Understanding Lambda Architecture: A Comprehensive Guide
Written on
Chapter 1: Introduction to Lambda Architecture
Lambda architecture serves as a framework for processing vast amounts of data, commonly referred to as "Big Data." It integrates both batch and stream processing techniques, offering a hybrid solution. Introduced by Nathan Martz in 2014, this architecture has significantly benefited data analysts and business professionals by providing immediate access to processed data from the speed layer alongside more complex datasets from the batch layer.
The structure of Lambda architecture consists of three main layers:
1. Batch Layer
The batch layer is tasked with storing extensive volumes of data. Data is continuously fed into this system, which processes it in batches, typically on a set schedule. This layer updates the master dataset with the results of the batch processing. Two key functions of the batch layer include managing the master dataset and pre-computing batch views.
2. Speed Layer
The speed layer focuses on managing large volumes of data that have not yet been delivered in batch view due to the time required for batch operations. It primarily handles recent data, ensuring a complete and timely view of the information.
3. Serving Layer
This layer is responsible for indexing batch views, making them queryable for end users. It allows for low-latency querying and ad-hoc requests, while also consuming real-time data from the speed layer.
Chapter 2: Pros and Cons of Lambda Architecture
In this brief tutorial, the Lambda Architecture is explained in under ten minutes, highlighting its structure and applications in handling big data.
Lambda architecture has notable advantages, including reduced latency due to the indexing of recent data in the speed layer. This capability allows for critical data to be readily available as needed. Furthermore, if the batch layer encounters issues, the speed layer can still process recent data, and the batch layer can be re-executed.
However, this architecture does have its drawbacks. It necessitates maintaining two separate codebases for the batch and stream layers, which adds a layer of complexity to the system.
The second video delves deeper into the Lambda Architecture, explaining its intricacies and real-world applications in the realm of big data.