Lambda Data Architecture for Timely Insights

Core Concepts of the Data Lake Driven by Lambda Architecture - While the world seems to have come to a standstill in the wake of the COVID-19 pandemic, there is more data clogging the internet pipelines than ever before as working from home becomes the new normal in most industries. With more data comes the need to store, analyze and develop relevant insights – this is where big data technology comes into play.

As a concept, a big data platform is designed to accommodate data that has three distinct attributes of high volume, high variety and high velocity. This architecture has matured over the years with Hadoop becoming a primary standard for implementing big data at most companies. However, with a marked increase in data content, which we are observing during the COVID-19 pandemic, some big data platforms are starting to show signs of strain as they reach peak capacity. Thus, users who may have been previously used to quick access to data and insights are having to wait longer to get their hands on the information they seek, a phenomenon known “latency.”

Lambda architecture is a solution that was designed to solve the sort of data problems that the world is facing today. Created by James Warren and Nathan Marz, Lambda architecture optimizes the balance between working with large datasets without sacrificing the need for interaction with data at high velocity. This architecture compensates for the traditional big data architectures, which are built to handle historic data quite efficiently, but tend to exhibit latency when it comes to interacting with real time data. Lambda architecture allows users to take business intelligence and analytics to the next level by providing access to blended historic data and real time data.

Lambda architecture also enables the user to seamlessly query both real-time and historical data. To gain insight into the historical data movements, the information is sent to the data store. The principle of this architecture is based on Lambda calculus, hence the name Lambda architecture. The architecture is designed to work with immutable datasets, especially for its functional manipulation. The architecture has also solved the problem of computation of arbitrary functions. In general, a problem can be segregated into three layers:

  1. Batch
  2. Speed
  3. Serve

Here, the batch layer is same as the traditional data lake layer where historical data is collected and used for analytics and served using the serve layer. The speed layer comes into action when real time data needs to be used and processing occurs before serving the results via the presentation layer, along with the batch data.

As the world moves from fast analytics to real time analytics, Lambda architecture should certainly be top of mind for companies to consider in order to expand their capabilities with big data. Pandemic or not, there is always a need for real time data and analysis, and Lambda architecture brings us a step closer in that direction.

For more information, reach out to us at analytics@dhg.com.

ABOUT THE AUTHORS

Amit Arya
Chief Data Officer
amit.arya@dhg.com

Sujay Somasekhar
Senior Manager
DHG Data Analytics

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