The Potential of Kafka : How Event Streaming Transforms Modern Applications

Introduction

Have you ever wondered about real-time data streaming? If not, let’s embark on a journey to explore the Kafka system and explore its strengths and weaknesses. Before jumping into learning about it, first, we should talk about Big Data, as we are surrounded by an enormous amount of data and the volume is huge; such data is termed Big Data. With this, two prime challenges come in our way:

• The first challenge is storing an enormous amount of unstructured data.
• Secondly, requires a system that can handle queuing, storage, and transmits of such a huge bulk and produces real-time results in no time.

In today’s widely distributed high throughput systems, Kafka comes into play which is specially designed to handle large volumes of data. In comparison to other older message broker systems, Kafka did exceptionally well. For large-scale message processing applications, Apache Kafka demonstrates superior throughput, built-in partitioning, replication, and fault tolerance when compared to other messaging systems.

Need to learn more about Kafka? Keep reading!

What is Kafka?

Kafka is one of the open-source distributed streaming platforms that is used to build real-time, event-driven applications. It is super-fast which makes it the best among others available in the market. It also maintains a very high level of accuracy and helps in maintaining order.

Components of Kafka:

1. Topics: Name given to channel from which messages are streamed by producers.
2. Producers: Data Sources that publish data on topics.
3. Consumers: These are just like producers but listen to Kafka topics and consume data from them.
4. Brokers: These are intermediaries that sit between producers and consumers, managing and storing the topics.
5. Partitions: Small divisions were created for load balancing in the topic. Each topic is cut down to either one or more partitions.
6. Offsets: Identifier uniquely identifies each message in a partition.
7. Consumer Groups: A set of consumers that combines to consume messages from a topic.
8. Replication: It is a mechanism by which Kafka ensures messages’ high availability and durability through replication.
9. ZooKeeper: As the name suggests, it is like a Super-visor that coordinates, manages, and configures.
10. Streams: Library for building real-time stream processing applications on top of Kafka

Uses of Kafka

Apache Kafka offers a great range of benefits that make it a powerful tool for real-time data streaming, event-driven architectures, and large-scale data processing. Below are some of the key pros of using Kafka:

1. Activity tracking: It is a common use case for monitoring and logging user behavior, system events, application interactions, and other real-time data. Kafka’s ability to handle high-throughput, low-latency message streams makes it a suitable platform for tracking and analyzing activities across distributed systems.
   Real-time data processing: It refers to the ability to process and analyze streaming data as it arrives, enabling timely decision-making and insights. Kafka’s design as a distributed event streaming platform makes it ideal for real-time applications such as monitoring, fraud detection, recommendation systems, and real-time analytics.
2. Messaging: Messaging in Apache Kafka is one of the fundamental use cases for which Kafka was originally developed. Kafka is a distributed event streaming platform that excels in handling high-throughput, low-latency messaging for applications that need to send and receive messages asynchronously. It allows for reliable and scalable message delivery across a distributed system. Kafka’s messaging system is used for event-driven architectures, real-time analytics, data pipelines, and much more.
3. Operational Metrics/KPIs: Monitoring Apache Kafka is essential for ensuring its health, performance, and reliability in production environments. By tracking key operational metrics and Key Performance Indicators (KPIs), you can detect issues, optimize configurations, and ensure that Kafka is running efficiently.
4. Log Aggregation: This refers to the process of collecting and consolidating logs from various sources (e.g., servers, applications, services) into a central system where they can be stored, analyzed, and processed. Apache Kafka, as a distributed event streaming platform, is widely used for log aggregation in modern architectures due to its scalability, durability, and high throughput. Kafka can collect, store, and stream log data, enabling real-time log analysis and monitoring.

When ‘Not’ to use Kafka

1. Not for applications with a lesser amount of data: When dealing with Kafka in scenarios where the data volume is relatively low or moderate, many of the features and configurations designed to handle large-scale data can be overkill. In such cases, optimizing for simplicity, cost-efficiency, and resource management is key. Kafka can still provide substantial value, even for systems with lesser amounts of data, but the configuration and deployment choices might differ.
2. Streaming ETL: (Extract, Transform, Load) is the process of continuously ingesting, transforming, and loading data in real-time or near-real-time as it flows through various systems. In a traditional batch ETL pipeline, data is extracted from a source, transformed in batches, and loaded into a destination system at scheduled intervals. In contrast, streaming ETL processes data continuously as it arrives, providing real-time insights and enabling immediate data-driven decision-making.

Read More: Unlocking the Potential: Kafka Streaming Integration with Apache Spark

Cons of Kafka

1. No Complete Set of Monitoring Tools: Although Kafka itself provides metrics and logging capabilities, managing Kafka clusters effectively requires a lot of custom work or third-party tools for full visibility into the system’s health and performance.
2. Issues with Message Tweaking: Message tweaking, or altering messages in-flight as they are processed by producers, consumers, or stream processors in Kafka, can introduce a variety of complexities and challenges. While Kafka is highly flexible in terms of allowing message processing and transformation, the act of modifying messages (e.g., filtering, enriching, or changing the structure of messages) can cause several issues if not handled carefully
3. Reduces Performance: Apache Kafka is a high-performance, distributed event streaming platform. However, several factors can reduce its overall performance, affecting throughput, latency, and reliability. These performance bottlenecks can arise from improper configuration, resource limitations, or inefficient usage patterns
4. Lack of some Messaging Paradigms: While Apache Kafka is a highly versatile and scalable event streaming platform, it is primarily designed around a publish-subscribe and message queue paradigm. However, there are certain messaging paradigms that Kafka doesn’t fully support or have limitations in supporting, which might require workarounds or alternative solutions in some use cases.

Use Case of Kafka

If a developer working on development for a retail application, the first thing that needs to be created is checkout. So, when a user checks out the product from the application, the order goes for shipment. The second thing that comes into the picture is data volume, the way data is transported, and the format of data, it is only one integration that needs to be developed which is not a big deal for them. But, as the application grows, a few more things get added likewise an automated email receipt when a checkout happens, or an update to the inventory when checkout triggers. As the front and back-end services get added at any point of time in the application and the application grows with time, more and more integrations need to be built and eventually get very complicated. It will create a lot of dependency among teams and rely on each other.

So, here comes Kafka which helps in decoupling system dependencies. Every time a checkout happens that will get streamed in topic. Then the other services like- email, shipment, and inventory- subscribe to that stream, listen to that one, and then get the information they need and it triggers accordingly. This is how Kafka can be helpful and reliant.

Conclusion

One of the reasons for Kafka’s popularity is its power and flexibility. This message-based Kafka system is reliable, scalable, and fault-tolerant. It excels in real-time, complex scenarios that require data processing and monitoring of application activity. When there’s a need for real-time data handling, Kafka should undoubtedly be your go-to solution.

TO THE NEW aims to transform traditional Quality Assurance into Quality Engineering by leveraging technology catering to CICD & Agile environments. Contact our experts today.