The Why and How of Streaming-First Data Architectures
Organizations build streaming-first architectures to increase revenue, reduce cost, and control risk. By replacing traditional batch-only ETL processes, data teams can enable real-time decision making & machine learning, improve efficiency, increase scale, and accelerate applications. Many organizations embrace streaming-first architectures as part of broader strategic initiatives that include cloud modernization and data pipeline automation.
To realize these benefits, data teams must carefully scope their use cases and select the right technologies for efficiently manipulating data in motion. They must streamline processes while still integrating heterogeneous end points and flexibly adapting their architecture to address changing business requirements.
This report examines the benefits, challenges, adoption patterns and use cases for a streaming- first approach to data management. It guides data leaders through the capabilities and planning criteria for each architectural component – data sources, collection, transformation, targets and analytics – as well as the role of on-premises, hybrid and cloud infrastructure. Readers will learn guiding principles to navigate their journey to a streaming-first data architecture.