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Apache Kafka
Distributed event streaming platform for high-throughput, scalable, and durable real-time data pipelines
By Apache Software Foundation
Event Streaming
Event Streaming
Real-Time Data
Data Pipelines
Product Overview
Apache Kafka is an open-source distributed event streaming platform designed to handle high-throughput, real-time data feeds. It allows producers to publish streams of records and consumers to read them in a fault-tolerant and scalable manner. Kafka is widely used for building data pipelines, streaming analytics, and event-driven applications.
Apache Kafka provides a robust, horizontally scalable infrastructure for real-time event streaming and processing. It supports persistent storage, multi-subscriber capabilities, and stream processing features that enable enterprises to react to data instantly. Its distributed architecture ensures data durability and availability, making it essential for modern data architectures spanning finance, telecommunications, retail, and technology sectors.
Headquarters and Est. In
Pittsburgh, United States — Est. 2011
No. of Employees
1001-5000
Customer Demography
Global
Customer Domains
Technology
Finance
Telecommunications
Retail
Healthcare
Use Case Deep Dive
Interactive analysis dashboard - explore detailed performance insights for key business scenarios
Event-Driven Microservices Architecture
Use Kafka as the backbone messaging system to decouple microservices and enable asynchronous communication.
Scalable Log Aggregation and Centralized Monitoring
Aggregate logs from multiple sources in real-time for monitoring and troubleshooting.
Multi-Data Center Replication and Disaster Recovery
Implement cross-site replication to ensure data availability and disaster recovery readiness.
IoT Telemetry Ingestion and Processing
Handle vast amounts of sensor and device telemetry data in real-time for actionable insights.
Real-Time Analytics Pipeline
Create streaming data pipelines that feed live analytics dashboards.
Log Compaction for Event Sourcing
Use Kafka compacted topics to retain the latest state changes for event-driven applications.
Multi-Tenant SaaS Platform Data Isolation
Support multiple organizations securely using Kafka multi-tenancy capabilities.
Real-Time Change Data Capture (CDC)
Stream database changes into Kafka to power real-time applications and analytics.
Use Case Details
Integration Details
Feature Details
Key Features
Explore the core capabilities that make Apache Kafka stand out.
High Throughput and Scalability
Handles millions of messages per second with distributed brokers supporting horizontal scaling.
Durable and Fault Tolerant Messaging
Ensures messages are durably stored and replicated across brokers to provide fault tolerance.
Publish-Subscribe Messaging Model
Supports decoupled communication between producers and multiple consumers via topics and partitions.
Stream Processing with Kafka Streams API
Enables real-time computation and transformation of event streams directly within Kafka.
Exactly Once Semantics
Provides strong guarantees for message processing to avoid duplicates in consuming applications.
Multi-Subscriber and Consumer Groups
Allows multiple independent applications to concurrently consume the same stream with load-balanced partitions.
Rich Ecosystem and Connectors
Extensive ecosystem with numerous connectors and integrations for seamless data movement.
Schema Registry Support
Manage data schemas centrally to control compatibility and evolution of message formats.
Low Latency Message Delivery
Delivers messages with millisecond latency to support realtime use cases.
Security Features
Supports encryption, authentication, and authorization to secure data streams.
Cross-Data Center Replication with MirrorMaker
Replicates Kafka topics across multiple geographic locations for disaster recovery and global data distribution.
Tiered Storage
Offloads older data to cheaper storage while keeping recent data on fast disks.
Kafka Streams Interactive Queries
Allows querying the state stores of stream processing applications in real-time.
Integration with Kubernetes and Cloud
Supports cloud-native deployments and runs seamlessly in containerized environments.
Flexible Retention Policies
Configurable message retention based on time or size per topic or partition.
Kafka Connect Framework
Facilitates scalable and fault-tolerant integration of Kafka with external systems.
Role-Based Access Control (RBAC)
Manages user permissions with granular topic and cluster level controls.
Log Compaction
Retains the latest value for each key within a topic, enabling stateful applications.
Backpressure Handling
Manages flow control between producers and brokers under load.
Time-Based and Size-Based Partitioning
Organizes data in topics for balanced load and efficient consumption.
Kafka REST Proxy
Enables HTTP access to Kafka clusters for producers and consumers.
Metrics and Monitoring
Exposes detailed metrics for broker and client performance monitoring.
Multi-Language Client Support
Provides client APIs in various popular programming languages.
Message Compression
Reduces network bandwidth and storage by compressing messages at producer side.
Contextual Integrations
Not just "integrates with" – here's the specific value each integration delivers:
Kafka Connect JDBC Source Connector
Delivers: Integrates relational databases by streaming change data capture into Kafka.
Confluent Schema Registry
Delivers: Manages and validates data schemas for Kafka topics to ensure data quality.
Elasticsearch Sink Connector
Delivers: Streams Kafka topic data into Elasticsearch for powerful search and analytics.
Prometheus Monitoring Integration
Delivers: Exposes Kafka metrics to Prometheus for monitoring and alerting.
Grafana Dashboards
Delivers: Visualizes Kafka metrics and business data in custom dashboards.
Confluent Control Center
Delivers: A monitoring and management system for Kafka clusters.
Resources
Latest insights, guides, and templates to accelerate your decisions.
Downloads
Downloads coming soon
Resources and templates will be available soon
Case Studies
LinkedIn: Scaling Apache Kafka
Netflix Real-Time Stream Processing
Platform Updates
Apache Kafka 3.5 Release Notes
Videos
Watch Apache Kafka in action.
Introduction to Apache Kafka
Kafka Architecture Explained
Pricing & Plans
Open Source
Free
Confluent Cloud
Usage-based
Confluent Enterprise
Custom pricing
Frequently Asked Questions
Common questions about Apache Kafka:
Apache Kafka is used for building real-time data pipelines and streaming applications. It enables high-throughput messaging and processing of event streams.
Yes, Apache Kafka is an open-source project maintained by the Apache Software Foundation. It is freely available for use and modification.
Kafka persists messages on disk and replicates them across multiple brokers. This replication ensures data is not lost in case of node failures.
Yes, Kafka is designed for horizontal scaling and handles millions of messages per second with low latency.
Kafka has official and community clients for Java, Python, Go, C++, .NET, and more. This allows integration across diverse software stacks.
Kafka Streams is a client library for building real-time stream processing applications directly on Kafka. It supports complex event transformations and aggregations.
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