What are the common data processing architectures and what are the pros and cons of each?

Lambda Architecture

Lambda architecture starts with a messaging layer where stream data is queued for processing. Often uses Kafka as a messaging layer.

The data flow is then split into two parallel paths:

  • Batch layer - handles data in files or other batch processes
  • Streaming layer (also known as speed layer) - can be consumed instantly with an API

Both layers then land in a serving layer (often a data warehouse) where analytics processes can access the data.

Lambda Architecture Flow

Data Processing Steps:

  1. Data Sources → Kafka/Messaging Layer
  2. Data Split → Two parallel paths:
    • Batch Layer: For historical data
    • Speed Layer: For real-time data
  3. Merge Results → Serving Layer (Data Warehouse)
  4. Final Output → Analytics & Queries

Advantages:

  • Can use batch processing for reliable, comprehensive data processing
  • Can use streaming only when needed for real-time requirements
  • Enables fast access when needed but keeps the reliability of batch processing
  • Batch process can do thorough validation and cleaning

Disadvantages:

  • Complexity: Need to maintain two separate processing pipelines
  • Timing challenges: Data timing is more difficult to manage as some processes run daily and others in real-time
  • Data consistency: Potential for discrepancies between batch and streaming results

Kappa Architecture

In Kappa architecture, the message layer coordinates everything. All data gets passed through the speed layer as it’s received and lands directly in a serving layer.

Kappa Architecture Flow

Data Processing Steps:

  1. Data Sources → Kafka/Messaging Layer
  2. Single Stream → Speed Layer (Stream Processing)
  3. Real-time Processing → Serving Layer (Data Warehouse)
  4. Final Output → Analytics & Queries

Historical Data Handling:

  • Historical Data → Replay from Kafka → Speed Layer

Advantages:

  • Fast processing: Everything is handled in real-time
  • Simpler architecture: Fewer components to maintain and debug
  • Consistency: Single processing path eliminates data discrepancies
  • Scalability: Easier to scale a single processing pipeline

Disadvantages:

  • Implementation challenges: Difficult to achieve when files are used to transfer data
  • Legacy compatibility: Legacy systems often can’t stream data
  • Historical data: Requires replay capabilities from message queue for historical analysis

When to Use Each Architecture

Choose Kappa if:

  • All your data sources can stream data
  • You need real-time processing for all use cases
  • You want a simpler, more maintainable architecture

Choose Lambda if:

  • You have batch data sources that can’t stream
  • You need the reliability and thoroughness of batch processing
  • You’re dealing with legacy systems that require batch processing