• Ashitosh Chitnis

Abstract Enterprise data from SAP systems that continues to expand rapidly creates difficulties for organizations to maintain data quality together with consistency and governance standards. The peta-byte scale data management requires advanced approaches instead of traditional rule-based methods because they fail to maintain data integrity effectively. The research analysis delves into machine learning (ML) automation of data quality management through its detection of anomalies and monitoring of schema changes and its implementation of autorepair data pipelines. Data precision and completion together with reliability improve through the proposed ML-driven framework which reduces the need for human involvement. Results from experimental tests demonstrate that ML-driven data quality management lowers system errors and creates higher operational efficiency than standard methodologies. The study produces knowledge about how to implement real-time and automated data governance within SAP-driven data lakehouses that leads enterprises to improve their analytics capabilities and decision-making processes.

Machine Learning-Based Data Quality, SAP-Driven Data Lakehouses, Big Data Management, Automated Data Governance, Scalability in Data Lakes

"Scaling Trust: Machine Learning for Data Quality in PetaByte Scale SAP-Based Data Lakehouses", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.10, Issue 3, page no.i663-i677, March-2023, Available :http://www.jetir.org/papers/JETIR2303894.pdf

"Scaling Trust: Machine Learning for Data Quality in PetaByte Scale SAP-Based Data Lakehouses", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.10, Issue 3, page no. ppi663-i677, March-2023, Available at : http://www.jetir.org/papers/JETIR2303894.pdf