Best Kafka alternatives in 2024 you must know: NATS, GlassFlow, Apache Pulsar, Amazon Kinesis, Amazon MSK, Confluent, Aiven, Redpanda, Google Pub/Sub, and RabbitMQ
Kafka Challenges
Kafka has long been a popular choice for handling real-time data with its exceptional performance, fault tolerance, and durability. However, its complexity in setup, configuration, and management can be a challenge for many new users (such as creating clusters, managing partitions, shards, and workers’ setup). Managing a Kafka on your own can be expensive, both in terms of infrastructure and operational costs. Self-managing Kafka presents several challenges, including:
- Determining which team or individual is responsible for Kafka DevOps operations.
- Configuring Kafka correctly.
- Deploying changes to Kubernetes, or other environments.
- Expanding Kafka topic storage capacity.
- Upgrading Kafka brokers effectively.
- Monitoring Kafka’s performance and health.
- Training developers on Kafka management and configuration.
- Deciding whether to implement a single cluster or multiple clusters for your organization.
Kafka users talk about taking months to implement Kafka-based data pipelines or they hate hiring people just to manage Kafka.
Here I leave some useful links for understanding the challenges of using Kafka:
- Top 10 Problems When Using Apache Kafka
- The 4 Key Challenges You’ll Face as Your Kafka Estate Grows
- Common Apache Kafka mistakes to avoid
Let's start first by discovering options without using any managed Kafka providers. On the bottom, you can also find a list of managed Kafka Providers. We’ll cover six notable Kafka alternatives: GlassFlow, Apache Pulsar, NATS, Amazon Kinesis, Google Pub/Sub, and RabbitMQ.
Kafka Alternatives Table
This table aims to give a comprehensive view of each tool’s features and strengths, helping you choose the best Kafka alternative for your real-time data processing needs. Read more information about each tool below:
1. GlassFlow: A Modern Kafka Alternative for Python
Overview
GlassFlow is a powerful data streaming platform designed to simplify real-time data processing and building real-time data pipelines. As a Kafka alternative, GlassFlow offers several advantages, especially for Python developers, Data Engineering, Data Scientists and Data Analysts:
Key Features
- Ease of Use: GlassFlow provides a user-friendly interface that simplifies the creation and management of data pipelines in a low-code environment. It eliminates much of the complexity associated with traditional Kafka setups like creating computing clusters or running JVM.
- End-to-end in Python: GlassFlow can be used out-of-the-box with any existing Python library (like Pandas, NumPy, Scikit Learn, Flask, TensorFlow, etc.) to connect to hundreds of data sources and use the entire ecosystem of data processing libraries. GlassFlow’s Python SDK allows developers to build and manage data pipelines with minimal effort.
- Serverless Architecture: GlassFlow operates in a serverless environment, reducing the need for infrastructure management and scaling concerns. This approach helps in focusing on developing and deploying data pipelines without the overhead of managing servers.
- Integration with Various Data Sources: GlassFlow supports integration with a wide range of data sources and sinks, including databases, message queues, and APIs, making it a versatile tool for diverse data streaming needs.
- Real-Time Transformation: GlassFlow excels in the real-time transformation of events so that applications can immediately react to new information.
Reasons to Choose GlassFlow
- Simplified Pipeline Management: GlassFlow’s intuitive interface and streamlined setup process make it easier to create and manage data pipelines without heavy reliance on external teams compared to Kafka where you need a dedicated Java software engineer or DevOps team.
- Cost-Effective: The serverless nature of GlassFlow can reduce costs related to infrastructure and operational management.
- Built-in message broker: Data Engineers can build pipelines without knowing how message brokers like Kafka work internally. Built-in message broker scales automatically and handles billions of events, ensuring your pipeline remains efficient regardless of the load.
Limitations
- Purely in Python: As a newer platform in Python, GlassFlow may not fit for Java-based development stack for stream processing.
2. Apache Pulsar
Overview
Apache Pulsar is an open-source distributed messaging platform originally developed by Yahoo! It provides a highly scalable solution for messaging and stream processing with robust durability and fault tolerance.
Key Features
- Multi-Tenancy: Supports multiple tenants for various teams and projects.
- Geo-Replication: Efficiently replicates messages across clusters and data centers.
- Tiered Storage: Moves older messages to long-term storage like Amazon S3.
- Scalability: Features a decoupled architecture for independent scaling of serving and storage layers.
Reasons to Choose Pulsar
- Built-In Geo-Replication: Easier setup for geo-replication compared to Kafka’s MirrorMaker.
- Native Multi-Tenancy: Suitable for organizations with multiple teams or departments.
