⚡ Apache Kafka Basics

📖 What is Kafka?

Apache Kafka is a distributed event streaming platform designed for high-throughput, fault-tolerant, and real-time data pipelines. It is widely used for building event-driven architectures, data streaming, and real-time analytics.

🔑 Core Concepts

• Topic: A logical channel where messages (events) are published.
• Partition: A subset of a topic that allows parallelism and scalability.
• Producer: Application that sends messages to Kafka topics.
• Consumer: Application that reads messages from Kafka topics.
• Broker: Kafka server that stores and serves messages.
• Consumer Group: A group of consumers that share the work of consuming messages from a topic.
• Zookeeper (legacy): Used for cluster coordination (being replaced by Kafka’s internal quorum controller).

🛠 Example in .NET Core

Using the Confluent.Kafka NuGet package:

// Install-Package Confluent.Kafka

using Confluent.Kafka;
using System;
using System.Threading.Tasks;

public class KafkaExample
{
    public static async Task Main()
    {
        var config = new ProducerConfig { BootstrapServers = "localhost:9092" };

        // Producer
        using (var producer = new ProducerBuilder<Null, string>(config).Build())
        {
            var dr = await producer.ProduceAsync("demo-topic", new Message<Null, string> { Value = "Hello Kafka!" });
            Console.WriteLine($"Delivered '{dr.Value}' to '{dr.TopicPartitionOffset}'");
        }

        // Consumer
        var consumerConfig = new ConsumerConfig
        {
            BootstrapServers = "localhost:9092",
            GroupId = "demo-group",
            AutoOffsetReset = AutoOffsetReset.Earliest
        };

        using (var consumer = new ConsumerBuilder<Ignore, string>(consumerConfig).Build())
        {
            consumer.Subscribe("demo-topic");
            var cr = consumer.Consume();
            Console.WriteLine($"Consumed message '{cr.Message.Value}' at: '{cr.TopicPartitionOffset}'.");
        }
    }
}
    

✅ Advantages

• High throughput and low latency.
• Scalable horizontally with partitions.
• Durable and fault-tolerant (data replicated across brokers).
• Supports real-time and batch processing.
• Integrates well with big data and streaming ecosystems (Spark, Flink, etc.).

⚠️ Disadvantages

• Operational complexity (cluster setup, monitoring, scaling).
• Requires careful schema management to avoid data issues.
• Not ideal for small, simple messaging needs (overkill).
• Eventual consistency (consumers may see data at slightly different times).

🧭 Best Practices

• Use schema registry to enforce data contracts.
• Design idempotent producers and idempotent consumers.
• Partition topics wisely to balance load.
• Monitor lag in consumer groups.
• Secure Kafka with TLS, SASL, and ACLs.

🔒 Precautions

• Plan for data retention policies (avoid unbounded storage).
• Handle backpressure when consumers are slower than producers.
• Test failover scenarios (broker crashes, network partitions).
• Ensure proper offset management to avoid data loss or duplication.

🎯 Summary

Apache Kafka is a powerful event streaming platform that enables real-time data pipelines and event-driven architectures. With proper design, monitoring, and security, it can handle enterprise-scale workloads efficiently.

⚡ Kafka vs RabbitMQ vs Azure Service Bus

📊 Comparison Table

Aspect	Kafka	RabbitMQ	Azure Service Bus
Type	Distributed event streaming platform	Message broker (AMQP, MQTT, STOMP)	Fully managed enterprise messaging (Azure PaaS)
Use Cases	Real-time analytics, event sourcing, log aggregation	Microservices communication, IoT, work queues	Enterprise workflows, financial transactions, cloud-native apps
Message Model	Publish/subscribe with durable logs	Queues and exchanges (direct, fanout, topic)	Queues and topics with subscriptions
Scalability	Horizontal scaling with partitions	Clustering, but manual scaling	Automatic scaling built-in
Reliability	Data replicated across brokers	Requires HA setup and persistence	Geo-redundancy, dead-letter queues
Protocols	Kafka protocol (custom TCP)	AMQP, MQTT, STOMP, HTTP	AMQP 1.0 only
Hosting	Self-hosted or managed (Confluent Cloud, MSK)	Self-hosted (on-prem, Docker, Kubernetes)	Azure cloud only (fully managed)
Cost	Free (open-source), infra costs apply	Free (open-source), infra costs apply	Pay-as-you-go Azure pricing

🛠 .NET Core Examples

Kafka (Confluent.Kafka)

// Producer
var config = new ProducerConfig { BootstrapServers = "localhost:9092" };
using var producer = new ProducerBuilder<Null, string>(config).Build();
await producer.ProduceAsync("demo-topic", new Message<Null, string> { Value = "Hello Kafka!" });
    

RabbitMQ (RabbitMQ.Client)

var factory = new ConnectionFactory() { HostName = "localhost" };
using var connection = factory.CreateConnection();
using var channel = connection.CreateModel();
channel.QueueDeclare("demo-queue", false, false, false, null);
channel.BasicPublish("", "demo-queue", null, Encoding.UTF8.GetBytes("Hello RabbitMQ!"));
    

Azure Service Bus (Azure.Messaging.ServiceBus)

await using var client = new ServiceBusClient("<connection-string>");
ServiceBusSender sender = client.CreateSender("demo-queue");
await sender.SendMessageAsync(new ServiceBusMessage("Hello Azure Service Bus!"));
    

✅ Advantages

• Kafka: High throughput, durable logs, real-time streaming.
• RabbitMQ: Flexible protocols, simple microservice integration.
• Azure Service Bus: Fully managed, enterprise-grade reliability.

⚠️ Disadvantages

• Kafka: Complex to operate, overkill for small apps.
• RabbitMQ: Requires manual scaling and maintenance.
• Azure Service Bus: Vendor lock-in, cost at scale.

🧭 Best Practices

• Use idempotent consumers to avoid duplicate processing.
• Monitor queue/topic lag and set alerts.
• Secure connections with TLS and proper authentication.
• Define dead-letter queues for failed messages.

🎯 Summary

Kafka is best for real-time streaming and analytics. RabbitMQ is ideal for microservices and IoT messaging. Azure Service Bus is perfect for enterprise-grade, cloud-native workflows. The right choice depends on whether you prioritize streaming performance, protocol flexibility, or managed reliability.