Introduction
If you’re looking for effective data streaming and processing methods, incorporating Apache Kafka and Apache Flink into your problem-solving architecture is a wise choice. Kafka empowers event stream processing by letting systems store, retrieve, and manipulate data. Flink provides a means of running computations over data streams, whether they’re bounded (finite) or unbounded (continuous). In tandem, they provide strong, reliable means of gathering and processing data. Here we review what each solution does well and what they can achieve when they’re put together.
Apache Kafka Advantages
Over the years, Apache Kafka has become a central technology for event-driven architecture.
There are other architectural options available, such as service-oriented architecture (SOA), that allow connecting endpoints among the various components of a system’s tech stack to achieve whatever end. (For more information, refer to IBM’s or AWS’s explainers.)
Kafka approaches the question of data transmission and integration a bit differently. With Kafka, you establish “topics” that receive and record messages or events. You can then direct other parts of your tech stack to read or listen to those topics. Thus, instead of setting up components and decorators across all the different parts of your tech stack, you can use Kafka connectors to configure the downstream parts of the stack to listen for the appropriate signals like an operator tuning in to a radio channel. This can make for a much more efficient arrangement in environments with high volumes of data.
Further, Kafka is billed as “an open-source distributed event streaming platform.” Each of these descriptors captures part of what makes Kafka special:
- Open-source: Kafka’s code is available to the public. This means that Kafka users know exactly what they’re hooking up to their systems or solutions. Also, by letting all the world see how Kafka works, Apache enjoys much higher odds of catching and repairing bugs or security vulnerabilities before they cause problems.
- Distributed: Kafka involves a “cluster” setup consisting of servers and clients. This provides great flexibility for deploying it. You could keep Kafka on a machine on your company’s premises, or you could deploy it across multiple locations, including cloud environments.
- Event streaming platform: Kafka gives systems or solutions the capacity to read and write to streams of events, as well as store and process event streams.
In tandem, these principles unlock the power of distributed event stream processing. Not only does it allow you to handle high volumes of streaming data, but it also lets you replicate your data across multiple locations, keeping it safe in the event of maintenance or disaster.
Apache Flink Advantages
Apache describes Flink as “a framework and distributed processing engine for stateful computations over unbounded and bounded data streams.” If Kafka’s main function boils down to recording information, then Flink’s main function is processing it. Apache Flink is therefore commonly paired with Apache Kafka or Apache Polaris.
To provide a simplified picture: Kafka stores data in topics, but isn’t designed to do much with the data beyond that. Flink, by contrast, creates tasks (or “jobs”) for processing and transforming data, whether that data comes from Kafka topics or other databases. Flink thus gives your system the “brains” for dealing with your data.
What makes Flink particularly useful, though, is its role in data streaming. Flink can work with continuously updating streams of data, giving systems the ability to process large volumes of data from a variety of sources. In situations where millions of data points are generated, such as when a financial institution tracks its worldwide transaction information, Flink can give systems the materials to meaningfully work with all that incoming data.
It’s also worth noting that, since Flink is distributed like Kafka, it enjoys the same advantages we outlined above. Namely: versatility, flexibility, and durability. In addition, it can drive distributed event stream processing.
Combining the Powers of Kafka and Flink
Kafka and Flink are each useful on their own, but pairing them together unlocks their true potential. Their functions can be played off one another to produce more sophisticated data transformations – which can be harnessed to serve all kinds of purposes.
An especially useful Flink feature is that its outputs can be fed into new Kafka topics, which can then be read by the rest of a system’s tech stack. Thus, through repeated applications, you can use Flink to refine data or create steadily higher-level abstractions. This principle furnishes one of the core operating concepts behind Cogynt: comparing data against a series of hierarchical patterns in a model to draw inferences and perform predictive analysis.
For example, analysts using Cogynt Workstation can specify the topics they wish to examine. Cogynt will then show the data it has gathered and the inferences it has made about it. Analysts can then use these findings to support their own analysis. In this manner, Cogynt provides usable decision intelligence even when dealing with noisy, high-volume data.
