Apache Flink plus Apache Kafka: A perfect open-source combination

Organizations in the era of ongoing digital transformation should plan how to quicken their business pace in order to stay competitive, if not outpace it. Consumers are changing quickly, making it more challenging to meet their changing needs. Because of this, she believe that having access to real-time data is essential to developing corporate agility and improving decision-making.

Real-time data is based on stream processing. It enables your company to absorb real-time continuous data streams and push them to the front for analysis, allowing you to stay ahead of ever-changing market conditions.

Collaboration between Apache Flink and Apache Kafka

The open-source event streaming de facto enterprise standard, Apache Kafka, is well known to everyone familiar with the stream processing environment. great throughput and great fault tolerance in the event of an application failure are just two of Apache Kafka’s many impressive features.

Data is delivered to its destination via Apache Kafka streams, but when Apache Kafka is installed separately, its potential is not fully realized. To make sure you’re getting the information you need from your real-time data, Apache Flink should be a vital component of your technology stack if you already use Apache Kafka.

When Apache Flink and Apache Kafka are combined, the possibilities for open-source event streaming grow dramatically. Low latency is produced by Apache Flink, which enables you to react fast and precisely to the growing corporate demand for prompt action. When combined, you have the power to produce automation and insights in real-time.

You can obtain an unfiltered stream of events from everything that occurs in your company when you use Apache Kafka. Nevertheless, some of it gets caught in queues or huge data batch processing, and not all of it is necessarily actionable. This is where working with relevant events instead of raw events is possible thanks to Apache Flink. Furthermore, Apache Flink uses pattern recognition to contextualize your data so you can see how different events relate to one another.

This is crucial because processing historical data could make events less valuable because they have a finite shelf life. Think about handling situations involving aircraft delays. These situations need for quick attention, and handling them after the fact will undoubtedly make some very irate consumers.

Apache Kafka communicates everything that is always happening within your company, serving as a kind of firehose of events. The ideal blend of pattern recognition, facilitated by Apache Flink, and this event firehose allows for lightning-fast response times once the pertinent pattern is identified. Use Apache Flink in conjunction with Apache Kafka to receive a wealth of capabilities that will help you: encourage positive behavior in your consumers, make smarter decisions in your supply chain, and captivate them with the appropriate offer at the right moment.

Developing Apache Flink: Providing Apache Flink to Everyone

You may be thinking, now that IBM have shown how useful it is for Apache Kafka and Apache Flink to collaborate, who can use this technology to work with events. Usually, it’s coders these days. Still, when you wait for astute developers with heavy workloads, things can move slowly. Furthermore, expenses are always a crucial factor to take into account because companies cannot afford to engage in every opportunity until there is proof that it will provide value. Finding the right people with the necessary expertise to take on development or data science initiatives is difficult, which increases complexity.

For this reason, it’s critical to provide more business professionals access to events for their benefit. Other users, like as analysts and data engineers, can begin to obtain real-time insights and work with datasets when it counts most when you make working with events easy. As a result, by avoiding crucial information from becoming trapped in a data warehouse, you lower the skills barrier and accelerate your data processing speed. 

By building on Apache Flink’s features, IBM’s approach to event streaming and stream processing applications addresses these significant industry challenges in an open and modular manner. In order to prevent vendor lock-in, IBM’s solution builds on what customers already have and Apache Flink can be integrated with any version of Apache Kafka. Making the most of this perfect combination, IBM grabbed the initiative and selected Apache Flink as the preferred option for event processing, with Apache Kafka serving as the industry standard for event distribution.

What if you could experiment with automations while still having a continuous view of your events? With a straightforward, user-friendly, no-code style that enables individuals with little to no knowledge in SQL, Java, or Python to exploit events, regardless of their function, IBM created IBM Event Automation in this spirit. IBM Automation, Integration Software’s VP of Product Management, Eileen Lowry, discusses the innovation that IBM is implementing with Apache Flink.

“IBM understand that funding event-driven architecture projects might require a significant financial commitment, but they also understand how important they are to a company’s ability to compete. They’ve seen them become completely trapped as a result of budget and expertise limitations. Because of this, IBM created IBM Event Automation, a no-code solution for Apache Flink that simplifies event processing. You can test new concepts more rapidly, extend into new use cases by reusing events, and shorten the time it takes to get data.

This UI not only makes Apache Flink accessible to all those who can benefit the business, but it also fosters experimentation, which can spur innovation and accelerate data pipelines and analytics. The tool allows the user to configure events from streaming data and receive feedback right away. You may pause, alter, aggregate, play, and test your solutions against data right now. Think about the innovations that could result from this, including bettering your e-commerce strategies or preserving your products’ real-time quality control.