Cassandra vs Kafka: What are the differences?

Cassandra and Kafka are two popular open-source distributed systems used for different purposes. While Cassandra is a highly scalable and distributed NoSQL database, Kafka is a distributed streaming platform. Let's explore the key differences between them.

1. Data Storage and Processing: Cassandra is primarily designed for data storage and processing, providing a highly scalable and fault-tolerant solution for storing data across multiple nodes. On the other hand, Kafka is a distributed streaming platform that enables high-throughput, fault-tolerant distributed messaging and real-time data streaming.

2. Message vs Data: Cassandra is designed to store and retrieve large amounts of structured and unstructured data, making it suitable for use cases such as online transaction processing, time-series data, and analytics. In contrast, Kafka is optimized for handling streams of messages or events, making it ideal for scenarios like real-time analytics, event sourcing, log aggregation, and data integration.

3. Data Model: Another significant difference is their data model. Cassandra uses a flexible schema-less data model, allowing users to store and query data without predefined structure or fixed schema. It supports both key-value and tabular data models. On the other hand, Kafka doesn't have a data model of its own. It acts as a message queue that can transmit data in any format or structure, such as JSON, Avro, or raw bytes.

4. Data Consistency: Cassandra provides strong consistency guarantees, ensuring that data written to the database is immediately consistent across all replicas. It achieves this by using the Quorum consistency level for read and write operations. In contrast, Kafka sacrifices strong consistency for high throughput and fault tolerance. It guarantees at-least-once message processing, providing eventual consistency across consumers.

5. Data Replication: Cassandra uses a peer-to-peer distributed architecture with data replication across multiple nodes, ensuring high availability and fault tolerance. It uses a configurable replication factor to determine the number of copies of data stored across the cluster. Kafka, on the other hand, uses a distributed log-based architecture with replication for fault tolerance. It replicates data, called partitions, across multiple brokers to provide durability and availability.

6. Query Language: Cassandra uses CQL (Cassandra Query Language), which is similar to SQL, for querying data. It supports a wide range of query operations, including filtering, aggregation, and joins. Kafka, being a distributed messaging system, doesn't have a query language of its own. However, it provides a simple publish-subscribe API and supports Kafka Streams, which allows for real-time data processing using a stream processing DSL.

In summary, Cassandra is a NoSQL database used for scalable and fault-tolerant data storage and processing, while Kafka is a distributed streaming platform used for high-throughput, fault-tolerant messaging and real-time data streaming.