Overview:

In the evolving landscape of distributed streaming platforms, Kafka has become the foundation of real-time data processing. As organizations opt for multiple deployment options, choosing Confluent Kafka, AWS MSK, and on-premises Kafka becomes important. This blog aims to provide an overview and comparison of these three Kafka deployment methods to help readers decide based on their needs.

Here's a list of key bullet points to consider when comparing Confluent Kafka, AWS MSK, and on-premise Kafka:

1. Deployment and Management
2. Communication
3. Scalability
4. Performance
5. Cost Considerations
6. Security

So, let’s go through all the parameters one by one.

Deployment and Management: 

Deployment and management refer to the processes involved in setting up, configuring, and overseeing the operation of a Kafka infrastructure. Each deployment option—Confluent Kafka, AWS MSK, and On-Premise Kafka—has its own approach to these aspects.

1. Deployment:

-> Confluent Kafka

• Confluent Kafka deployment involves setting up the Confluent Platform, which extends the open-source Kafka with additional tools.
• Users must install and configure Confluent components, such as Confluent Server, Connect, and Control Center.
• Deployment may vary based on whether it's on-premise or in the cloud using Confluent Cloud.

-> AWS MSK

• AWS MSK streamlines deployment as it is a fully managed service.
• Users create an MSK cluster through the AWS Management Console or API, specifying configurations like instance types, storage, and networking.
• AWS handles the underlying infrastructure, automating tasks like scaling, patching, and maintenance.

-> On-Premise Kafka:

• On-premise deployment requires manual setup and configuration of Kafka brokers, Zookeeper, and other components.
• Organizations must provision and maintain the necessary hardware and networking infrastructure.
• Deployment may involve high availability, fault tolerance, and scalability considerations based on organizational needs.

2. Management:

-> Confluent Kafka:

• Confluent provides additional management tools beyond what's available in open-source Kafka, including Confluent Control Center for monitoring and Confluent Hub for extensions.
• Users have control over configurations and can leverage Confluent's tools for streamlining tasks like data integration and schema management.

-> AWS MSK:

• AWS MSK offers a managed environment where AWS takes care of routine management tasks.
• AWS Console and APIs provide tools for monitoring, scaling, and configuring MSK clusters.
• AWS handles maintenance tasks such as applying patches and updates to the Kafka software.

-> On-Premise Kafka:

• On-premise management involves manual oversight of the entire Kafka infrastructure.
• Organizations have full control but must handle tasks like software updates, monitoring configurations, and addressing any issues that arise.
• Management may require coordination between IT and operations teams to ensure smooth operation.

Communication

There are three main components of communication protocols, network configurations, and inter-cluster communication, so let’s look at them one by one.

1. Protocols

-> Confluent Kafka:

• Utilizes standard Kafka protocols such as TCP for communication between Kafka brokers and clients.
• Confluent components may communicate using REST APIs and other protocols specific to Confluent Platform extensions.

-> AWS MSK:

• Relies on standard Kafka protocols for communication between clients and brokers.
• Also employs protocols like TLS/SSL for secure communication.

-> On-Premise Kafka:

• Standard Kafka protocols are used for communication between components, including TCP for broker-client communication.
• Specific protocols may vary based on the organization's network configuration.

2. Network Configuration

-> Confluent Kafka:

• Requires network configuration for Kafka brokers and other Confluent components.
• Configuration may include specifying listener ports, security settings, and inter-component communication settings.

-> AWS MSK:

• Network configuration involves setting up Virtual Private Cloud (VPC) settings, security groups, and subnet configurations.
• AWS MSK integrates with AWS networking services, allowing organizations to define network parameters.

-> On-Premise Kafka:

• Network configuration includes defining IP addresses, ports, and firewall rules for Kafka brokers.
• Organizations have full control over the on-premise network infrastructure, allowing for custom configurations.

3. Inter-Cluster Communication:

-> Confluent Kafka:

• Confluent components within the same cluster communicate using Kafka protocols.
• Communication between Confluent Kafka clusters or with external systems may involve additional configurations.

-> AWS MSK:

• AWS MSK clusters can communicate with each other within the same VPC or across different VPCs using standard Kafka protocols.
• AWS networking services facilitate secure and efficient inter-cluster communication.

