Event-Driven Architecture with Kafka and Apache Pulsar

Event-Driven Architecture with Kafka and Apache Pulsar

Discover key differences and similarities between Kafka and Apache Pulsar in the blog.

Blog Overview: Integrating event mesh and AI like Kafka and Apache Pulsar can empower businesses with real-time processing and data intelligence. This blog compares Kafka and Apache Pulsar regarding performance, architecture, use cases, and offerings. Learn the best practices for implementing these event-driven architecture platforms.

In 2024, businesses will focus more on faster use and interpretation of data rather than just collecting it. They are turning to something called an "event mesh". Imagine this as a vast network like a highway for data. It allows companies from different parts of the digital ecosystem to communicate in real-time. It adds AI to this set-up, and it's like adding a brain. This brain can learn, make new decisions, and adapt to information. It can analyze spoken words, text, and videos and immediately take the best action. Hire developers skilled in Apache Kafka and Apache Pulsar today.


As of 2024, the data streaming landscape is vibrant and rapidly evolving, with increased demand for real-time data processing. Many platforms, cloud services, and frameworks have made the data streaming ecosystem seamless and efficient.

Existing solutions include Kafka, Apache Pulsar, frameworks like Apache Flink. Cloud services vendors offer managed kafka services like installation and scaling infrastructure. Competitive technologies include Redpanda and Warpstream. However, Kafka dominates the scene with 100,000 businesses using it worldwide.

What is Event-driven Architecture?

In event-driven architecture (EDA), different parts of software applications interact with each other called "events" when something important happens. Other parts "listen" to these events and react without being directly connected for updates. For example, picture a city where no one needs to call anybody directly to share news or tell things. Instead they send out a postcard and the news spreads. In this way, the event-driven applications can handle lots of activities and changes while staying flexible and well-organized.

What are the key components of event-driven architecture?

EDA is made for constantly changing environments. The key components of event driven architecture include:

1. Events: Imagine something interesting happens at the party - like someone dropping a glass. That incident is like an "event" in EDA. It's a piece of news that something has happened.

2. Event Producers (The Note Writers): These are like the guests who saw the glass drop. They decide to write a note about it to let others know. In EDA, these note writers are the "event producers" because they create a message about what they observed.

3. Event Consumers (The Note Readers): Now, imagine there are guests at the party interested in cleaning up messes. They read the note about the dropped glass and decide to act by cleaning it up. these helpful guests are like  "event consumers" because they read the message and then do something about it.

4. Event Broker (The Bulletin Board): To make sure note about events like the dropped glass get seen, there's a bulletin board in the middle of the party where people can pin their notes. This board is like the "event broker" or "event bus." It helps share the news without the note writers needing to find the right people themselves.

5. Event Channels (Different sections on the Bulletin Board): The bulletin board is organized into different types of news - like spills, lost items, or announcements. These sections make it easier for the right people to find the notes they care about. In EDA, these sections are like "event channels." 

6. Event Store (The Scrapbook): Imagine there is a scrapbook at the pary where every note ever written is pasted in. This scrapbook lets people look back at all the things that have happened. In EDA, this called an "event store" a place where all event notes are kept for record.

7. Event Processing (Deciding What to do About the Note): When someone reads a note and decides to do something-like cleaning the spill or finding a lost item- that's "event processing." They're taking the news and acting on it, which could involve simple or complicated actions.

8. External Systems (Neighbors Joining the Party): Lastly, imagine neighbors can also write notes or read the bulletin board if they're interested in the party happenings. They're like "external systems" that either get involved by writing their own notes or by reading and acting on notes from the party.  

What is Kafka?

Kafka was developed by Linkedin and then taken by Apache Software Foundation. Java and Scala are the main programming languages of Kafka. It is designed to handle real-time data feeds with low latency. Kafka works with a distributed commit log, which means messages can be stored in topics. These topics are divided and duplicated in Kafka cluster. It ensures fault tolerance and scalability.

It is widely used to develop real-time streaming data pipelines that communicate data between systems or applications, as well as for developing real-time streaming applications that transform or react to the streams of data.

What is Apache Pulsar?

