Data Governance in Lakehouse

Authors

  • Samarth Shah University at Albany, Washington Ave, Albany, NY 12222, UNITED STATES.
  • Shubham Jain IIT Bombay, INDIA.

DOI:

https://doi.org/10.55544/sjmars.3.5.12

Keywords:

Data governance, lakehouse architecture, metadata management, data quality, access control, policy enforcement, data lineage, compliance, data security, unified data platform, audit trails, regulatory standards, machine learning, data consistency, actionable insights

Abstract

Data governance in a lakehouse architecture is crucial for managing the lifecycle, quality, and security of data across hybrid and scalable environments. The lakehouse model combines the benefits of data lakes and data warehouses, enabling organizations to handle large volumes of structured and unstructured data while ensuring analytical accuracy. However, the seamless integration of diverse data sources introduces challenges related to governance, including compliance, privacy, and data lineage.

This paper explores the principles and practices essential for implementing effective data governance in a lakehouse environment. Key components such as metadata management, data cataloging, access control, and policy enforcement are discussed in detail. The role of automation and machine learning in enhancing governance capabilities, such as detecting anomalies and ensuring compliance with regulatory standards, is also examined.

Additionally, the lakehouse architecture's unified nature demands a governance framework that ensures data consistency while fostering accessibility for both data scientists and business analysts. Strategies for integrating data governance within the lakehouse model, including role-based access, audit trails, and encryption, are presented to mitigate risks and enhance trust in data usage.

By addressing these challenges and opportunities, this paper demonstrates how a robust data governance framework can empower organizations to balance innovation and control. Ultimately, data governance in the lakehouse architecture serves as a foundation for reliable, secure, and actionable insights, driving informed decision-making in complex and dynamic environments.

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[87] Chamarthy, Shyamakrishna Siddharth, Rajas Paresh Kshirsagar, Vishwasrao Salunkhe, Ojaswin Tharan, Prof. (Dr.) Punit Goel, and Dr. Satendra Pal Singh. 2021. Path Planning Algorithms for Robotic Arm Simulation: A Comparative Analysis. International Journal of General Engineering and Technology 10(1):85–106. ISSN (P): 2278–9928; ISSN (E): 2278–9936.

[88] Byri, Ashvini, Nanda Kishore Gannamneni, Bipin Gajbhiye, Raghav Agarwal, Shalu Jain, and Ojaswin Tharan. 2021. Addressing Bottlenecks in Data Fabric Architectures for GPUs. International Journal of Progressive Research in Engineering Management and Science 1(1):37–53.

[89] Byri, Ashvini, Phanindra Kumar Kankanampati, Abhishek Tangudu, Om Goel, Ojaswin Tharan, and Prof. (Dr.) Arpit Jain. 2021. Design and Validation Challenges in Modern FPGA Based SoC Systems. International Journal of General Engineering and Technology (IJGET) 10(1):107–132. ISSN (P): 2278–9928; ISSN (E): 2278–9936.

[90] Joshi, Archit, Raja Kumar Kolli, Shanmukha Eeti, Punit Goel, Arpit Jain, and Alok Gupta. (2021). Building Scalable Android Frameworks for Interactive Messaging. International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET) 9(12):49.

[91] Joshi, Archit, Shreyas Mahimkar, Sumit Shekhar, Om Goel, Arpit Jain, and Aman Shrivastav. (2021). Deep Linking and User Engagement Enhancing Mobile App Features. International Research Journal of Modernization in Engineering, Technology, and Science 3(11): Article 1624.

[92] Mallela, Indra Reddy, Sivaprasad Nadukuru, Swetha Singiri, Om Goel, Ojaswin Tharan, and Arpit Jain. 2021. Sensitivity Analysis and Back Testing in Model Validation for Financial Institutions. International Journal of Progressive Research in Engineering Management and Science (IJPREMS) 1(1):71-88. doi: https://www.doi.org/10.58257/IJPREMS6.

[93] Mallela, Indra Reddy, Ravi Kiran Pagidi, Aravind Ayyagari, Punit Goel, Arpit Jain, and Satendra Pal Singh. 2021. The Use of Interpretability in Machine Learning for Regulatory Compliance. International Journal of General Engineering and Technology 10(1):133–158. doi: ISSN (P) 2278–9928; ISSN (E) 2278–9936.

[94] Sivaprasad Nadukuru, Shreyas Mahimkar, Sumit Shekhar, Om Goel, Prof. (Dr) Arpit Jain, and Prof. (Dr) Punit Goel. (2021). Integration of SAP Modules for Efficient Logistics and Materials Management. International Journal of Research in Modern Engineering and Emerging Technology (IJRMEET) 9(12):96. Retrieved from www.ijrmeet.org

[95] Sivaprasad Nadukuru, Fnu Antara, Pronoy Chopra, A. Renuka, Om Goel, and Er. Aman Shrivastav. (2021). Agile Methodologies in Global SAP Implementations: A Case Study Approach. International Research Journal of Modernization in Engineering Technology and Science, 3(11). DOI: https://www.doi.org/10.56726/IRJMETS17272

[96] Kshirsagar, Rajas Paresh, Raja Kumar Kolli, Chandrasekhara Mokkapati, Om Goel, Dr. Shakeb Khan, & Prof.(Dr.) Arpit Jain. (2021). Wireframing Best Practices for Product Managers in Ad Tech. Universal Research Reports, 8(4), 210–229. https://doi.org/10.36676/urr.v8.i4.1387

[97] Kankanampati, Phanindra Kumar, Rahul Arulkumaran, Shreyas Mahimkar, Aayush Jain, Dr. Shakeb Khan, & Prof.(Dr.) Arpit Jain. (2021). Effective Data Migration Strategies for Procurement Systems in SAP Ariba. Universal Research Reports, 8(4), 250–267. https://doi.org/10.36676/urr.v8.i4.1389

[98] Nanda Kishore Gannamneni, Jaswanth Alahari, Aravind Ayyagari, Prof.(Dr) Punit Goel, Prof.(Dr.) Arpit Jain, & Aman Shrivastav. (2021). Integrating SAP SD with Third-Party Applications for Enhanced EDI and IDOC Communication. Universal Research Reports, 8(4), 156–168. https://doi.org/10.36676/urr.v8.i4.1384

[99] Nanda Kishore Gannamneni, Siddhey Mahadik, Shanmukha Eeti, Om Goel, Shalu Jain, & Raghav Agarwal. (2021). Database Performance Optimization Techniques for Large-Scale Teradata Systems. Universal Research Reports, 8(4), 192–209. https://doi.org/10.36676/urr.v8.i4.1386

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Published

2024-10-22

How to Cite

Shah, S., & Jain, S. (2024). Data Governance in Lakehouse. Stallion Journal for Multidisciplinary Associated Research Studies, 3(5), 126–145. https://doi.org/10.55544/sjmars.3.5.12

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