Limitations
- Complex Architecture: More complex setup and management due to its two-layer system.
- Smaller Community: Less mature than Kafka, with a smaller community and fewer integrations.
3. NATS
Overview
NATS is an open-source, lightweight, high-performance messaging system known for its simplicity and ease of use. It is designed for cloud-native and IoT applications.
Key Features
- Simplicity: Minimalistic design for easy deployment and management.
- High Performance: Optimized for low-latency messaging and high throughput.
- Security: Includes TLS/SSL encryption and token-based authentication.
- Scalability: Supports clustering and auto-discovery of nodes.
Reasons to Choose NATS
- Ease of Deployment: Ideal for projects needing a simple and fast messaging system.
- High Performance: Suitable for applications requiring low-latency communication.
Limitations
- Advanced Features: Lacks features like message persistence and complex routing.
- Replication: No native support for data replication across clusters.
4. Amazon Kinesis
Overview
Amazon Kinesis is a fully managed real-time data streaming service by AWS, designed for large-scale data ingestion and processing.
Key Features
- Scalability: Handles real-time data streaming from numerous sources.
- Reliability: Replicates data across three AWS data centers for durability.
- AWS Integration: Integrates seamlessly with other AWS services.
Reasons to Choose Kinesis
- Fully Managed: Reduces the overhead of managing infrastructure.
- AWS Ecosystem: Simplifies integration with AWS services.
Limitations
- Cost: Can be expensive at scale compared to open-source alternatives.
- Vendor Lock-In: Tightly integrated with AWS, leading to potential lock-in.
5. Google Pub/Sub
Overview
Google Pub/Sub is a fully managed messaging service offered by Google Cloud Platform, designed for real-time messaging and event-driven systems.
Key Features
- Global Scalability: Supports high-throughput, real-time messaging.
- Google Cloud Integration: Integrates seamlessly with other Google Cloud services.
- Automatic Scaling: Handles traffic spikes and scales automatically.
- At-Least-Once Delivery: Ensures messages are delivered at least once.
Reasons to Choose Google Pub/Sub
- Fully Managed: Eliminates infrastructure management.
- Integration with Google Cloud: Ideal for projects using Google Cloud services.
Limitations
- Vendor Lock-In: Tightly integrated with Google Cloud, which may lead to vendor lock-in.
- Cost: Can become costly depending on usage and data volume.
6. RabbitMQ
Overview
RabbitMQ is an open-source message-broker software that implements the Advanced Message Queuing Protocol (AMQP). It supports various messaging patterns and is known for its reliability and flexibility.
Key Features
- Multiple Messaging Protocols: Supports AMQP, STOMP, MQTT, and more.
- Flexible Routing: Routes messages in complex ways to suit various use cases.
- Reliability: Offers features like persistence, delivery acknowledgments, and publisher confirms.
- Distributed Deployment: Can be deployed in distributed and federated configurations.
Reasons to Choose RabbitMQ
- Protocol Flexibility: Supports multiple messaging protocols beyond Kafka’s API.
- Versatile Routing: Suitable for scenarios requiring complex routing logic.
- Developer-Friendly: Known for its ease of setup, robust documentation, and large community.
Limitations
- Throughput Limitations: It may not handle very high throughput as effectively as Kafka.
- Scalability: Horizontal scalability and fault tolerance are weaker compared to Kafka. You can read more about the Difference Between Kafka and RabbitMQ.
Summary of Kafka Alternatives
Each Kafka alternative presents distinct advantages that cater to different requirements. GlassFlow, Apache Pulsar, NATS, Amazon Kinesis, Redpanda, Google Pub/Sub, and RabbitMQ offer varied features ranging from simplicity and ease of use to specific integrations and performance benefits. By evaluating these alternatives, you can find the best fit for your real-time data streaming needs, balancing factors like scalability, performance, and operational complexity.
Managed Kafka provider alternatives
1. Amazon MSK
Amazon Managed Streaming for Apache Kafka (Amazon MSK) is a fully managed service that simplifies the use of Apache Kafka for streaming data.
Key Features:
- Compatibility: Works with existing Kafka applications, tools, and plugins.
- Automatic Recovery: Detects and recovers from common cluster failures with minimal disruption.
- Managed Service: Handles cluster management tasks such as creation, updates, and deletions.
Who Is It For?
- Kafka Users: Suitable for organizations already using Kafka but seeking a managed service to simplify operations.
- Reliability Seekers: Good for those needing automatic failure recovery and minimal downtime.
- Existing Kafka Ecosystem: Ideal for users who want to continue using Kafka’s ecosystem with less overhead.
