Confluent Cloud
Confluent Cloud Documentation
A fully managed data streaming platform, available on AWS, Google Cloud, and Azure, with a cloud-native Apache Kafka® engine for elastic scaling, enterprise-grade security, stream processing, and governance.
View Confluent Cloud release notes
Products
Represent Kafka topics as open table formats like Apache Iceberg™ and Delta Lake.
Use AI functions, interfaces, and tools to build agentic workflows, query real-time data, manage Confluent Cloud resources, and build streaming applications.
Manage and monitor both self-managed Confluent Platform and fully managed Confluent Cloud clusters from a single, centralized interface using Unified Stream Manager.
Use connectors to stream data between Kafka and external systems.
Store and retrieve Avro®, JSON Schema, and Protobuf schemas in a centralized repository to enforce data quality, ensure compliance, and manage schema evolution.
Build and run complex, stateful, low-latency streaming applications at scale with Apache Flink.
Produce and consume Kafka messages quickly using official Confluent clients for Java, librdkafka, and derived libraries.
Control Kafka resources, security, and account settings using the Confluent command-line interface.
Use HashiCorp Terraform to deploy and manage your Confluent Cloud infrastructure.
Visualize event streams and data relationships to track data movement from source to destination.
Manage, configure, and automate your Confluent Cloud deployment using REST APIs.
Deploy a fully managed event streaming service built specifically for U.S. government workloads and developed by those who built Confluent Cloud.
Browse by feature
Core streaming platforms
Deploy and scale a fully managed, elastic cloud Kafka service built on the Kora engine
Stream and store real-time data using the open-source distributed event engine at the core of Confluent
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 data records
Track consumer group membership and manage committed read offsets
Stream processing
Execute stateful, low-latency stream processing workloads on a fully managed Flink service
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, cloud storage, SaaS applications, and messaging systems
Upload and run custom connector plugins directly within Confluent Cloud
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 seamlessly across clusters and regions without deploying extra infrastructure
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
Manage Confluent Cloud resources, configurations, and settings through a web UI
Manage hybrid cloud and self-managed clusters from a single control plane
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 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
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