Pulsar is a recent player in the messaging and streaming space, originally developed by Yahoo and now part of Apache Software Foundation. It is similar to kafka but has differences in architecture and features. It has a different serving layer and different storage layer, which allows for independent scaling and supports multi-tenancy. 


Building Event-Driven Architecture with Kafka:

EDA with Kafka is like setting up a modern, efficient communication system for your software application. Imagine software components as people in a company who need quick and accurate information without delay or waiting for anybody. This is how Kafka operates to make the entire process smooth.

Steps to Use Kafka in Event-Driven Architecture:

Step 1: Set Up Your Project: Create a new project in your favorite Java development environment. this is like setting up a new office in your company. 

Step 2: Add Dependencies:  Add some tools to your project, like you need office supplies for your new office. If you plan to use Maven or Gradle, include the Spring Boot starter and Spring Kafka. These tools help your software understand how to use Kafka. Know how to build microservices in Java with SpringBoot and SpringCloud.

Step 3: Configure Kafka Topics: Think of a Kafka topic like a bulletin board or a mail channel dedicated to a specific type of news. Set up a topic for your software components to know where to post and read their messages. You can do this with a simple configuration in your project.

Step 4: Create a Kafka Producer: A producer is like someone who has a news and shares it on a bulletin board. Create a service in your software that can send messages to your Kafka topic. This service uses Kafka tools to make sure the message gets to the right topic.

Step 5: Create a Kafka Consumer: A consumer is like someone who reads the news on the bulletin board. In your project set up another service that listens for new messages on your topic. Whenever a message is posted, this service will see it and can act on the information.

Why Use Kafka for Event-driven Microservices?

Here's why kafka is a top choice for building microservices:

High throughput and scalability: Kafka can handle millions of messages per second. It's what makes it ideal for building microservices, which need to high volumes of events in real-time. It can grow with your needs by adding more servers, handling more and more information smoothly without slowing down.

Fault tolerance and durability: Kafka makes sure that the same data is copied in several places, so if one part stops breaking the system doesn't break and no information gets lost. This is really important for systems where everything needs to work together without problems.

Decoupling of services: Kafka works like the main hub that separates the part of the system that create data from the parts that use this data. This setup lets teams working on different parts of the system witout messing with each other, making things fast and less complex.

Real-time processing: Kafka can handle data as it comes in, right away. This is important for systems that need to act fast, like spotting fraud, changing pieces on the go, or personalizing what content users see instantly. 

Ecosystems and Integrations: Kafka comes with a lot of tools and add-ons, making it easier to setup complex data flow and connect with many other types of systems and databases. It helps make system more powerful and flexible.

Event scoring and replayability: Kafka stores changes in the system as sources of events. This method lets system go back to past events or use them again for testing or flexing things. 

Kafka is great for building systems that can adapt and respond quickly, making sure everything runs smoothly and efficiently.

Building Event Driven Architecture with Apache Pulsar:

Pulsar facilitates communication between microservices through the use of events but it also offers some distinct advantage like built in multi-latency, geo-replication, and a light weight framework.

Steps to Use Apache Pulsar in Event Driven Architecture:

Step 1: Setting Up Apache Pulsar: You can run Pulsar locally by downloading from official Apache Pulsar website and following installation manual. You can also use docker alternatively to run Pulsar, which simplifies the setup process.

Step 2: Integrating Pulsar with Your Application: To use Pulsar with your application, you need to add the Pulsar client library. If you're using Maven, add dependency to your 'pom.xml' file.

Step 4: Creating a Consumer: Consumers receive messages from topics. This consumer connects to Pulsar, subscribes to "my-topic", and starts listening for messages. Each received message is printed to the console.

Step 5: Expanding your Architecture: As you become more familiar with Pulsar, you can explore its advanced feature like:

  • Multi-tenancy: Separating environments within the same Pulsar instance.
  • Geo-Replication: Replicating data across different geographical locations.
  • Functions: Processing streams of data with Pulsar Functions.

This process answers why Apache pulsar for Event-driven architecture is the solution for building scalable and resilient applications.

Why Use Apache Pulsar for Event-Driven Microservices?

Here's why Apache Pulsar is great choice for developing Event-Driven Microservices applications:

Built-in Multi Tenancy - Pulsar's architecture natively supports multi-tenancy, allowing different teams or applications to share the same pulsar instance, while keeping there data and configurations isolated. This is good for businesses looking to optimize resources and manage multiple projects efficiently.

