Kappa Data Architecture

Kappa data architecture is a software architecture that is the simplification of Lambda architecture. In Kappa architecture, the data flows in the form of streams, unlike Lambda architecture, where the data is processed in batches.

This architecture is built on a streaming architecture where incoming data streams are initially stored in a message engine like Apache Kafka. The data will then be read by a stream processing engine, formatted for analysis, and stored in an analytics database for end users to query.

Since it can manage both real-time stream processing and historical batch processing using the same technology stack as the Lambda Architecture, the Kappa Architecture is seen as a more straightforward option.

For large-scale analytics, both designs require the storage of historical data. The “human fault tolerance” problem, where errors in the processing code can be fixed by upgrading the code and rerunning it on the historical data, can likewise be addressed by both systems.

As a result of treating all data in the Kappa Architecture as though it were a stream, the stream processing engine serves as the only data transformation engine. Streaming delivers low-­latency, near-­real-­time results. It uses incremental algorithms to perform updates, which saves time but sacrifices accuracy.

Pros and Cons of Kappa architecture:

• Reprocessing is only necessary when the code alters.
• Fixed memory can be deployed with it.
• It can be applied to systems that are scalable horizontally.
• Since machine learning is being done in real-time, fewer resources are needed.

Cons:

• The lack of a batch layer could lead to errors in data processing or database updates, necessitating the use of an exceptional manager to reprocess the data or perform reconciliation.
• Not easy to implement.

Conclusion:

The important motivation for inventing the Kappa architecture was to avoid maintaining two separate code bases for the batch and speed layers. The key idea is to handle both real-time data processing and continuous data reprocessing using a single stream processing engine.