Confluent Documentation
Confluent Documentation
Find the guides, samples, tutorials, API, and CLI reference to get started with the streaming data platform based on Apache Kafka®.
Development
Manage Confluent Platform and Cloud using the command-line interface.
Use API references to manage your deployment or integrate Confluent into your product.
Produce and consume messages through Apache Kafka using official Confluent clients for Java, along with librdkafka and derived clients.
Build streaming data pipelines and applications with the open-source distributed data streaming engine that thousands of companies use to power operational and analytics use cases.
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Build real-time data architectures
Integrate your data ecosystem
Transform, analyze, and act on real-time data
Get started
This quick start guide will help you begin your data streaming journey by showing you how to create a cluster, add a topic, and produce data.
This quick start will help you get up and running locally with Confluent Platform and its main components using Docker containers.
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Core streaming platforms
Deploy and scale a fully managed, elastic cloud Kafka service built on the Kora engine
Deploy a self-managed enterprise distribution of Apache Kafka for on-premises or cloud
Run an enhanced Kafka broker with Confluent enterprise and security features
Stream and store real-time data using the open-source distributed event engine at the core of Confluent
Manage cluster metadata with built-in consensus that removes the Apache ZooKeeper™ dependency
Run stateless, S3-backed Kafka workloads with zero disk footprint on brokers
Clusters and storage
Provision clusters across flexible tiers tailored to your scaling, isolation, and pricing requirements
Create, configure, and manage topics, partitions, and records
Track consumer group membership and committed read offsets
Offload older topic data to cheaper object storage automatically
Automatically rebalance partitions as brokers are added or removed
Redistribute partitions to even out load across brokers
Stream processing
Execute stateful, low-latency stream processing workloads on a fully managed Flink service
Run self-managed Flink for complex, stateful streaming applications
Write and deploy real-time streaming queries and data pipelines using standard ANSI SQL
Build stateful stream processing applications directly in Java
Process and query streaming data in real time using SQL directly against Kafka topics
Data integration
Stream data between Kafka and external systems using managed or self-managed infrastructure
Integrate instantly with databases, storage, SaaS applications, and messaging systems
Upload and run custom connector plugins directly within Confluent Cloud
Replicate topics and configurations between Kafka clusters
Produce, consume, and administer Kafka using an HTTP interface
Ingest MQTT messages from IoT devices into Kafka without a broker
Data governance
Centralize, version, and enforce schema compatibility across your data streams
Ensure data quality, maintain metadata catalogs, and trace lineage across all event streams
Visualize end-to-end data flow across topics, connectors, and streaming applications
Discover, search, and access event streams through a self-service portal
Enforce schema rules, metadata standards, and quality guarantees on topic data
Expose Kafka topics as Apache Iceberg™ or Delta Lake tables for open analytical processing
AI and machine learning
Power real-time, context-aware AI applications with built-in streaming intelligence features
Build event-driven AI agents that analyze and act on streaming data in real time
Get instant assistance, execute queries, and complete guided tasks using an in-product AI assistant
Detect sentiment, anomalies, and PII, or run forecasts directly within streaming SQL queries
Call remote AI models and generate vector embeddings directly from streaming SQL
Serve fresh, real-time context to AI models and downstream applications
Query external vector and text stores directly within streaming SQL pipelines
Expose Confluent data and actions to external AI tools via Model Context Protocol (MCP)
Multi-region and replication
Mirror topics across clusters and regions without deploying extra infrastructure
Span a single cluster across regions for high availability
Replicate topics and configurations between Kafka clusters
Security and access control
Authenticate and verify user and service identities using SSO, SAML, OAuth/OIDC, SASL, or mTLS
Grant precise access permissions using role-based scopes across specific resources
Define fine-grained allow rules per principal and resource
Restrict cluster access to authorized source IP address ranges
Encrypt all client and broker network traffic over the wire
Encrypt stored cluster data using your own customer-managed encryption keys
Encrypt sensitive fields on the client side before transmitting data to brokers
Track, log, and review authentication and access events for security compliance
Deploy FedRAMP-aligned event streaming for secure U.S. government workloads
Networking
Connect to clusters securely over public internet endpoints
Establish private, one-way network connections to clusters on AWS
Establish private network connectivity to clusters on Microsoft Azure
Establish private network connectivity to clusters on Google Cloud
Route private network traffic between your VPC and Confluent infrastructure
Configure custom DNS domain names and isolate network segments
Secure and govern private access to Confluent services
Management and operations
Administer Confluent Cloud and Platform resources directly from your terminal
Monitor and manage Confluent Platform clusters through a web UI
Manage Confluent Cloud resources, configurations, and settings through a web UI
Manage hybrid cloud and self-managed clusters from a single control plane
Deploy and manage Confluent Platform on Kubernetes using a dedicated operator
Automate Confluent Platform installation and configuration with Ansible
Provision and manage Confluent Cloud resources as code using Terraform
Provision and manage Confluent Cloud resources using Pulumi infrastructure as code
Track, allocate, and manage Confluent Cloud spend across teams and environments
Monitor and manage resource limits and service quotas across Confluent Cloud
Configure, route, and manage alerts and notifications for account events
Monitoring and observability
Monitor and alert on Confluent Platform health
Expose broker and client metrics over JMX
Publish Confluent Platform metrics to a Kafka topic
Monitor consumer lag and track processing delays across topics
Export cluster metrics and logs to external monitoring tools like Datadog and Prometheus
APIs and developer tools
Manage Confluent Cloud infrastructure programmatically over HTTP
Produce records and administer Kafka topics programmatically over REST
Configure, monitor, and manage Kafka connectors programmatically via REST
Query cluster health, performance, and resource metrics programmatically
Fetch Confluent metadata and resource details using GraphQL queries
Develop, test, and manage Kafka resources directly inside JetBrains IDEs
Manage topics, schemas, and connectors directly from Visual Studio Code
Produce and consume Kafka messages directly from the command line
Client libraries
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