Why is Kafka important?
Kafka is the most popular event streaming platform used today. While cloud providers like AWS offer more fully managed solutions (like AWS Kinesis), they introduce limitations that make Kafka preferable for most use cases.
The technology behind Kafka is what allows event streaming to work at scale in a fault tolerant manner. Using Kafka, organizations can process millions of events with low latency and high throughput. This is what allows companies like Uber to exist.
Without Kafka, the engineering community would rely more heavily on inferior pub/sub implementations like RabbitMQ or IBM MQ. While these platforms have proved resilient in production grade environments, they lack key advantages of Kafka including parallel consumption and fault tolerance.
So why is Kafka SO IMPORTANT? Because nothing better exists today for event streaming...
Because few alternatives exist that do what Kafka does...
Kafka is important because it represents the "latest and greatest" method for processing data in real time.
Kafka is important because it allows companies like Uber to track the location of millions of vehicles in real time.
Kafka is the preferred event streaming platform in modern programming. While alternatives exist, Kafka has proven superior in both performance and flexibility.
Why is Kafka important?
Kafka is important because it represents the cutting edge of event driven architecture. Kafka allows events to be published and consumed with scalability, performance, and resilience. Using Kafka, data streams can be processed in real time and read from multiple consumers in parallel.
Kafka is important because it beats the alternative as far as pub/sub is concerned. Message frameworks like MQ have proven resilient but they lack the sophistication of Kafka. There are key differences between these inferior solutions and Kafka.
Kafka is an event streaming platform. It is important because it allows companies like Uber to process millions of location updates in real time.
Without Kafka, software engineers would have to rely on inferior alternatives to send/receive messages in a distributed environment.
While alternative message oriented solutions exist for sending/receiving messages, Kafka wins in performance, scalability, and fault tolerance.
Using Kafka, companies can process live data streams in real time without worrying about down time, data loss, or scalability.