When it comes to real-time data processing, Apache Flink is emerging as the dominant standard. It is bundled with Kafka Streams, a widely used Java library for stream processing.
Apache Flink, an independent and successful open-source project, provides a stream processing engine for real-time and batch workloads. The combination of Kafka (including Kafka Streams) and Flink is already widely used in many enterprises across all industries.
The Perfect Match
Bitrock envisions a world where Flink and Kafka not only coexist but thrive together, optimizing and improving the process and results of real-time data streaming and analysis.
These two systems share similar characteristics, including scalability, operational optimization, and easy integration with existing systems, leading to cost reduction, data compliance, and much more. Far from being opposed, Flink and Kafka work together to complement each other’s strengths and deliver superior results.
Flink can tap into heterogeneous data sources within our system. In a well-designed architecture, Kafka collects this information and concentrates it in its storage system, distributed logs (Kafka topics). Using Kafka connectors, these collect and store the entire flow of information from heterogeneous sources.
At this point, Flink has a single connection point to Kafka, where the data is in its preferred form (messages). This makes it easy for Flink to leverage its core capabilities: generating this data stream and enriching it through state management or using batch mechanisms.
The two systems can therefore not only coexist, but must coexist in a well-designed architecture. Otherwise, one or the other will take over tasks that are not its strengths.
What sets Flink apart
Flink has the typical characteristics of distributed, scalable, and elastic environments, but it has one distinguishing feature. Unlike other real-time (or near real-time) data streaming systems, Flink can manage states, ensuring that the data stream received by the processing platform or end user is enriched and processed.
Flink’s power lies in its ability to enrich the data stream, integrating it with other information or with processes and operations running within it, while maintaining real-time or near real-time capabilities.
Flink Use Cases
Flink has a wide range of possible use cases, and listing them all would result in an almost endless list. We have said that Flink is a framework for processing data streams that can come from a multitude of sources. Here are a few examples of application where using Flink can bring significant benefits in terms of increased efficiency and resource savings:
- Updating dashboards and visualizations to ensure a rich end-user experience.
- Real-time recommendation systems based on user behavior – on e-commerce sites to suggest upselling, enrich the offer based on user preferences.
- Fraud detection, verification of economic and financial transactions.
- Monitoring and alerting.
- Support for machine learning models that are constantly updated with new data.
Conclusions
The combination of Kafka and Flink provides a powerful and versatile solution for real-time data processing. This open-source powerhouse duo, with Kafka’s seamless integration and Flink’s robust processing, can tackle any stream processing challenge.
From hybrid cloud deployments to mission-critical transactions, and even real-time analytics with embedded machine learning, Kafka and Flink enable you to build the future faster.
Discover more about Flink and Kafka by listening to the latest episode of our Bitrock Tech Radio podcast, or get in contact with one of our experienced engineers and consultants!
