What happens if one of the Request-Response chain’s links breaks? Requests that have been waiting for a response do not get one. They either keep waiting or they run out of time. The application as a whole has been disabled. In addition, as the number of services grows, so does the number of synchronous interactions between them. In this case, a single system’s outage has an impact on the availability of other systems. Hence, building a microservices application using an Event Driven Architecture (EDA) can help save your day!
An Event Driven Architecture (EDA) is a microservices-based architectural paradigm that is becoming more prominent with rising in Big Data and Cloud environments. This isn’t just a coincidence. From the standpoint of Developers, EDA provides an efficient technique of linking microservices, which can aid in the development of future-proof systems. Furthermore, event-driven systems when integrated with robust streaming services such as Apache Kafka become more agile, durable, and efficient than prior messaging approaches.
This article will help you understand the reason behind the rising popularity of Event Driven Architecture. You will discover more about the basic components and frequently used patterns for building the Event Driven Architecture. However, this article places a major focus on the need for Kafka Event Driven Architecture and how you can develop it. At the end of this article, you will explore the various benefits and use cases where you can leverage this architecture. So, let’s begin the journey to the world of Kafka Event Driven Architecture by understanding what Kafka is.
Table of Contents
- Introduction to Apache Kafka
- Why Use Kafka for an Event Driven System?
- The Need for Kafka Event Driven Architecture
- Components of an Event Driven Architecture
- Patterns of Event Driven Architecture
- Steps to Build a Kafka Event Driven Architecture
- Benefits & Use Cases of Kafka Event Driven Architecture
Introduction to Apache Kafka
Apache Kafka is a Distributed Event Streaming solution that enables applications to efficiently manage large amounts of data. Its fault-tolerant, highly scalable architecture can easily manage billions of events.
The Apache Kafka framework is a Java and Scala-based distributed Publish-Subscribe Messaging system that accepts Data Streams from several sources and allows real-time analysis of Big Data streams.
It can quickly scale up and down with minimal downtime. Kafka’s global appeal has grown as a result of its minimal data redundancy and fault tolerance.
Thousands of businesses, including more than 60% of the Fortune 100, use Kafka. Box, Goldman Sachs, Target, Cisco, Intuit, and others are among them.
Kafka is a trusted platform for enabling and developing businesses. Kafka Event Driven Architecture allows firms to upgrade their data strategy and increase productivity.
Key Features of Apache Kafka
Apache Kafka is the most popular Open-Source, Stream-processing platform, with high throughput, low latency, and fault tolerance. Let’s have a look at some of these powerful features:
- Fault-Tolerant & Durable: By distributing partitions and replicating data over several servers, Kafka protects data from server failure and makes it fault-tolerant. It has the ability to restart the server by itself.
- Highly Scalable with Low Latency: Kafka’s partitioned log model distributes data over several servers, allowing it to extend beyond the capabilities of a single server. Kafka has low latency and great throughput since it separates data streams.
- Robust Integrations: Kafka supports various third-party integrations. It also offers many APIs. Hence, you can add more features in a matter of seconds. Take a look at how you can use Kafka with Amazon Redshift, Cassandra, and Spark.
- Detailed Analysis: For tracking operational data, Kafka is a popular solution. It enables you to collect data from several platforms in real-time and organize it into consolidated feeds while keeping a check with metrics. Refer to the Real-time Reporting with Kafka Analytics article for further information on how to analyze your data in Kafka.
Why Use Kafka for an Event Driven System?
It is critical to understand the true meaning of the term “Event” when we use it. A record of “something that happened” is called an Event. When you read or transmit data to Kafka, you do so using Events.
A key, value, timestamp, and optional metadata headers can all be part of an Event. As a result, an Event may be seen as a single atomic data point.
For example, when you visit a website and register or sign up, that signup is an event with some data associated with it. This may contain all of the information required to sign up. The event can also be thought of as a message including data.
