Did you know that the software architecture that we use affects the performance of our application, especially if the application needs to handle a large volume of data? Therefore, it is vital that we design the software architecture to ensure we don’t lose data. In this article, we discuss Kappa, Lambda, CQRS and Even sourcing architecture styles that are used in data-centric applications.

Kappa Architecture

The Kappa Architecture is a data processing framework designed to handle real-time and batch data streams smoothly. It was introduced as an alternative to the Lambda Architecture, aiming to simplify the overall architecture for data processing. In the Kappa Architecture, all data is treated as an immutable stream, eliminating the need for separate batch and real-time processing layers. Instead, data is ingested into a single stream and processed using stream processing systems like Apache Kafka, Flink, or Spark Streaming. This approach simplifies the development, maintenance, and scalability of data pipelines, making it well-suited for applications requiring low-latency data processing and analytics.

Lambda Architecture

The Lambda Architecture is a robust data processing framework that combines batch and real-time data processing to provide comprehensive insights into large datasets. It consists of three layers: batch, speed, and serving. Batch processing handles historical data, while real-time processing manages incoming data streams. These two layers are then merged at the serving layer to provide a unified view of the data. Lambda Architecture is known for its fault tolerance, scalability, and ability to handle complex analytics tasks. It is commonly used in applications where data consistency and reliability are critical, such as big data analytics platforms.

CQRS (Command Query Responsibility Segregation)

CQRS is a software architectural pattern that separates the responsibility for handling commands (write operations) from queries (read operations) in an application. This separation allows for more flexibility in designing and optimizing the components responsible for these operations. In a CQRS architecture, commands update the application's state, while queries retrieve data from that state. This separation simplifies scaling and tuning each aspect independently, leading to improved performance and responsiveness. CQRS is often employed in complex and high-throughput systems, such as financial applications and e-commerce platforms.

Event Sourcing

Event Sourcing is an architectural pattern used to maintain the state of an application by capturing all changes to its data as a sequence of immutable events. These events represent state transitions and are stored in an event log, allowing the application to be reconstructed at any point in time by replaying the events. Event Sourcing enables a complete audit trail of data changes, provides temporal querying capabilities, and supports scalability and fault tolerance. It is particularly beneficial in domains where data lineage and historical analysis are essential, such as financial systems, healthcare applications, and version control systems.