Data processing is a key aspect of any data-driven application, as it involves transforming raw data into meaningful and useful information. Data processing can be performed in different ways, such as batch processing, stream processing, or hybrid processing. Batch processing is the process of processing large and fixed batches of data at regular intervals, such as daily or weekly. Stream processing is the process of processing continuous and unbounded streams of data in real time, such as seconds or milliseconds. Hybrid processing is the process of combining batch processing and stream processing to handle both historical and real-time data.
One of the most popular and widely used hybrid data processing architectures is the Lambda Architecture, which consists of three layers: the batch layer, the speed layer, and the serving layer. The batch layer handles the historical data and performs batch processing using frameworks such as MapReduce, Spark, or Hive. The speed layer handles the real-time data and performs stream processing using frameworks such as Storm, Flink, or Spark Streaming. The serving layer combines the results from both layers and provides a unified view of the data to the end-users or applications.
However, the Lambda Architecture has some drawbacks and challenges, such as:
- It requires maintaining two separate code bases for batch processing and stream processing, which can increase development and testing efforts.
- It introduces complexity and latency in synchronizing and updating the results from both layers.
- It may produce inconsistent or inaccurate results due to delays, errors, or duplicates in both layers.
To overcome these drawbacks and challenges, a new data processing architecture was proposed by Jay Kreps, one of the co-founders of Apache Kafka, in 2014. This architecture is called the Kappa Architecture.
What is Kappa Architecture?
Kappa Architecture is a data processing architecture that is designed to provide a scalable, fault-tolerant, and flexible system for processing large amounts of data in real time. Kappa Architecture is based on the idea that all data is treated as a stream, and that both real-time and batch processing can be performed using the same stream processing engine.
The Kappa Architecture consists of two main components:
- Messaging System: This is the system that stores and distributes the data streams from the data sources to the stream processing engine. The messaging system acts as a buffer that can handle high volumes and velocities of data, and also provides fault-tolerance and durability. A common choice for the messaging system is Apache Kafka, which is a distributed and scalable platform for streaming data.
- Stream Processing Engine: This is the system that reads and processes the data streams from the messaging system and produces various outputs or views of the data. The stream processing engine can perform various operations on the data, such as filtering, aggregation, transformation, enrichment, or machine learning. The stream processing engine can also handle both real-time and historical analytics by reading the data streams from different offsets or timestamps.
The following diagram illustrates the Kappa Architecture:
Kappa architecture. Figure courtesy of Ignacio Mulas Viela and Nicolas Seyvet.
Benefits of Kappa Architecture
Here are some of the main benefits of Kappa Architecture:
- Simplicity: Kappa Architecture reduces the complexity of managing multiple layers, frameworks, and systems for data processing. It also reduces the development and testing efforts by using a single code base and framework for stream processing.
- Consistency: Kappa Architecture avoids inconsistencies or inaccuracies in the data due to delays, errors, or duplicates in different layers. It also enables easy debugging and reprocessing of the data by using a single source of truth in the messaging system.
- Scalability: Kappa Architecture can scale horizontally to handle large amounts of data by adding more nodes or machines to each component. Each component can also scale independently according to its workload and requirements.
Challenges of Kappa Architecture
Here are some of the main challenges of Kappa Architecture:
- Performance: Kappa Architecture may have performance issues due to the overhead of streaming all data through the messaging system and the stream processing engine. It may also have latency issues due to network delays or backpressure in the data pipeline.
- Complexity: Kappa Architecture introduces complexity in terms of dealing with trade-offs between latency, accuracy, and cost for each component. It also requires handling complex scenarios such as out-of-order events, late arrivals, or retractions in the stream processing engine.
Kappa Architecture is an emerging paradigm that represents a high-level software pattern of leveraging streaming data. Kappa Architecture depends upon event streams, or flows of real-time user interaction and device-generated data, to communicate changes to a centralized and immutable (unchangeable) log that represents a single source of truth of all observed data. Kappa Architecture utilizes stream processing engines, or high-performance technologies that continuously process event streams’ data (in real-time), to apply decision logic and provide information to persons and software applications.
Kappa Architecture is a streamlined and simplified alternative to the Lambda Architecture, which is designed to handle both real-time and batch processing with a hybrid approach. Kappa Architecture has many benefits, such as simplicity, consistency, and scalability. However, Kappa Architecture also has some challenges, such as performance and complexity.
If you want to learn more about Kappa Architecture, you can check out the following resources:
Questioning the Lambda Architecture | Jay Kreps https://hazelcast.com/glossary/kappa-architecture/