What are the differences between Lambda and Kappa architecture?
An example of an AI chatbot conversation about Lambda vs Kappa architecture in data processing.
What are the differences between Lambda and Kappa architecture?
Lambda architecture is a data processing architecture that combines batch and real-time processing, while Kappa architecture simplifies this by using only real-time processing.
Can you explain how data is processed in each architecture?
In Lambda, data is processed in two layers: the batch layer for historical data and the speed layer for real-time data. Kappa processes data in a single stream, focusing on real-time processing.
Which architecture is better for large-scale data applications?
It depends on the use case. Lambda is better for applications needing both batch and real-time processing, while Kappa is simpler and may be more efficient for purely real-time applications.
What are some use cases for each architecture?
Lambda is great for analytics and reporting, while Kappa is ideal for real-time monitoring and event-driven applications.
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