AI-Ready Data Platforms

Context: FIT3003_MOC · the Data Engineering pipeline extended so its output feeds AI as well as BI · storage layer = warehouse or lakehouse · (W12 emerging-topics deck — not a book chapter)

Quick Revision

• 🎯 Objective: prepare reliable, timely, well-governed data for analytics and AI ➔ the same sources → ingestion → processing → storage chain, plus an AI-preparation layer.
• ⚡ Key Constraint: AI is only as reliable as the data and context supplied to it — generated SQL and conclusions still need validation.

📝 Core

• Six components ➔ sources (relational DBs, apps, documents, logs, sensors, APIs) · ingestion (batch loads, APIs, streaming events) · processing & enrichment (clean, validate, integrate, transform) · storage (warehouses/lakehouses hold curated data; lakes keep raw and unstructured) · AI preparation (organise documents, metadata and features for training, retrieval and inference) · consumption (BI dashboards, ML models, AI apps).
• Cross-cutting ➔ data quality · metadata · lineage · security · monitoring ➔ Data Management and Data Governance.
• Reference layout ➔ sources ➔ streaming pipelines / batch loads ➔ lake & staging (raw datasets) ➔ warehouse & processing (cleanse/normalise ➔ ETL/ELT ➔ dimensional modelling ➔ fact and dimension tables; feature engineering ➔ feature store) ➔ AI & analytics (training, predictive analytics, BI, real-time dashboards, ML serving) ➔ consumption (recommendations, revenue forecasting, customer insights, reporting APIs).
• 1 — Natural-language analytics ➔ users ask in everyday language; AI generates SQL and summarises results ⛔ queries and conclusions require validation.
• 2 — Vector search / semantic retrieval ➔ content becomes numerical embeddings; retrieval returns semantically similar items rather than only exact keyword matches ➔ document search and RAG applications.
• 3 — ML & predictive analytics ➔ historical data predicts outcomes, detects anomalies, classifies events ⛔ needs suitable training data and ongoing evaluation ➔ Machine Learning.
• 4 — Data products & data mesh ➔ curated datasets treated as reusable, documented products; domain ownership within shared governance standards.
• Challenges ➔ privacy · inaccurate outputs · bias · access control · integration complexity · cost.

⚠️ Common Mistakes

• 💡 Treating AI output as an answer ➔ natural-language SQL is a draft; check it against the star schema’s grain before trusting the number.
• 💡 Data mesh ≠ no governance ➔ ownership moves to domains, but within shared standards.

🧠 Active Recall

The warehouse is already clean and dimensional. What does an AI-ready platform add on top, and what stays the bottleneck?

Answer

• Short answer: an AI-preparation layer (documents, metadata, features for training, retrieval and inference) and AI consumers; data reliability stays the bottleneck.
• Why: Same pipeline, extra consumer ➔ sources, ingestion, processing and storage are the unit’s pipeline unchanged — fact and dimension tables still come from dimensional modelling. Garbage in ➔ AI is only as reliable as its data and context, so quality, lineage and security remain cross-cutting duties.