Kappa Architecture vs Lambda Architecture – Your Easy Architecture Guide in 2024 (Part 4)

Todays world is crazy, everybody is talking about data. Data is the ultimate king right? But we need processing more and more data as we progress. But the question is how? 

We will take two contestants as our answer solution. Lambda vs Kappa architecture. These two are a bit different than the morden software architecture patterns. Have you heard of it? Great! You are real geek bro trust me! But if by any chance you dont know about these, don’t worry, we will discuss it in details, means, I got you covered!

But you may get confused which one to use in what situation. In this article, we are going to compare Kappa architecture vs Lambda architecture, their benefits, flaws everything! Let’s get into it!

Before we see the difference, let’s understand what there architectures are!

Kappa architecture

Kappa Architecture makes the data processing in real-time eliminating a separate batch layer. Usually having a separate batch layer is costly in terms of latency and delay in the data processing. In software architecture, all the processing happens in a single data stream. The data gets stored on the go and becomes available for real-time query.

Kappa Architecture, processes as soon as it comes to the system making it super quick and a lot faster in response times. It just needs a single stream to do all the processing. But in cases where we need to process or analyze historical data, we can treat the historical data as another stream of data. By doing so, we can eliminate dedicated batch layers from the system.

So, in the debate of Kappa architecture vs Lambda architecture, Kappa Architecture stands out as highly scalable and faster system, for processing big volumes of data, in a real-time manner.

Layers of Kappa Architecture

Kappa Architecture is designed to be highly scalable, making it ideal for processing large volumes of data in real time. To do so there are several layers, in this architecture that are there. Let’s break them down.

1. Streaming Layer: The Kappa Architecture is typically built around Apache Kafka along with a high-speed stream processing engine. Here Apache Kafka acts as a data buffer.
2. Stream Processing Engine: A Stream processing engine, is a setup that can read messaging systems like Kafka, and using the business logic it transforms the data in the desired format. Once the processing is done, we can store the data in the database but can achieve a real-time or near-real-time response.
3. Analytics Database: Once data is stored in an analytics database Snowflake, BigQuery, or maybe RedShift, we can have both near real-time database and historical analytics.
4. Serving Layer: The Serving Layer in Kappa Architecture is responsible for serving the computed data from the streaming layer to the users. It provides optimized responses to the queries made to the analytics database but this layer is not responsible for storing data in the database.

Benefits of Kappa Architecture

1. Simplicity: Kappa Architecture simplifies the overall setup just by eliminating the batch layer from the system. By doing so, it makes things easy to build and maintain big data systems.
2. Unified Technology Stack: Unification of the batch layer and the steam layer simply means a single technology stack, plus it reduces complexity and is more straightforward.       
3. Faster Processing: The main idea behind removing the batch processing layer is to do the data processing in a much faster and improved way possible.
4. Cost Savings: Just by eliminating the batch processing layer, we can significantly reduce the infra cost and laggy performance.
5. Easier Maintenance: The unified codebase in Kappa Architecture simplifies the testing, debugging, and over-maintenance process.

Lambda architecture

Lambda Architecture is a combination of batch processing and stream processing to handle massive amounts of data. In generic setups, this architecture has a batch layer, a speed layer (aka real-time layer), and a serving layer. Lambda Architecture is used for querying quickly access real-time data. However, this architecture is criticized for being complex and limiting flexibility as batch processing and streaming require different codebases that must be kept in sync.

Layers of Lambda Architecture

Lambda architecture is comprised of three layers. Let’s discuss them.

1. Batch Layer: This layer is responsible for processing huge chunks of data in intervals. This layer receives data in an append-only manner from different sources like databases, 3rd party APIs, files, etc. Once the data gets processed, it gets stored in the analytical database.
2. Speed (or Real-Time) Layer: This layer is designed to process data from real-time data streams and generate views based on the incoming data. It is also responsible for handling low-latency queries and is used in real-time analytics.
3. Serving Layer: The serving layer is the user-facing layer, which queues the batch views prepared by the batch processing layer, and creates indexes for them. This layer is to make the data queryable in a very short period. This layer stores and combines batch layer output with speed layer output.

Benefits of Lambda architecture

1. Scalability: Lambda architecture allows independent scaling of each of the layers. However, the scaling strategy of each layer depends on the data volumes.
2. Fault-Tolerance: Any error that may happen in the stream layer can be handled gracefully by simply reprocessing the data from the batch layer again.
3. Flexibility: Each layer can use different tools and frameworks to process the data.
4. Real-Time and Historical Insights: The batch layer keeps the data ready for processing. It makes the data available for both historical and near real-time insights for all the data sources.
5. Automated High Availability: We can configure the system in such a way that it can recover any software level failure without human intervention, proving automated high availability.

Kappa architecture vs Lambda architecture

Lambda Architecture	Kappa Architecture
Uses two separate data processing systems for handling different types of workloads: a batch processing system and a stream processing system.	Involves a single stream processing engine for handling real-time data processing and continuous data reprocessing.
Involves a batch layer, a speed layer (or stream layer), and a serving layer.	Avoids maintaining two different code bases for batch and speed layers, as in the Lambda Architecture.
Suitable for scenarios where both batch and real-time processing are required.	Designed for real-time processing of distinct events and can be deployed in scenarios where multiple data events or queries are logged in a queue to be catered against a distributed file.
Can be more reliable in updating the data lake and is suitable for scenarios where expensive hardware gets used.	Not a substitute for the Lambda Architecture, but rather serves different use cases like IoT Data Processing, Edge computing, etc.

Conclusion

Wow, you are reading this. Seems like you are a real geek (unlike me) who loves to read and learn! We have seen so far that each architecture comes with its own set of challenges and advantages. Understanding these nuances is vital for making informed decisions in architecting robust data processing systems.

References

1. https://www.linkedin.com/advice/1/what-benefits-using-lambda-architecture-pattern
2. https://towardsdatascience.com/a-brief-introduction-to-two-data-processing-architectures-lambda-and-kappa-for-big-data-4f35c28005bb
3. https://www.qlik.com/blog/lambda-or-kappa-the-need-for-a-new-data-processing-architecture
4. https://www.sqlservercentral.com/articles/advantages-of-kappa-architecture-in-the-modern-data-stack

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