Lambda Architecture #

Introduction #

Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch and stream-processing methods. This approach aims to balance latency, throughput, and fault-tolerance. Let’s explore the key components of lambda architecture:

1. Batch Layer:

   • The batch layer precomputes results using a distributed processing system capable of handling large data volumes.
   • It ensures perfect accuracy by processing all available data when generating views.
   • Output is stored in a read-only database, with updates replacing existing precomputed views.

2. Speed Layer:

   • The speed layer processes data streams in real time without fix-ups or completeness requirements.
   • It minimizes latency by providing immediate views into the most recent data.
   • These views may not be as accurate or complete as batch layer views but are available promptly.

3. Serving Layer:

   • The serving layer responds to user queries by combining batch and speed layer outputs.
   • It provides comprehensive and timely views of data for various use cases.

Learning Resources #

Articles #

• Lambda Architecture: Design Simpler, Resilient, Maintainable, and Scalable Big Data Solutions
• Lambda Architecture | Snowflake
• What Is Lambda Architecture? - Databricks