Kafka is an Event-Streaming system that handles a continuous stream of events. Event Streaming is the process of capturing data in the form of streams of events in real-time from event sources such as databases, IoT devices, or others.
Then you store them durably for later retrieval, analysis, or process them in real-time, and route them to various destinations as needed. Event Streaming allows for continuous data flow and interpretation, ensuring that the appropriate information is available at the right time and in the right place. In this article, you will learn more about Event Driven Architecture using Apache Kafka.
The Need for Kafka Event Driven Architecture
In an Event Driven Architecture, an event notification is generated, the system captures what happened and waits to provide the response back. The application that got the notification might either reply right away or wait till the status changes.
Event Driven Architecture allows for more flexible, scalable, contextual, and responsive digital business systems. This is why this architecture style has been gaining popularity.
The key rationale for leveraging Apache Kafka for an Event Driven system is the decoupling of microservices and the development of a Kafka pipeline to connect producers and consumers. Instead of checking for new data, you may just listen to a certain event and take action. Kafka provides a scalable hybrid approach that incorporates both Processing and Messaging.
Another advantage of using Kafka Event Driven Architecture is that, unlike messaging-oriented systems, events published in Kafka are not removed as soon as they are consumed. They are removed once a specific amount of time has passed.
During their lifetime, they may be read by a variety of consumers, allowing them to respond to a variety of use cases, which is exactly what you want in an Event Driven strategy.
Apart from the above features, Kafka comes with a rich ecosystem of tools, such as Kafka Connect, which allows you to collect Events from a third-party system (e.g., database, S3, etc.) and deliver them to a Kafka Topic, or vice versa.
This makes it a suitable choice for Kafka Event Driven Architecture. Moreover, Kafka Streams enables you to work with Event flows and construct new flows from existing ones, allowing you to make the most of data in transit while maintaining a high level of flexibility.
Hence, now you might have understood the popularity of Kafka and why it is among the best choice to use for Event Driven Architecture. In the next part of the blog, you will understand the various components and patterns that can be used to build a Kafka Event Driven Architecture.
Simplify Kafka ETL and Data Analysis with Hevo’s No-code Data Pipeline
Hevo Data, a No-code Data Pipeline, helps load data from any data source such as Databases, SaaS applications, Cloud Storage, SDK,s, and Streaming Services and simplifies the ETL process. It supports 100+ Data Sources including Apache Kafka, Kafka Confluent Cloud, and other 40+ Free Sources. You can use Hevo Pipelines to replicate the data from your Apache Kafka Source or Kafka Confluent Cloud to the Destination system.
Hevo loads the data onto the desired Data Warehouse/destination and transforms it into an analysis-ready form without having to write a single line of code. Hevo’s fault-tolerant and scalable architecture ensures that the data is handled in a secure, consistent manner with zero data loss and supports different forms of data.GET STARTED WITH HEVO FOR FREE
Check out why Hevo is the Best:
- Secure: Hevo has a fault-tolerant architecture that ensures that the data is handled securely and consistently with zero data loss.
- Schema Management: Hevo takes away the tedious task of schema management & automatically detects the schema of incoming data and maps it to the destination schema.
- Minimal Learning: Hevo, with its simple and interactive UI, is extremely simple for new customers to work on and perform operations.
- Hevo Is Built To Scale: As the number of sources and the volume of your data grows, Hevo scales horizontally, handling millions of records per minute with very little latency.
- Incremental Data Load: Hevo allows the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
- Live Support: The Hevo team is available round the clock to extend exceptional support to its customers through chat, email, and support calls.
- Live Monitoring: Hevo allows you to monitor the data flow and check where your data is at a particular point in time.
Simplify your ETL & Data Analysis with Hevo today!SIGN UP HERE FOR A 14-DAY FREE TRIAL!
