What is stream processing?

Stream processing is a real-time data processing paradigm that continuously analyzes and transforms data as it flows through a system, without needing to store the complete dataset first. Unlike batch processing, which operates on fixed chunks of data at scheduled intervals, stream processing handles data immediately upon arrival, making it ideal for time-sensitive applications like fraud detection, IoT sensor monitoring, real-time analytics, and recommendation systems. The approach typically employs specialized frameworks such as Apache Kafka, Apache Flink, or Apache Spark Streaming that maintain state across events and apply computations to data streams using techniques like windowing, filtering, aggregation, and pattern detection to extract meaningful insights with minimal latency.

What is Kafka?

Apache Kafka is a distributed event streaming platform designed to handle high-throughput, real-time data feeds.. Kafka serves as a central data pipeline that decouples data producers from consumers, allowing for asynchronous communication.

Decoupling: Kafka allows producers and consumers to operate independently, meaning:

• Producers don’t need to know who will consume their data.
• Consumers don’t need to know where exactly the data is coming from.
• This makes the system scalable and fault-tolerant.

Asynchronous Communication

• Asynchronous means that producers and consumers do not need to operate at the same time.
• Producers can send data without waiting for consumers to process it.
• Consumers can process data at their own pace, even if the producer has already sent it.
• This is different from synchronous communication, where the sender waits for the receiver to respond before continuing.

Topics and Events

• A topic is a continuous stream of events (single piece of data at a particular time)