Building a scalable distributed data platform using lambda architecture - Mehta, Dhananjay (Kansas State University, 2017)
Data is generated all the time over Internet, systems sensors and mobile devices around us this is often referred to as ‘big data’. Tapping this data is a challenge to organizations because of the nature of data i.e. velocity, volume and variety. What make handling this data a challenge? This is because traditional data platforms have been built around relational database management systems coupled with enterprise data warehouses. Legacy infrastructure is either technically incapable to scale to big data or financially infeasible. Now the question arises, how to build a system to handle the challenges of big data and cater needs of an organization? The answer is Lambda Architecture. Lambda Architecture (LA) is a generic term that is used for scalable and fault-tolerant data processing architecture that ensures real-time processing with low latency. LA provides a general strategy to knit together all necessary tools for building a data pipeline for real-time processing of big data. LA comprise of three layers – Batch Layer, responsible for bulk data processing, Speed Layer, responsible for real-time processing of data streams and Service Layer, responsible for serving queries from end users. This project draw analogy between modern data platforms and traditional supply chain management to lay down principles for building a big data platform and show how major challenges with building a data platforms can be mitigated. This project constructs an end to end data pipeline for ingestion, organization, and processing of data and demonstrates how any organization can build a low cost distributed data platform using Lambda Architecture.
Keywords: Big data; Hadoop; Data supply chain; Spark; Map Reduce; Lambda architecture