Components of an Event Driven Architecture
Event Driven Architecture is mostly employed in real-time systems that require you to act and respond to data changes and events in real-time. This architecture is especially beneficial for IoT systems. Let’s understand the basic components included while building the Event Driven Architecture.
- Event: When users take action, a major change in the state of an object that happens is referred to as an Event.
- Event Handler: It is a software routine that handles the Event occurrence.
- Event Loop: The interaction between an Event and the Event Handler is handled by the Event Loop.
- Event Flow Layers: The logical layers that make up the event flow layer are as follows:
- Event Producer: They are responsible for detecting and generating Events.
- Event Consumer: They consume the events generated by the Event Producer.
- Event Channel: It is also called Event Bus. It helps to transfer events from the Event Generator to the Event Consumer.
Patterns of Event Driven Architecture
Kafka Event Driven Architecture can be implemented in a variety of ways. Here’s a short rundown of some of the most common Event Driven Architectural patterns that you can deploy using Apache Kafka:
1) Event Notification
This is a simple and direct model. A microservice merely broadcasts events to alert other systems of a change in its domain. For example, when a new user is created, a user account service could generate a notification event.
Other services can use this information, or it can be disregarded. Notification events often contain little data, resulting in a loosely coupled system with less network traffic dedicated to messaging.
2) Event Carried State Transfer
In this approach, the event’s recipient also obtains the data it needs to do additional actions on the required data. For example, the aforementioned user account service may send out an event with a data packet including the new user’s login ID, complete name, hashed password, and other relevant information.
This architecture may appeal to developers who are experienced with RESTful interfaces. However, it might result in a lot of data traffic on the network and data duplication in storage, depending on the system’s complexity.
3) Event Sourcing
This model’s purpose is to describe every change of state in a system as an event, with each event being recorded in chronological sequence. As a result, the event stream itself becomes the system’s primary source of truth.
It should be able to “replay” a sequence of events in order to reproduce the state of a SQL database at a certain moment in time. This approach has a lot of interesting potentials, but it may be difficult to get it working right, especially when events involve external system engagement.
As you can see above, Event Driven Architecture can be deployed in many patterns. Some are simpler to implement and others may be more adaptive to complicated requirements.
So, which pattern is appropriate for a specific use case depends on a variety of parameters, including the number of microservices involved, how closely they should be connected, etc.
Steps to Build a Kafka Event Driven Architecture
In this section, you will understand how to create a Kafka Event Driven Architecture using Python. Before you proceed further make sure you have started the Kafka and Zookeeper services.
In the given Kafka Event Driven Architecture example, the producer will send out an event to Kafka along with the timestamp. The consumer will receive this event and print the timestamp. So, follow the steps below to get get started:
- Step 1: Set Up the Environment
- Step 2: Configure the Event Producer
- Step 3: Configure the Event Consumer
- Step 4: Execute the Kafka Event Driven Architecture
Step 1: Set Up Python Environment
After you have set up your docker and started the Kafka and Zookeeper services, create 2 python projects namely “producer” and “consumer”. Or you can simply clone the GitHub repository from here.
Download the requirements.txt file from the above link which contains the kafka-python==2.0.2 dependency info. Then type the following command in your terminal to set up the Python version in each project.
python3 -m pip install -r requirements.txt
Step 2: Configure the Event Producer
After setting up the Python dependency, let’s proceed further to set up the Event Producer. It will produce timestamps and send them to Event Consumer via Kafka. Create a main.py file in producer python project and enter the following code snippet:
from kafka import KafkaProducer from datetime import datetime from json import dumps from time import sleep producer = KafkaProducer(bootstrap_servers='localhost:9092', value_serializer=lambda x: dumps(x).encode('utf-8')) while True: timestampStr = datetime.now().strftime("%H:%M:%S") print("Sending: " + timestampStr) producer.send('timestamp', timestampStr) sleep(5)
In the above code:
- bootstrap_servers parameter provides the host location of Kafka. By default it is localhost:9092.
