Understanding Kafka and how it solves real-time data challenges

Apache Kafka is a distributed event streaming platform designed to handle high-throughput, real-time data feeds. In this article, you will discover foundational concepts, as well as how Kafka helps process data flow management and ensures efficient delivery while maintaining high performance.

The complexity of real-time data management

As technology advances and the digital world grows, the demand for applications to respond to requests as quickly as possible is becoming increasingly significant. With each passing day, the urgency for swift responses amplifies.

The challenge of maintaining low response times becomes even more difficult as an application sees more usage. This task is further complicated when there’s a need to incorporate real-time data, such as when monitoring data or conducting real-time analytics.

One might consider addressing this challenge by creating direct pipelines from the data source to its ultimate destination. This approach essentially involves constructing a direct path for the data to travel, ensuring rapid delivery.

However, this would inevitably lead to the creation of dozens of custom pipelines. Each of these pipelines would need to be individually set up, monitored, and maintained.

Given the complexity and specificity of each pipeline, this task would require a substantial amount of manpower. As the number of pipelines increases, so too does the required workforce.

It quickly becomes apparent that this approach is impractical and unsustainable in the long run.

However, there is a more viable solution to this problem: a stream processing platform like Kafka.

Kafka is specifically designed to handle real-time data. It simplifies the process of managing data flow and ensures efficient delivery, all while maintaining high performance.

This makes Kafka an excellent tool for handling the challenges of real-time data, providing a feasible solution to a complex problem.

Where did Kafka come from?

The Kafka platform was originally developed by Jay Kreps, Neha Narkhede, and Jun Rao while they were at LinkedIn. These three individuals were the driving force behind the development of Kafka before it became open source in 2011.

After it was open-sourced, Jay Kreps embarked on a new journey in 2014 by starting up Confluent. This company was built around the concept of real-time data and the use of Kafka.

Confluent has since evolved to be the biggest contributor to the Kafka project, continuously developing and improving the platform. The company’s commitment to Kafka has significantly impacted the continual growth and development of the platform.

Over the years, Kafka has grown and evolved into a robust open-source platform that is now in use by 80% of Fortune 100 companies. These companies use Kafka to distribute data to various applications and platforms effectively and efficiently.

It has become an invaluable tool in the tech industry, serving as an event-streaming platform used for creating high-performance data pipelines, conducting streaming analytics, integrating data, and supporting mission-critical applications.

The rise of Kafka demonstrates the growing need for efficient, real-time data processing and distribution in today’s fast-paced digital world.

As quick response times and real-time data become increasingly important, platforms like Kafka are proving to be essential tools for companies wanting to stay competitive and innovative in their respective industries.

Popular use cases for Kafka

Over the last years, we’ve seen a rapid increase in Axway customers leveraging Kafka, both alongside and in connection with our solutions.

In subsequent blog posts, we’ll discuss some of the specific B2B/EDI and (event-driven) API use cases we are seeing among our customers. For now, the remainder of this article will discuss generic Kafka use cases at large organizations.

Companies such as LinkedIn, Netflix, and Spotify use Kafka in various ways, demonstrating its versatility and adaptability across different industries and use cases.

LinkedIn, one of the original driving forces behind Kafka, employs this platform for real-time data feeds and analytics.

With the vast network of connections and the constant flow of information on LinkedIn, Kafka allows them to manage and analyze this data efficiently, providing valuable insights and enabling dynamic user interactions.

Netflix, a global leader in the streaming industry, leverages Kafka to monitor its extensive system of microservices.

Given the sheer volume of activity and data on Netflix, Kafka plays a vital role in ensuring that all the microservices are functioning properly and efficiently.

Additionally, Netflix uses Kafka to process vast amounts of data in real time, providing them with real-time insights that are crucial to maintaining their high-quality user experience.

Spotify, another major player in the streaming industry, also uses Kafka for real-time analytics and to manage its enormous data pipeline. As with Netflix, the ability to process and analyze data in real time is crucial to Spotify’s success.

With Kafka, Spotify can manage the flow of data more efficiently, enabling them to provide personalized recommendations and a seamless user experience.

Kafka helps enterprises around the world leverage real-time data

Kafka has established itself as a powerful and effective tool for managing real-time data. Its capacity to deal with large amounts of data instantaneously makes it a vital asset in the contemporary digital landscape.

Businesses such as LinkedIn, Netflix, and Spotify are using Kafka to oversee their data pipelines and perform real-time analysis, showcasing the platform’s wide-ranging applicability and flexibility.

As the requirement for immediate response times and real-time data escalates, platforms like Kafka are poised to become even more crucial in aiding businesses to maintain their competitive edge and foster innovation.

In an upcoming series of articles, we’ll demonstrate more advanced Kafka implementations, showing how organizations can:

• Modernize legacy EDI/B2B integration patterns,
• Transform raw Kafka events into well-designed APIs, and
• Scale integration capabilities through reuse to create new business value from existing event streams.