The Data Systems Architect

You can write SQL. This module is about where that SQL actually runs: how real data systems are built, scaled, and defended once one machine stops being enough.

Three cyan panels naming what Module 6 solves: Scale (data outgrows one machine, spread it across a cluster), Architecture (structure dictates capability, relational/JSONB/vector and one stack or many systems), and Trust (data is a liability, keep attackers out and people anonymous).

Figure 1. Three constraints drive system design. Scale: a dataset exceeds one machine, so the system partitions data across a cluster and the engine must match the hardware limits at that scale. Architecture: a data model (relational, JSONB, vector) and a deployment choice (one system or several) determine which features applications can implement. Trust: a large dataset increases exposure, so the system must block attackers and preserve anonymity for people represented in the data.

We follow the scale journey from the smallest sensible setup upward.

Case Study: OpenClaw's Memory → Start with a system you already use: an AI agent. Its memory is a folder of files with a search index, and every trick it plays is an idea from this course.