Further Reading

This book draws on three different kinds of source: R teaching, data architecture, and the official documentation of systems DataRaft can integrate with.

Teaching R systems

R for Data Science is the main reference for progressive, workflow-first R teaching (Wickham et al. 2023).

Tidy Modeling with R is the model for teaching both a methodology and a family of cooperating R packages without turning the book into function-by-function documentation (Kuhn and Silge 2022).

The Posit skills repository documents current R package development, testing, and Quarto authoring practices used while preparing this project (Posit 2026b, 2026a).

Architecture

The Rise of Logical Data Management motivates the broader architectural progression from physical integration toward a logical access and governance layer (Gardner 2025). DataRaft applies only a narrower R-first interpretation of that lesson.

For data mesh as an organizational and product-oriented architecture concept, see Dehghani (Dehghani 2022).

Interoperability and infrastructure

For contracts, see the Open Data Contract Standard project (Bitol 2026).

For external lineage events, see OpenLineage (OpenLineage Project 2026). For metadata catalog integration, see OpenMetadata (OpenMetadata 2026).

For the SQL transformation ecosystem that the experimental DataRaft bridge integrates with, see dbt documentation (dbt Labs 2026).

For the local analytical database and lake technology used by dataraft.lake, see DuckDB and DuckLake documentation (DuckDB Foundation 2026a, 2026b).

DataRaft itself

For implementation details, prefer the current GitHub repositories and generated pkgdown references over this appendix. DataRaft is still a development package family, and experimental components may evolve faster than this book.

AUDIT.md records the exact source revisions used for this edition.

Bitol. 2026. Open Data Contract Standard. https://bitol-io.github.io/open-data-contract-standard/.

dbt Labs. 2026. Dbt Documentation. https://docs.getdbt.com/.

Dehghani, Zhamak. 2022. Data Mesh: Delivering Data-Driven Value at Scale. O’Reilly Media.

DuckDB Foundation. 2026a. DuckDB Documentation. https://duckdb.org/docs/.

DuckDB Foundation. 2026b. DuckLake Documentation. https://ducklake.select/.

Gardner, Christopher. 2025. The Rise of Logical Data Management: An Essential Data Strategy for Transforming Your Business in the Age of AI. O’Reilly Media. https://www.denodo.com/en/document/e-book/the-rise-of-logical-data-management-oreilly.

Kuhn, Max, and Julia Silge. 2022. Tidy Modeling with r. O’Reilly Media. https://www.tmwr.org/.

OpenLineage Project. 2026. OpenLineage Documentation. https://openlineage.io/.

OpenMetadata. 2026. OpenMetadata Documentation. https://docs.open-metadata.org/.

Posit. 2026a. Posit Development Skills. https://github.com/posit-dev/skills.

Posit. 2026b. Testing r Packages with Testthat. https://github.com/posit-dev/skills.

Wickham, Hadley, Mine Çetinkaya-Rundel, and Garrett Grolemund. 2023. R for Data Science. 2nd ed. O’Reilly Media. https://r4ds.hadley.nz/.