managed Kafka is free but costs $1,000–$5,000/month in infrastructure for a production cluster.

Pros and Cons

Pros

• Built by Kafka's creators — deepest Kafka expertise; features and fixes reach Confluent before open-source Kafka
• 120+ managed connectors — pre-built integrations eliminate custom connector development; fully managed on Confluent's infrastructure
• Serverless Kafka — no cluster management, automatic scaling, pay-per-use pricing; removes Kafka's biggest operational burden
• Schema Registry — enforces data contracts between producers and consumers; prevents breaking changes in streaming pipelines
• Multi-cloud — available on AWS, GCP, and Azure with Cluster Linking for cross-cloud replication
• $400/month free credit — generous for development and small production workloads

Cons

• Expensive at scale — high-throughput production workloads can cost $5,000–$50,000+/month; significantly more than self-managed Kafka
• Kafka complexity remains — Confluent simplifies operations but the conceptual complexity of Kafka (partitions, consumer groups, offsets, rebalancing) still requires expertise
• Vendor lock-in risk — Schema Registry, ksqlDB, and Cluster Linking are Confluent-specific; migrating to vanilla Kafka or alternatives requires rework
• ksqlDB limitations — SQL-based stream processing is convenient but less powerful than Flink or custom Kafka Streams applications for complex logic
• Pricing complexity — pay-per-use across partitions, storage, networking, connectors, and processing makes cost prediction difficult

Getting Started

Getting started with Confluent is straightforward. Visit the official website to create a free account or download the application. The onboarding process typically takes under 5 minutes, and most users can be productive within their first session. For teams evaluating Confluent against alternatives, we recommend a 2-week trial period to assess whether the feature set and user experience align with your specific workflow requirements. Documentation and community resources are available to help with initial setup and configuration.

Alternatives and How It Compares

Amazon MSK (Managed Kafka)

Amazon MSK ($0.21/broker-hour) provides managed Kafka on AWS with less operational overhead than self-managed but fewer features than Confluent. MSK lacks managed connectors, Schema Registry, and ksqlDB. MSK for AWS-native teams wanting basic managed Kafka; Confluent for full-featured streaming platform.

Redpanda

Redpanda is a Kafka-compatible streaming platform written in C++ (no JVM) with simpler operations and lower latency. Redpanda Cloud provides managed hosting. Redpanda is simpler and faster for pure streaming; Confluent has a richer ecosystem (connectors, Schema Registry, stream processing).

Apache Pulsar

Pulsar is an alternative distributed messaging system with built-in multi-tenancy, geo-replication, and tiered storage. Pulsar has architectural advantages for multi-tenant deployments; Kafka/Confluent has a larger ecosystem and more adoption.

AWS Kinesis

Kinesis is AWS's native streaming service — simpler than Kafka but less flexible. Kinesis is easier for basic streaming on AWS; Confluent/Kafka is more powerful for complex streaming architectures and multi-cloud deployments.