Modern data architectures (ETL/ELT)
Agents are only as trustworthy as the data they act on. This lesson covers the modern data architectures that move and shape enterprise data, and why those choices determine how AI-ready your organization is.
What you will learn
- The difference between ETL and ELT, and when to use each.
- Batch versus streaming integration and their trade-offs.
- How architecture choices shape downstream AI-readiness.
ETL versus ELT
Both patterns move data from source systems into a destination, but they differ in where transformation happens.
- ETL (Extract, Transform, Load) transforms data before loading it into the target. Good when the target is a structured warehouse with a fixed schema, or when you must cleanse/mask data before it lands.
- ELT (Extract, Load, Transform) loads raw data first, then transforms it inside the target (typically a cloud data warehouse or lakehouse). Good for scale and flexibility: you keep the raw data and can reshape it for new use cases later.
Rule of thumb
Batch versus streaming
- Batch processes data on a schedule (hourly, nightly). Simple and cost-effective when latency of minutes or hours is acceptable.
- Streaming processes events continuously as they arrive. Necessary when agents must react to fresh state (for example, an inventory or fraud signal).
Most enterprises run a mix: batch for historical/analytical loads and streaming for real-time signals.
Why this matters for agents
An agent grounding its answers (see Track 1’s RAG lesson) needs data that is timely, consistent, and reshaped for retrieval. The integration pattern you pick upstream decides whether that data arrives fresh enough and clean enough to be useful. MuleSoft’s integration platform provides the connectors and pipelines to implement these patterns (MuleSoft documentation).
Next steps
Continue to Master Data Management (MDM) fundamentals to see how enterprises turn many conflicting records into a single trusted view.