An enterprise-grade distribution of Apache Kafka® that is available on-premises as self-managed software, complete with built-in security, stream processing, and governance tooling.
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Store and retrieve Avro®, JSON Schema, and Protobuf schemas using a RESTful metadata interface.
Produce and consume Kafka messages efficiently using official Confluent clients for Java, librdkafka, and derived clients.
Stream data between Kafka and external sources or sinks using prebuilt connectors.
Manage and monitor both self-managed Confluent Platform clusters and fully managed Confluent Cloud clusters from a single, centralized interface.
Run complex, stateful, and low-latency streaming applications with self-managed Apache Flink®
Administer your streaming service, including topics, clusters, schemas, connectors, security, and billing, directly from your terminal.
Automate configuration and deployment of Confluent Platform on multiple hosts.
Deploy, scale, and manage Confluent Platform as a cloud-native system on Kubernetes.
Build event-driven applications and microservices using the native Kafka Streams library.
Programmatically control Kafka using dedicated APIs for Producer, Consumer, Streams, Connect, and Admin operations.
Protect your mission-critical services and data end-to-end across all Confluent Platform components.
Run a self-managed distribution of Kafka on-premises or in any cloud environment
Deploy Kafka brokers enhanced with advanced security, resilience, and commercial features
Stream and store real-time data using the open-source distributed event engine at the core of Confluent
Manage cluster metadata natively without Apache ZooKeeper™ using built-in KRaft consensus
Create, configure, and manage topics, partitions, and records
Track consumer group membership and manage committed read offsets across topics
Offload historical topic data automatically to cost-effective object storage
Rebalance partitions automatically across brokers as cluster capacity scales up or down
Redistribute partitions to balance workload and disk usage evenly across brokers
Self-managed Flink for sophisticated, stateful streaming applications
Write streaming queries and pipelines in SQL on self-managed Flink
Build stateful stream processing applications directly in Java
Process and query streaming data in real time using SQL directly against Kafka topics
Stream data between Kafka and external systems using managed or self-managed infrastructure
Connect to databases, cloud storage, SaaS, and messaging systems using prebuilt connectors
Produce, consume, and manage Kafka resources over standard HTTP REST endpoints
Ingest MQTT message streams directly from IoT devices into Kafka without a standalone broker
Centralize, version, and enforce compatibility rules across Avro, JSON Schema, and Protobuf schemas
Enforce schema rules, metadata standards, and quality guarantees on topic data
Mirror topics across clusters and regions without deploying extra connector infrastructure
Span a single Kafka cluster across geographical regions for high availability and disaster recovery
Replicate topics, schema definitions, and cluster configurations across Kafka environments
Verify identities using SASL, mTLS, OAuth/OIDC, or SSO
Grant precise access permissions using role-based scopes across cluster resources
Define fine-grained allow rules per principal and resource
Encrypt client and broker network traffic over the wire using TLS
Encrypt sensitive fields before they leave the client
Record, monitor, and review authentication and authorization events for security compliance
Administer Confluent Cloud and Platform resources directly from your terminal
Monitor, manage, and inspect Confluent Platform clusters through a centralized web UI
Manage hybrid Confluent Cloud and Platform clusters from a single control plane
Deploy, scale, and manage Confluent Platform natively on Kubernetes using declarative operators
Automate the installation, configuration, and provisioning of Confluent Platform across multiple hosts using Ansible
Monitor cluster health and receive proactive alerts using Confluent Health+
Expose broker and client performance metrics using standard JMX endpoints
Publish cluster performance metrics directly to a dedicated Kafka topic for custom analysis
Track consumer lag to ensure applications process streaming data in real time
Configure, monitor, and manage Kafka connectors programmatically using REST endpoints
Develop and manage Kafka resources inside JetBrains IDEs
Manage topics, schemas, and connectors directly from Visual Studio Code
Produce, consume, and inspect Kafka topics directly from the command line using kcat
Produce and consume Kafka events in native Java applications
Build high-performance C/C++ producers and consumers or power derived language libraries
Produce and consume Kafka events using native Python code
Build fast, concurrent Kafka producers and consumers in Go
Integrate Kafka messaging directly into .NET and C# applications
Produce and consume Kafka events in Node.js and JavaScript applications