Geo-replication: Pulsar provided out-of-the-box support for geo-replication. This feature enables data to replicate across multiple data centers or cloud regions, improving data availability and disaster recovery strategies. It's essential for global applications requiring data consistency across geographical locations.

Scalable storage layer: Unlike Kafka, which relies on file system storage, this design allows for independent scaling, of storage and compute resources, providing better utilization and performance scaling as your application grows. It's especially useful for handling variable workloads and large data volumes without compromising performance.

Client libraries for multiple languages: Pulsar provides client libraries for multiple programming languages, making it easy to integrate into different parts of your microservices architecture. This broad language support simplifies, development and reduces effort, required to connect various services. 

Apache Pulsar's unique features make it a powerful platform for building event-driven microservices.


Patterns of Event-Driven Architecture

Event-driven architecture (EDA) encompasses several patterns that facilitate efficient and effective communication and processing of events in software systems. These patterns include

  • Event Notification: A system sends out a simple signal or message about state changes.
  • Event-carried state transfer: It involves sending complete state change within the event, allowing consumers to update their state directly.
  • Event sourcing: Where state changes are stored as a sequence of events, enabling system to rebuild the state by replacing these events.
  • CQRS (Command Query Responsibility Segregation): It separates the writing of the data (command) from the reading of the data (query), often used in conjunction with event sourcing for improved scalability and performance.
  • Event Streaming: Where events are continuously processed in a stream, allowing for real-time data processing and analytics. Together, these patterns provide the backbone for building responsive, scalable loosely coupled systems in an event driven manner.

A Comparison Table - Kafka vs Pulsar 



Apache Pulsar

Storage Approach

Distributed commit log

Tiered storage

Message Consumption Model

Pull (long polling)



High (millions of messages per second)

Comparable to Kafka


Low (in the millisecond range)

Low (in the millisecond range)


Highly scalable

Highly scalable, independent data and serving layers scale


Configurable data retention

Configurable message retention, with offloading to long-term storage

Data Replication

Across multiple nodes for fault tolerance

Across multiple bookies for fault tolerance, supports global replication

GitHub Stats (as of July 11, 2023)

25.3k stars, 12.8k forks

12.9k stars, 3.3k forks

Community and Documentation

Large, active community with extensive documentation

Growing community, good documentation but not as comprehensive


Built-in CLI tools for various actions

Offers CLI tools for managing components and data


Numerous official and third-party clients

Good variety of official and third-party client libraries

Language Support

Broad support across many languages

Targets same primary languages, fewer third-party libraries


Larger ecosystem, hundreds of connectors

Growing ecosystem, tens of connectors

Stream Processing

Built-in (Kafka Streams), plus integrations

Basic built-in (Pulsar Functions), plus integrations


Apache License 2.0

Apache License 2.0

Commercial Support

Offered by many vendors

Fewer vendors offering support

Deployment Options

On-prem, Docker, cloud, Kubernetes

Similar flexibility with deployment options

Managed Services

Many well-known providers

Growing list of providers

Security - Audit

External tools required

Built-in audit logs

Data Isolation


Multi-tenancy, namespace isolation



Event-driven architecture will play a pivotal role in data streaming with Kafka developers can build reliable messaging and data streaming apps with high throughput capabilities. Pulsar offers an attractive alternative to Kafka with its features like geo-replication, multi-tenancy, and a separate serving and shortage architecture. For managing real-time data streams, building microservices architecture, or improving the responsiveness of your applications, businesses need to hire developers with expertise in event-driven architectures.

Embrace the power of event-driven architecture for your projects. Discover how Kafka and Pulsar can transform your data transformation capabilities! Contact us to know more!



Vinit Sharma, a seasoned technologist with over 21 years of expertise in Open Source, cloud transformation, DevSecOps strategy, and software architecture, is a Technical Architect leading Open Source, DevOps, and Cloud Computing initiatives at Clarion. Holding certifications as an Architect and Business Analyst professional, he specializes in PHP services, including CMS Drupal and Laravel, contributing significantly to the dynamic landscape of content management and web development.

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