Lambda architecture is the favored model for data processing that unites traditional batch processing and stream processing methods into the same framework. It has been the standard approach in big data to balance latency, throughput, and fault tolerance. In gest, batch processing is carried out to find the old dataset’s behavioral pattern in a batch layer to obtain accurate and comprehensive views of the data on a daily or hourly basis. Simultaneously, real-time stream processing is carried out to provide several views from the online data.
The lambda architecture is further divided into three major sections to compute the distributed data in real-time.
Batch Layer in the Lambda Architecture
Initially, the data streams are obtained from various sources split into two parts and are ingested into the batch layers and speed layer simultaneously. When the new data is being streamed, it gets deposited into this layer and becomes part of the master dataset. It is also called a ‘Data Lake, ‘ which acts as a historical archive to hold all the data. The data in this layer is unchangeable except for adding up the new information. Then, the data are ingested in large batches in specific schedules to generate the various reports. The schedules can be set either daily or on an hourly basis as per the requirements. The output is typically stored in a ready only database by replacing the precomputed views.
Speed Layer in the Lambda Architecture
This layer is responsible for processing the data’s continuous stream by not caring if data is incomplete or needs some fixation. It does not precompute the entire data; instead, it undergoes the incremental process to store and update the data’s real-time views. This layer tries hard to provide real-time views of the most frequent data by minimizing the latency because older data is observed in the batch layer. The views produced from this layer may not be complete and have a specific time to compute as the batch layer because it really works with the live data.
Serving Layer in the Lambda Architecture
The combined batch views and the real-time views, which are the output of the batch layer and speed layer, respectively, are forwarded into this layer. The result is stored in the specialized distributed database, which can be queried by the user in low latency and ad-hoc manner. This layer is responsible for responding to any of the queries by providing the results of the calculations.
Benefits
The following features made the lambda architecture more beneficial to process big data:
Data Consistency
Lambda architecture solves the problem of data inconsistency in the distributed system. The sequentially processed data and the indexing process ensure the consistent data replica in batch and speed layers.
Scalability
This architecture is highly scalable, which allows to add or remove several nodes and doesn’t care how much data needs to be processed
Fault tolerance
It is also a fault tolerance for any of the hardware or software failure. There is another node that continues the workload for any failure and doesn’t impact the system’s performance.
Business Agility
This architecture can process the data in real-time, which helps the various companies make crucial decisions.
Applications of the Lambda Architecture
Twitter, which is regarded as a microblogging system, uses the lambda architecture to understand the various tweets’ sentiments so far.
Crashlytics deals with mobile analysis to produce meaningful insights with the use of lambda architecture.
A popular forum stack overflow that deals with the questions and answers also use the lambda architecture. Here, batch views are used to find the analytical results for voting.
