Apache Kafka

Distributed Architecture: Kafka is designed to be distributed across multiple servers, offering high availability, fault tolerance, and scalability.
Publish-Subscribe Messaging System: Kafka allows multiple producers to publish messages to topics, which consumers can subscribe to, enabling decoupled communication between different parts of an application
Topics and Partitions: Data is organized into topics, which are further divided into partitions. Each partition is an ordered, immutable sequence of records that Kafka appends to in real-time.
High Throughput and Low Latency: Kafka can handle large volumes of data with minimal latency, making it ideal for high-throughput use cases like log aggregation, real-time analytics, and event sourcing.
Durability and Fault Tolerance: Data in Kafka is written to disk and replicated across multiple brokers, ensuring that it is durable and fault-tolerant.
Stream Processing: Kafka includes Kafka Streams, a stream processing library that enables processing and transforming data in real-time as it flows through Kafka topics.
Scalability: Kafka's architecture allows easy scaling by adding more brokers to a cluster, which distributes the load across more hardware.
Retention and Compaction: Kafka allows you to define retention policies for how long data is kept, with options for data compaction to keep only the latest value for each key, reducing storage requirements.<
Exactly-once Semantics: Kafka supports exactly-once delivery semantics, ensuring that messages are neither lost nor duplicated during processing.
Integrations and Ecosystem: Kafka integrates with a wide range of data sources and sinks, and its ecosystem includes tools like Kafka Connect for integration with external systems and Confluent Schema Regist