# Authoritative Readings and Resources

These resources extend the supplied learning chapters in **AINS6006 Big Data Management for AI Applications**. They were selected because they are primary standards, official documentation, open textbooks, or authoritative institutional guidance—not unsourced link lists.

## How to Read Them

For each module, read the supplied chapter first. Then use the two linked resources at the end of that chapter to test terminology, compare the course's worked example with an authoritative treatment, and identify one point that should change or qualify your recommendation. Students are not expected to read every linked document cover to cover.

### 1. [Apache Spark Documentation](https://spark.apache.org/docs/latest/)

Distributed processing, SQL, streaming, and operations.

**Use with:** Data architectures for AI, Distributed processing and scale, Metadata, lineage, and provenance, AI data platform readiness review.
### 2. [Apache Kafka Documentation](https://kafka.apache.org/documentation/)

Event streaming architecture and delivery semantics.

**Use with:** Data architectures for AI, Pipelines, orchestration, and quality, Metadata, lineage, and provenance, Cloud integration and cost control.
### 3. [NIST Privacy Framework](https://www.nist.gov/privacy-framework)

Privacy risk management for data-intensive systems.

**Use with:** Pipelines, orchestration, and quality, Storage, indexing, and retrieval, Cloud integration and cost control, Security and access governance.
### 4. [W3C Data on the Web Best Practices](https://www.w3.org/TR/dwbp/)

Metadata, provenance, access, and data-quality practices.

**Use with:** Storage, indexing, and retrieval, Distributed processing and scale, Security and access governance, AI data platform readiness review.

## Source-Use Standard

Assignments should distinguish among measured notebook evidence, course-provided synthetic evidence, claims supported by these sources, and the student's own professional judgment. Cite the specific page, section, control, or documentation topic used; a bare homepage link is not adequate evidence.