• [CCT09] James Cheney, Laura Chiticariu, and Wang Chiew Tan. Provenance in databases: Why, how, and where. Found. Trends Databases, 1(4):379–474, 2009.
• [GGSS21] Stefan Grafberger, Shubha Guha, Julia Stoyanovich, and Sebastian Schelter. MLINSPECT: A data distribution debugger for machine learning pipelines. In Guoliang Li, Zhanhuai Li, Stratos Idreos, and Divesh Srivastava, editors, SIGMOD ’21: International Conference on Management of Data, Virtual Event, China, June 20-25, 2021, pages 2736–2739. ACM, 2021
• [LFS+23] Raoni Louren ̧co, Juliana Freire, Eric Simon, Gabriel Weber, and Dennis E. Shasha. Bugdoc. VLDB J., 32(1):75–101, 2023.
• [ZAI19] Nan Zheng, Abdussalam Alawini, and Zachary G. Ives. Fine-grained provenance for matching & ETL. In 35th IEEE International Conference on Data Engineering, ICDE 2019, Macao, China, April 8-11, 2019, pages 184–195. IEEE, 20
• AWS Best practices for performance tuning AWS Glue for Apache Spark jobs, AWS Prescriptive Guidance, here

Keep in mind that for performing data analytics you are willing to make sense of data and this implies acquiring Data Literacy. Have a look at this reference for background. 

Michel Bowen, Anthony Bartley, The Basics of Data Literacy: making your students (and you!) make sens of data, NST Press, Arlington Virginia