Snowflake vs. Databricks: A Comparative Study of Data Engineering and Analytics in the Cloud

Authors

  • Ravi Kumar Kota Topgolf Callaway Brands, USA Author
  • Lalitha Amarapalli Fresenius-Kabi, USA Author
  • Lavanya Shanmugam Tata Consultancy Services, USA Author

Keywords:

Snowflake, Databricks, data warehousing, lakehouse architecture

Abstract

The rapid evolution of cloud-based data management solution has compelled leading platform to guide enterprises in selecting optimal architecture for their big data analytics requirements. This study provides the in-depth evaluation of Snowflake’s data warehousing capabilities against Databricks’ AI-powered architecture. This paper focuses on key parameters such as performance, scalability, cost efficiency, and AI/ML integration.

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References

Singu, Santosh Kumar. "Real-Time Data Integration: Tools, Techniques, and Best Practices." ESP Journal of Engineering & Technology Advancements 1.1 (2021): 158-172.

S. Kumari, “Agile Cloud Transformation in Enterprise Systems: Integrating AI for Continuous Improvement, Risk Management, and Scalability”, Australian Journal of Machine Learning Research & Applications, vol. 2, no. 1, pp. 416–440, Mar. 2022

S. Kumari, “AI-Enhanced Agile Development for Digital Product Management: Leveraging Data-Driven Insights for Iterative Improvement and Market Adaptation”, Adv. in Deep Learning Techniques, vol. 2, no. 1, pp. 49–68, Mar. 2022

Singu, Santosh Kumar. "Designing scalable data engineering pipelines using Azure and Databricks." ESP Journal of Engineering & Technology Advancements 1.2 (2021): 176-187.

S. Kumari, “AI-Driven Cybersecurity in Agile Cloud Transformation: Leveraging Machine Learning to Automate Threat Detection, Vulnerability Management, and Incident Response”, J. of Art. Int. Research, vol. 2, no. 1, pp. 286–305, Apr. 2022

P. Cudre-Mauroux, E. Wu, and S. Madden, “The case for wisdom of the crowds in database systems,” in Proc. VLDB Endowment, vol. 4, no. 6, pp. 420–431, Mar. 2011.

D. DeWitt and J. Gray, “Parallel database systems: The future of high performance database processing,” Commun. ACM, vol. 35, no. 6, pp. 85–98, June 1992.

D. J. Abadi, P. A. Boncz, and S. Harizopoulos, “Column-oriented database systems,” Proc. VLDB Endowment, vol. 2, no. 2, pp. 1664–1665, Aug. 2009.

F. Pérez and B. E. Granger, “IPython: A system for interactive scientific computing,” Comput. Sci. Eng., vol. 9, no. 3, pp. 21–29, May 2007.

J. Goecks, A. Nekrutenko, and J. Taylor, “Galaxy: A comprehensive approach for supporting accessible, reproducible, and transparent computational research in the life sciences,” Genome Biol., vol. 11, no. 8, Aug. 2010.

J. Dean and S. Ghemawat, “MapReduce: Simplified data processing on large clusters,” in Proc. 6th Symp. Operating Syst. Design Implement., San Francisco, CA, USA, 2004, pp. 137–150.

A. Thusoo, J. Sen Sarma, N. Jain, Z. Shao, P. Chakka, S. Anthony, H. Liu, P. Wyckoff, and R. Murthy, “Hive – A petabyte scale data warehouse using Hadoop,” in Proc. Int. Conf. Data Eng. (ICDE), Long Beach, CA, USA, 2010, pp. 996–1005.

J. Gonzalez, Y. Low, H. Gu, D. Bickson, and C. Guestrin, “PowerGraph: Distributed graph-parallel computation on natural graphs,” in Proc. 10th USENIX Symp. Operating Syst. Design Implement., Hollywood, CA, USA, 2012, pp. 17–30.

R. V. Nehme and N. Bruno, “Automated partitioning design in parallel database systems,” in Proc. ACM SIGMOD Conf., Indianapolis, IN, USA, 2010, pp. 1137–1148.

A. Pavlo, E. Paulson, A. Rasin, D. J. Abadi, D. DeWitt, S. Madden, and M. Stonebraker, “A comparison of approaches to large-scale data analysis,” in Proc. ACM SIGMOD Conf., Providence, RI, USA, 2009, pp. 165–178.

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Published

20-05-2022

How to Cite

[1]
Ravi Kumar Kota, Lalitha Amarapalli, and Lavanya Shanmugam, “Snowflake vs. Databricks: A Comparative Study of Data Engineering and Analytics in the Cloud ”, Essex Journal of AI Ethics and Responsible Innovation, vol. 2, pp. 98–135, May 2022, Accessed: Apr. 16, 2025. [Online]. Available: https://ejaeai.org/index.php/publication/article/view/17