by Stephen Swoyer

Increasingly, companies of all sizes expect to be able to develop new business use cases, create new products, enter into new markets, and pursue other strategies that are dependent on access to real-time data. This is the remit of stream processing, a new data processing paradigm that gives businesses a scalable, reliable, low-latency solution for accessing and analyzing data in real-time.

This paper explores the transformative uses of stream-processing. It describes how stream processing supports a data-in-motion paradigm that differs radically from the legacy data-atrest paradigm. It outlines the reference components of the combined open-source technology that underpins stream-processing: a New Stack, comprising Apache Kafka, Apache Spark, and Apache Cassandra.

In summary, the New Stack comprises a simple and cost-effective solution for stream processing that is designed to integrate non-disruptively with an organization’s extant IT infrastructure.