Streaming ETL pipelines enable three categories of real-time use cases: human insights, automated insights and automated actions.
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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 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 replaces the inefficient method of 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 ELT.
Enterprises adopt streaming ETL pipelines to enable three categories of real-time use cases: human insights, automated insights, and automated actions. Streaming ETL pipelines support these use cases by integrating with BI products, embedded BI features within applications, ML models, business-monitoring tools, and intelligent process automation (IPA) workflows.
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. The most effective streaming ETL initiatives will take a cross-functional approach to learning and collaboration.
Report written by Kevin Petrie of Eckerson Group and sponsored by Equalum
The Rise of Streaming ETL
Transforming Data in Flight for Real-Time Insight and Action
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