2. Confluent
Confluent Kafka enhances the Apache Kafka platform with additional enterprise features for real-time data processing.
Key Features:
- Advanced Capabilities: Includes features like multi-datacenter replication, schema management, and security enhancements.
- Pre-Built Connectors: Offers around 100 connectors for easy integration with various systems.
- Managed Service: Provides Confluent Cloud for a fully managed Kafka service with additional tools.
Who Is It For?
- Enterprise Users: Suitable for organizations needing advanced features and high-performance capabilities.
- Integration Needs: Ideal for those requiring numerous pre-built connectors and integrations.
- Managed Service Seekers: Best for users looking for a managed Kafka service with enhanced features.
3. Upstash
Upstash offers a serverless Kafka platform with a per-request pricing model, emphasizing simplicity and flexibility.
Key Features:
- Serverless Architecture: Eliminates the need for managing infrastructure with a pay-per-request model.
- API Compatibility: Fully compatible with Kafka APIs and provides a REST API for serverless integration.
- Scalability: Adapts easily to varying usage patterns with no charges during inactive periods.
Who Is It For?
- Developers Seeking Simplicity: Ideal for those who want to avoid infrastructure management.
- Serverless Enthusiasts: Suitable for projects benefiting from a serverless data streaming architecture.
- Cost-Sensitive Users: Best for users who prefer a pricing model that scales with actual usage.
4. Aiven for Apache Kafka
Aiven for Apache Kafka is a managed service that simplifies the deployment, management, and scaling of Apache Kafka clusters. It provides a user-friendly interface for managing Kafka clusters and integrates with various cloud providers.
Key Features
- Ease of Use: Simplifies Kafka management through an intuitive dashboard for setup, scaling, and monitoring.
- Flexible Deployment: Supports deployment across multiple cloud platforms, including AWS, Google Cloud, and Azure.
- Automatic Scaling: Handles scaling automatically based on usage and demand.
- Managed Infrastructure: Aiven takes care of all infrastructure and operational aspects, including updates and maintenance.
Who Is It For?
- Organizations Seeking Managed Solutions: Ideal for businesses looking to offload the complexities of managing Kafka clusters while still leveraging Kafka’s robust capabilities.
- Teams Without Dedicated DevOps Resources: Suitable for teams that lack extensive DevOps expertise and need a streamlined, user-friendly way to deploy and manage Kafka.
- Cross-Cloud Users: Perfect for organizations that require Kafka deployment across multiple cloud providers, offering flexibility and ease of integration with existing cloud infrastructure.
- Enterprises Focused on Cost Efficiency: Beneficial for enterprises aiming to optimize operational costs with a managed service that scales automatically according to usage.
5. Redpanda: Low-Latency Kafka Alternative
Redpanda is a Kafka-compatible streaming platform designed for low-latency, high-throughput applications.
Reasons to Choose
- No ZooKeeper: Simplified architecture without ZooKeeper reduces operational overhead and speeds up deployments.
- Ultra-Low Latency: Up to 6x faster than Kafka. Redpanda offers sub-millisecond latencies, making it ideal for mission-critical applications.
- Kafka-Compatible: Redpanda works with existing Kafka clients, making it easy to transition.
Challenges
- Newer platform: A smaller community compared to Kafka or Kinesis.
- Enterprise costs: Some features are locked behind enterprise pricing tiers.
6. IBM Event Streams: Enterprise-Grade Kafka
IBM Event Streams builds on Apache Kafka to offer a highly resilient, enterprise-grade solution.
Reasons to Choose
- Enterprise Security: Provides enterprise-grade security and compliance tools on top of Kafka.
- IBM Cloud Integration: Seamlessly integrates with IBM Watson, AI services, and databases.
- Resiliency: Advanced replication and failover features ensure minimal downtime.
Challenges
- Complexity: Kafka’s complexity persists, even with IBM’s managed offerings.
- Cost: IBM’s enterprise pricing can be prohibitive for small organizations.
Considerations for choosing a managed Apache Kafka provider
When selecting a managed Apache Kafka provider, it’s important to evaluate several factors to ensure the service meets your operational needs and expectations. Kafka should be the most dependable element in your data architecture, which is why it’s so crucial. A key objective for any managed service provider is to achieve 100% uptime. You should also consider whether the provider offers access to a dedicated engineer who understands your specific use case and business needs. Running Kafka in your environment provides greater control and security compared to third-party hosted options. Choose a provider that offers robust security measures including encryption, authorization, and authentication. Additionally, watch out for overprovisioning and fluctuating costs. Understand the cost model of your managed Kafka provider.
