Beyond Big Data: Designing Agentic Data Pipelines for AI Workloads

Source: DZone DevOps

For years, data engineering was built around a familiar idea: ingest everything, store everything, process at scale, and make it available for dashboards, analytics, and reporting. That model worked well for business intelligence and historical analysis.

But AI workloads are changing what data pipelines are expected to do. Modern AI systems do not just consume data in batch. They retrieve, reason, act, monitor outcomes, and adapt in near real time. That shift is why agentic data pipelines are becoming a serious architectural pattern. Instead of moving data passively from source to sink, they actively decide what to retrieve, how to transform it, which tools to call, and when to trigger downstream actions.