The Rise of Streaming ETL: Transforming Data in Flight for Real-Time Insight and Action
Kevin Petrie
July 15, 2021
The confluence of digital transformation, data democratization, and data modernization create the opportunity for real-time data integration, which in turn drives real-time business insights and action. Streaming extract, transform, and load (ETL) has emerged as the most efficient, effective method of real-time data integration. As the name suggests, this method extracts streams of live data updates, transforms them in flight, and loads them in real time to analytics targets. Streaming ETL achieves higher efficiency and lower cost than traditional batch ETL, which repeatedly processes full batches of data rather than just focusing on real-time updates. Streaming ETL also can reduce the latency of basic data transformations compared with streaming extract, load, and transform (ELT).
This report examines why and how enterprises adopt streaming ETL pipelines to enable three categories of real-time use cases: human insights, automated insights, and automated actions. Streaming ETL pipelines enable human insights by providing transformed data to business intelligence (BI) products or embedded BI features within applications. They help create automated insights by providing transformed data to machine learning (ML) models or business monitoring tools that then predict or recommend actions for humans to take. Finally, streaming ETL pipelines support the next logical step of automated action by providing transformed data to ML models or intelligent process automation (IPA) workflows that automatically trigger actions.
Streaming ETL offers new promise to enterprises that seek to capitalize on the short-lived value of business events in our accelerating world. Data teams that standardize their data pipelines, incrementally adapt existing architectures, and select simple ML approaches are best poised to scale with the needs of the business. Perhaps most importantly, the most effective teams will take a cross-functional approach to learning and collaboration.
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