Lambda Architecture vs Kappa Architecture
Are you struggling to choose between Lambda and Kappa architecture for your big data processing? Don't worry; you are not alone! In this post, we'll break down the differences between the two and help you make an informed decision.
What are Lambda and Kappa Architecture?
Lambda and Kappa Architecture are big data processing solutions that aim to provide real-time data processing with fault-tolerance and scalability. They both offer efficient ways to handle big data workloads while providing reliable and consistent outputs.
Lambda Architecture
Lambda Architecture was first introduced by Nathan Marz in 2015 to address the limitations of batch processing. It consists of three layers:
- Batch layer
- Speed layer
- Serving layer
The batch layer handles large volumes of data at rest and generates batch views. The speed layer, on the other hand, handles large volumes of real-time streaming data and generates real-time views. The serving layer merges the results from both layers and provides a comprehensive view of the data.
Kappa Architecture
Kappa Architecture, introduced by Jay Kreps in 2014, is an evolution of Lambda Architecture. It eliminates the batch layer and only uses a single stream processing layer. It uses an append-only log that can replay old data, creating new views for retrieving historical data.
How Do They Differ?
The primary difference between Lambda and Kappa architecture is their approach to batch processing. Lambda Architecture includes batch processing for historical data, while Kappa Architecture does not.
Lambda Architecture is reliable for batch processing, as it can process data at any volume, and the batch processing time can be adjusted according to our needs. However, it can sometimes lead to discrepancies between real-time and historical data.
Kappa Architecture, on the other hand, processes data in real-time only. Its append-only log allows for automatic replay of real-time data, which makes it easier to update real-time results. However, Kappa Architecture's difficulty in managing historical data increases as the volume of data increases.
Which One Should You Choose?
The choice between Lambda and Kappa Architecture depends on the specific needs of your organization. If retrieving historical data is critical and there is no restriction on batch processing, Lambda Architecture is a more suitable choice. However, if you are processing real-time data and dealing with continuously updating views, Kappa Architecture is a better fit.
Conclusion
In the end, choosing between Lambda and Kappa is not a trivial task, and both architectures can provide efficient ways to process big data. It ultimately depends on the specific needs of an organization, and sometimes it can be a hybrid solution of the two.
We hope that with this post, we've provided you with some valuable insights and helped you make an informed decision. If you need further guidance, don't hesitate to contact us.
References
- Nathan Marz, "Lambda Architecture", https://lambda-architecture.net/, accessed May 24, 2021.
- Jay Kreps, "Questioning the Lambda Architecture", https://blog.acolyer.org/2015/06/08/the-truth-about-streaming-and-batch-processing/, accessed May 24, 2021.