In recent times, there has been a surge in the usage of lambda architecture. Mostly, it’s seen beside real-time analytics and big data. So you might have the question, what exactly is a lambda architecture?
Don’t worry! In today’s article, we’re going to discuss what lambda architecture is. We’re also going to discuss its various components.
With that said, let’s discuss what lambda architecture is!
Introduction To Lambda Architecture
Lambda architecture is a type of data processing architecture. It was mainly designed for handling huge quantities of data while taking the help of both real-time processing and batch processing methods.
Lambda architecture intends to support fault-tolerance, output, and latency with the help of batch processing. Lambda architecture also provides an accurate and comprehensive showing of batch data. Another great thing about it is, it provides simultaneous real-time stream processing to its users.
Lambda architecture allows both real-time and batch processing to be joined into a single processing unit. The surge of lambda architecture is interrelated to the development of real-time analytics and big data.
Lambda architecture is based on a data model that has an unchangeable and add-only data source. This data source is used as a system record. This is directed to the processing of timestamped events. And instead of overwriting these events, they are only added on.
Lambda Architecture Layers
Lambda Architecture consists of 3 main layers. They are:
- Batch Layer
- Serving Layer
- Speed Layer
The batch layer predetermines the result in a system. It uses a distributive processing system that handles huge sums of data. The goal of the batch layer is to process the data available in the system while generating views.
The batch layer mends any occurring error by recomputing the entire dataset. After recomputing it updates the existing views. The outputted data is then stored in a read-only database. It gets timely updates that replace existing views.
The speed layer has real-time data-processing functionality. It can do such without the urge of completeness or fix-ups. But in this process, the layer has to sacrifice throughput. That’s because the goal of this layer is to reduce latency by providing real-time views of recently available data.
In short, the speed layer fills the gap that happens because of batch layers lag. The speed layer’s views aren’t as accurate as of the ones provided by the batch layer. But the data provided by the speed layer can be acquired almost immediately upon receiving.
Any output provided by the speed and batch layers are kept in the serving layer. And as the name implies, it serves data when requested. It responds to the queries made by pre-built views or pre-computed views from the processed data.
As of late, there has been a shift in how big data computing works. And knowing how technologies such as the lambda architecture functions, is very necessary for the sake of your business.
In this article, we’ve provided you with an introduction to Lambda architecture. Acquiring information from this article will help you greatly when you’re going to deal with lambda architecture.
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