- value_serializer specifies how the messages will be encoded.
The code will produce a timestamp string in the format of H:M:S.
Step 3: Configure the Event Consumer
After the producer creates timestamps, you need a consumer to listen to them. So, create a main.py file in the consumer python project and enter the following code snippet:
from kafka import KafkaConsumer from json import loads consumer = KafkaConsumer('timestamp', value_deserializer=lambda x: loads(x.decode('utf-8'))) for message in consumer: print(message.value)
In the above code:
- value_deserializer specifies how the received messages will be decoded.
The above code will print the timestamp received from the Event Producer.
Step 4: Execute the Kafka Event Driven Architecture
After you have completed the above steps, it’s time that you test your Kafka Event Driven Architecture. Your project structure will basically look like this:
code / - docker-compose.yml - producer -- main.py - consumer -- main.py
In your directory code, the Kafka and Zookeeper can be started using the following command:
docker-compose up -d
Next, change to producer directory and activate the environment using the following command:
$ source venv/bin/activate (venv) $ python3 main.py
Similarly, do it for the consumer directory.
Now, you will see the output of the above Kafka Event Driven Architecture build as shown below. The Event Producer is sending timestamps and the Event consumer is listening to them and printing them.
Hurray! You have learned the basic steps to get started with Kafka Event Driven Architecture. If you wish to generate and print timestamps in JSON format, you can refer to this article.
Benefits & Use Cases of Kafka Event Driven Architecture
Using a scalable Kafka Event Driven Architecture, you can generate and respond to a huge number of events in real-time seamlessly. Microservices, which are loosely connected software, benefit greatly from an Event Driven design. These architectures are very versatile since they operate well with unpredictable and non-linear events.
Key Benefits of Kafka Event Driven Architecture
Let’s discover some of the benefits of Kafka Event Driven Architecture:
- Decoupling: Brokers decouple services to make it easier to add new ones or adjust existing ones. Hence, the long sequences are broken and synced workflows can be decomposed easily.
- Efficient State Transfer: Your system’s dataset includes Events. Streams are a convenient way to distribute datasets such that they can be reassembled and queried within a limited environment. When resources are available, messages are buffered and consumed.
- Faster Operations: It’s easy to merge, join, and enrich data from several services. Joins are simple and quick to be executed. For high-volume Event processing, Event Driven services scale easily.
- Easy Traceability: When there’s a central, immutable, retentive narrative documenting each action as it unfolds in time, it’s simpler to debug the errors and issues.
Use Cases of Kafka Event Driven Architecture
The below figure depicts some of the common use cases in various industries where the Kafka Event Driven Architecture can be implemented:
In this article, you gain an in-depth understanding of Kafka Event Driven Architecture. You understood the need for Event Driven Architecture, its component, and commonly deployed patterns in the industry. In addition, you learned the key steps to building a Kafka Event Driven Architecture using Python.
At the end of this article, you discovered some of the use cases and benefits of Kafka Event Driven Architecture. Hence, with the increase in popularity of Event Driven Architectures, it is critical to identify the right method to boost your business workflow and simplify complex tasks.
However, streaming data from various sources to Apache Kafka or vice versa can be quite challenging and cumbersome. If you are facing these challenges and are looking for some solutions, then check out a simpler alternative like Hevo.
Hevo Data is a No-Code Data Pipeline that offers a faster way to move data from 100+ Data Sources including Apache Kafka, Kafka Confluent Cloud, and other 40+ Free Sources, into your Data Warehouse to be visualized in a BI tool. You can use Hevo Pipelines to replicate the data from your Apache Kafka Source or Kafka Confluent Cloud to the Destination system. Hevo is fully automated and hence does not require you to code.VISIT OUR WEBSITE TO EXPLORE HEVO
Want to take Hevo for a spin?
Feel free to share your experience of building Kafka Event Driven Architecture with us in the comments section below!