Lambda Architecture

1. Introduction

The Lambda Architecture is a data processing architecture designed to provide a balance between low-latency and high-throughput processing of large datasets. It combines batch processing and real-time processing to deliver both comprehensive and up-to-date analytics.

2. Key Concepts

• Batch Layer: Responsible for managing the master dataset and performing batch computations.
• Speed Layer: Handles real-time data processing and generates immediate results.
• Serving Layer: Merges batch and real-time results to provide a unified view.
• Data Sources: Various data sources such as databases, logs, and event streams.

3. Architecture Overview

graph TD;
    A[Data Sources] --> B[Batch Layer];
    A --> C[Speed Layer];
    B --> D[Serving Layer];
    C --> D;
            

4. Use Cases

• Real-time analytics for tracking user behavior.
• Fraud detection in transaction systems.
• Recommendation engines that require both historical and real-time data.

5. Best Practices

Implementing Lambda Architecture effectively requires adherence to certain best practices:

• Keep your batch and real-time processing systems decoupled.
• Ensure data consistency between batch and real-time layers.
• Use schema evolution to manage changes over time.

6. FAQ