This course develops the data infrastructure required for machine learning, deep learning, foundation models, retrieval systems, and agents. Learners design batch, distributed, streaming, feature, training data, retrieval, RAG, knowledge-graph, and memory pipelines. The course emphasizes data quality, provenance, labelling, synthetic data, contamination control, metadata, lineage, privacy, governance, and scalable knowledge access. Building on the agent memory and retrieval design patterns introduced in TECH 502 Agentic AI, Multi-Agent Systems and Orchestration, this course engineers the underlying retrieval, knowledge-graph, and memory systems that production agentic applications depend on.