-> On-Premise Kafka:

• Inter-cluster communication on-premise involves configuring network settings to enable communication between Kafka clusters.
• Organizations have full control over the network architecture for inter-cluster communication.

Scaling

Scalability is crucial for ensuring that Kafka deployments can handle varying workloads and grow as demands increase. Whether through vertical or horizontal scaling, the goal is to maintain performance, reliability, and efficiency in the face of changing requirements. Each deployment option—Confluent Kafka, AWS MSK, and on-premise Kafka—provides mechanisms for achieving scalability, with differences in how scaling is implemented and managed.

-> Confluent Kafka:

• Horizontal scaling is a fundamental feature of Kafka, allowing for adding more broker nodes to a Kafka cluster to handle increased message throughput.
• Kafka's partitioning mechanism allows data to be distributed across multiple broker nodes, enabling parallel processing and improved scalability.
• Scaling can be achieved dynamically by adding or removing broker nodes, providing flexibility in adapting to varying workloads.

-> AWS MSK:

• AWS MSK leverages horizontal scaling by allowing users to adjust the number of broker nodes in a cluster based on demand.
• The managed service handles the underlying infrastructure and scaling tasks automatically, simplifying the process for users.
• Scaling with AWS MSK is designed to be seamless, with the service managing the distribution of partitions across broker nodes.

-> On-Premise Kafka:

• Scaling Kafka on-premise involves manually adding or removing broker nodes based on the organization's infrastructure and capacity planning.
• Organizations need to consider factors such as hardware limitations, network configurations, and load balancing when scaling on-premise Kafka.

Performance

Performance in Kafka, whether it's Confluent Kafka, AWS MSK (managed streaming for Kafka), or on-premise Kafka, is a critical aspect that directly impacts the throughput, latency, and efficiency of the system. Here's a brief overview of performance considerations for each:

-> Confluent Kafka

• Enhancements and Extensions: Confluent Kafka builds upon the open-source Kafka with additional tools and features, potentially providing optimizations and performance enhancements. This may include tools for monitoring, data integration, and schema management.
• Customization: Organizations using Confluent Kafka have the flexibility to customize configurations and performance settings based on their specific use cases and requirements.
• Scalability: Confluent Kafka inherits Kafka's fundamental scalability features, allowing for horizontal scaling by adding more broker nodes to a cluster.

-> AWS MSK

• Managed Environment: AWS MSK is a fully managed service, meaning AWS takes care of many operational aspects, including patches, updates, and scaling. This managed environment can contribute to a more streamlined and optimized performance.
• Scalability: AWS MSK allows for horizontal scaling by adjusting the number of broker nodes in a cluster dynamically. This elasticity contributes to the efficient handling of varying workloads.
• Integration with AWS Services: Integration with other AWS services can enhance performance in specific scenarios, such as leveraging high-performance storage solutions or integrating with AWS networking services.

-> On-Premise Kafka

• Control and Customization: On-premise Kafka deployments provide organizations with complete control over the infrastructure, allowing for fine-tuning and customization to meet specific performance requirements.
• Scalability Challenges: Scaling on-premise Kafka may present challenges related to manual provisioning of hardware, potential limitations in scalability due to physical constraints, and increased complexity in managing a distributed system.
• Hardware Considerations: Performance is closely tied to the quality of hardware chosen for on-premise deployment. Organizations must invest in suitable hardware to meet performance expectations.

Cost Considerations

Cost considerations for Kafka deployments involve evaluating the direct and indirect expenses associated with setting up, managing, and maintaining a Kafka infrastructure. Kafka cost considerations span licensing, infrastructure, scalability, operations, data transfer, and ongoing support. The choice between Confluent Kafka, AWS MSK, or on-premise Kafka should be made by evaluating these costs in alignment with the organization's budget, requirements, and preferences.

Here's a brief overview of key cost considerations:

1. Deployment Costs

-> Confluent Kafka:

• Involves licensing costs for Confluent Platform, which provides additional tools and features beyond open-source Kafka.
• Infrastructure costs for hosting Confluent Kafka, whether on-premise or in the cloud.

-> AWS MSK:

• Pay-as-you-go pricing model for AWS MSK, where users pay for the resources consumed by the Kafka cluster.
• Costs include AWS MSK service charges, as well as associated AWS resource costs such as EC2 instances, storage, and data transfer.

