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Kafka is an open-source distributed streaming platform for high-throughput and fault-tolerant real-time data streaming in large-scale systems. It can integrate with a wide range of data sources and sinks, which include databases, message queues, big data processing frameworks like Apache Spark and Apache Flink, and many more. Kafka has grown in popularity due to its wide use for log aggregation, where it captures logs from different services and systems to a central repository for analysis, monitoring, improving visibility, and troubleshooting.
Kafka is available through Apache Kafka, an open-source version, or Confluent Kafka which offers an enterprise-grade distribution of Apache Kafka. Most people are aware of these two tools but they don’t know the difference between the two solutions or they assume that they’re similar if not identical offerings. So, to help you gain a better understanding of Apache Kafka and Confluent Kafka, in this article, we will outline the main similarities and differences between the two tools.
Contents
What is Apache Kafka?
Apache Kafka is an open-source distributed streaming platform for building real-time data pipelines and streaming applications. Developed initially at LinkedIn, Kafka is currently maintained by the Apache Software Foundation. It has grown in popularity due to its capability to handle high throughput and low-latency data streaming.
What is Confluent Kafka?
Confluent Kafka is a robust enterprise distribution of Apache Kafka, provided by Confluent, founded by the creators of Kafka. While Apache Kafka is an open-source, distributed event streaming platform only, Confluent Kafka extended the capability of core Kafka with additional features, tools, and services needed to enable enterprise use cases.
Confluent Kafka vs Apache Kafka: Similarities
Real-Time Data Processing
First among the major similarities is their support for real-time data processing. Both come with Kafka Streams, a powerful library that enables developers to build applications designed for processing and analyzing streams of data in real-time. This supports complex event processing, stream transformation, and real-time analytics directly inside a Kafka cluster.
Integrations
Both Confluent Kafka and Apache Kafka integrate well with several sources and sinks that cover all use cases, from log aggregation and real-time monitoring to event-driven architectures and microservices. They also offer similar producer and consumer APIs that enable a developer to implement custom data streaming and processing logic.
Architecture
Both Confluent Kafka and Apache Kafka share the architectural foundation of a distributed log in which streams of data are organized by topics. Data gets published to these topics by producers and consumed by subscribing entities. With this architecture, replication provides durability and availability, resulting in fault tolerance for storing and fetching operations of data. Both platforms allow horizontal scaling by adding brokers into the cluster to handle huge volumes of data.
Deployment
Regarding deployment, both Confluent Kafka and Apache Kafka can be deployed on-premises or in the cloud, hence offering flexibility to organizations in terms of infrastructure and scalability. They both support being run in containerized environments like Kubernetes, hence making them highly flexible to use and manage in modern DevOps practices.
Confluent Kafka vs Apache Kafka: Differences
Features and Enhancements
Confluent Kafka provides a number of enterprise-grade features not found in open-source Apache Kafka. Most notable is the Confluent Schema Registry, which provides for centralized management and validation of schemas against Kafka topics. This guarantees data compatibility and quality, with no schema conflicts in data pipelines. It also provides several pre-built connectors from Confluent Connect that can integrate easily with different data sources and sinks. These connectors reduce the time and effort in connecting Kafka to different databases, cloud services, or other messaging systems, improving the integration feature of the platform.
Managed Services and Support
Confluent provides the fully managed Confluent Cloud, which is a cloud-native service for running Kafka as a service, this removes the operational overhead associated with running Kafka clusters. By using a managed service, you can ensure high availability, scaling, and security concerns. Confluent also provides automated upgrades and maintenance, which frees organizations from the intricacies of managing clusters.
On top of these managed services, Confluent offers professional support, consulting, and training. This support can be key for enterprises that want a reliable hand to help them out and to get extra value from their Kafka deployments. Apache Kafka is an open-source project, so it relies on community support.
Multi-Region and Disaster Recovery
Confluent Kafka supports multiple-region deployment and other more complex configurations focused on disaster recovery. This delivers business continuity and data resilience in case of a regional failure or other catastrophic events. While Apache Kafka might be configured for multiple-region deployment, Confluent Kafka simplifies this process and strongly supports data integrity and availability in multiple regions.
Confluent Kafka: Pros and Cons
Pros
- Enhanced Features: Confluent Kafka offers additional features on top of standard Kafka, including KSQL, schema registry, and pre-built connectors.
- Advanced Security Features: Includes role-based access control (RBAC), audit logs, and enhanced authentication and authorization mechanisms, ensuring compliance and data protection.
- Enterprise Support and Services: Access to professional support, consulting, and training, which can be critical for enterprise deployments.
Cons
- Higher Costs: The additional features and managed services come at a premium, which can be a significant expense for smaller organizations or startups.
- Potential Dependency: Relying on Confluent’s proprietary features and services may lead to vendor lock-in, making it difficult to switch to another provider or the open-source version.
Apache Kafka: Pros and Cons
Pros
- Free and Open Source: Apache Kafka is free to use, which makes it an attractive option for startups and small organizations with limited budgets.
- Customizable: Being open-source, it can be customized and extended to meet specific requirements without any restrictions imposed by a vendor.
- Active Community: A large, active community contributes to continuous improvements, bug fixes, and a wealth of shared knowledge and resources.
Cons
- Operational Complexity: Managing and monitoring Kafka clusters can be complex and resource-intensive, requiring significant expertise and effort.
- Basic Security: While Kafka provides basic security features, it lacks the advanced security capabilities of Confluent Kafka, which might be necessary for enterprise-level applications.
Conclusion
Confluent Kafka is great for enterpises needing advanced features, better security, and professional support while lowering the effort needed for management and integration tasks. However, it is a bit more costly and can introduce vendor lock-in. Apache Kafka becomes the right choice for companies that have advanced technological potential to manage and customize a deployment—mostly in the case when they are trying to find some cost-effective solution with high flexibility and community support. However, this comes with increased effort needed for management, security, and integration.
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