by Jerome Kehrli
Posted on Friday May 04, 2018 at 12:32PM in Big Data
The Lambda Architecture, first proposed by Nathan Marz, attempts to provide a combination of technologies that together provide the characteristics of a web-scale system that satisfies requirements for availability, maintainability, fault-tolerance and low-latency.
Quoting Wikipedia: "Lambda architecture is a data-processing architecture designed to handle massive quantities of data by taking advantage of both batch- and stream-processing methods.
This approach to architecture attempts to balance latency, throughput, and fault-tolerance by using batch processing to provide comprehensive and accurate views of batch data, while simultaneously using real-time stream processing to provide views of online data. The two view outputs may be joined before presentation.
The rise of lambda architecture is correlated with the growth of big data, real-time analytics, and the drive to mitigate the latencies of map-reduce."
In my current company - NetGuardians - we detect banking fraud using several techniques, among which real-time scoring of transactions to compute a risk score.
The deployment of Lambda Architecture has been a key evolution to help us evolve towards real-time scoring on the large scale.
In this article, I intend to present how we do Lambda Architecture in my company using Apache Kafka, ElasticSearch and Apache Spark with its extension Spark-Streaming, and what it brings to us.Read More