-> On-Premise Kafka:

• Upfront costs for hardware, networking equipment, and software licenses.
• Ongoing operational costs, including maintenance, power, cooling, and any required hardware upgrades.

2. Scalability Costs

-> Confluent Kafka:

• Scaling Confluent Kafka may involve additional licensing costs if more resources are needed.
• Infrastructure costs increase with the addition of more nodes or resources.

-> AWS MSK:

• Scaling AWS MSK is dynamic, and users are billed based on the resources consumed during scaling events.
• Costs may increase with additional broker nodes and associated AWS resources.

-> On-Premise Kafka:

• Scaling on-premise Kafka requires manual provisioning of additional hardware, incurring upfront and ongoing costs.
• Organizations must consider the total cost of ownership (TCO) when planning for scalability.

3. Operational Costs

-> Confluent Kafka:

• Operational costs include manpower for managing and monitoring Confluent Kafka.
• Costs associated with maintaining Confluent extensions and tools.

-> AWS MSK:

• AWS MSK is a managed service, reducing the operational burden on the user.
• Operational costs may include AWS support charges and personnel for configuring and monitoring the Kafka environment.

-> On-Premise Kafka:

• Organizations bear full operational responsibility, incurring staffing, maintenance, monitoring tools, and ongoing support costs.

4. Data Transfer Costs:

-> Confluent Kafka:

• Costs associated with data transfer depend on the chosen deployment model (e.g., cloud-based deployments may have data transfer costs).

-> AWS MSK:

• Data transfer costs may apply for communication between AWS MSK clusters and other AWS services.

-> On-Premise Kafka:

• Data transfer costs may be associated with network usage, especially if there are multiple geographically distributed Kafka clusters.

Check the below images of pricing figures for Confluent Kafka and AWS MSK side by side.

Security

Security involves implementing measures to protect data, ensure confidentiality, integrity, and availability, and mitigate risks associated with unauthorized access or data breaches. Here's a brief overview of key security considerations for Kafka deployments:

1. Authentication:

-> Confluent Kafka:

• Supports various authentication mechanisms, including SSL/TLS for encrypted communication and SASL (Simple Authentication and Security Layer) for user authentication.
• Role-based access control (RBAC) allows administrators to define and manage user roles and permissions.

-> AWS MSK:

• Integrates with AWS Identity and Access Management (IAM) for user authentication and authorization.
• IAM policies control access to AWS MSK resources and actions.

-> On-Premise Kafka:

• Authentication mechanisms depend on the chosen Kafka distribution, but SSL/TLS and SASL are common.
• LDAP or other external authentication systems may be integrated for user management.

2. Authorization:

-> Confluent Kafka:

• RBAC allows fine-grained control over what users and clients can do within the Kafka environment.
• Access Control Lists (ACLs) can be used to restrict access at the topic level.

-> AWS MSK:

• IAM policies define permissions for accessing and managing AWS MSK resources.
• Fine-grained access control can be enforced using IAM roles and policies.

-> On-Premise Kafka:

• ACLs are typically used to specify which users or applications have access to specific Kafka topics or resources.
• Access controls depend on the Kafka distribution and configuration.

3. Encryption:

-> Confluent Kafka:

• Supports encryption in transit using SSL/TLS for secure communication between clients and brokers.
• Optionally supports encryption at rest for data stored on disks.

-> AWS MSK:

• Encrypts data in transit using SSL/TLS for communication between clients and brokers.
• Provides options for encrypting data at rest using AWS Key Management Service (KMS).

-> On-Premise Kafka:

• Encryption configurations depend on the Kafka distribution and may involve configuring SSL/TLS for secure communication and encryption at rest using platform-specific tools.

Here are some key points to evaluate AWS MSK, Confluent Kafka, and on-premise Kafka, along with their advantages and disadvantages.

Conclusion

• The best approach is to evaluate the level of Kafka expertise in-house and the consistency of your workload.
• On one side of the spectrum, with less expertise and uncertain workloads, Confluent’s breadth of features and support comes out ahead.
• On the other side, with experienced Kafka engineers and a very predictable workload, you can save money with MSK.
• For specific requirements and full control, you can go with on-premise Kafka.