Snowflake Innovations: Expanding Beyond Data Warehousing

Authors

  • Naresh Dulam Vice President Sr Lead Software Engineer, JP Morgan Chase, USA Author
  • Karthik Allam Big Data Infrastructure Engineer, JP Morgan & Chase, USA Author

Keywords:

Snowflake, data warehousing

Abstract

Snowflake has fundamentally changed how organizations approach data storage and analytics, quickly emerging as a leader in the cloud data warehousing space. Initially designed to address the challenges of traditional on-premises data warehouses, Snowflake's innovative architecture offers a unique approach that separates compute and storage, providing enhanced performance, scalability, and cost-efficiency. While its roots are firmly planted in data warehousing, Snowflake's potential stretches far beyond that. By introducing critical features like multi-cloud architecture, native support for semi-structured data, and advanced data-sharing capabilities, Snowflake has evolved into a versatile platform that supports a wide range of use cases. The Snowflake Data Exchange, for example, allows organizations to securely share data across business units or even with external partners, facilitating better collaboration and decision-making. Additionally, Snowflake's real-time analytics capabilities have made it a go-to solution for organizations seeking to unlock valuable insights from live data streams, paving the way for more agile and data-driven business operations. Beyond analytics, Snowflake's innovative approach to data management also empowers application developers by providing an easy-to-use platform for building data-centric applications. This expansion into areas like data collaboration and application development positions Snowflake as a cloud data warehouse and a central hub for a company's entire data ecosystem. The company's approach has opened the door for organizations to rethink their data strategies and move beyond traditional data warehousing into real-time analytics, seamless data sharing, and collaborative application development. As Snowflake continues to innovate, its future looks promising, offering new opportunities for businesses to leverage their data in ways that were once thought impossible. This article explores how Snowflake has expanded beyond its original data warehousing scope, the innovations driving its success, and the challenges & opportunities ahead as the platform continues to grow and evolve in a rapidly changing technological landscape.

Downloads

Download data is not yet available.

References

Taniar, D., & Chen, L. (2011). Integrations of Data Warehousing, Data Mining and Database Technologies: Innovative Approaches. Information Science Reference.

Linstedt, D., & Olschimke, M. (2015). Building a scalable data warehouse with data vault 2.0. Morgan Kaufmann.

Hodgins, W. (2008). The snowflake effect: The future of mashups and learning. Retrieved January, 8, 2009.

Collier, K. (2012). Agile analytics: A value-driven approach to business intelligence and data warehousing. Addison-Wesley.

Aswini, S., Murali, D., & Selvam, M. E. R. (2013). Dynamic Environment and Snowflake Schema in Real Time Data Services. International Journal of Computer Applications, 82(5), 23-29.

Ronthal, A. M., Edjlali, R., & Greenwald, R. (2018). Magic Quadrant for Data Management Solutions for Analytics. Gartner, Inc. ID: G00326691, 1-39.

Hammer, J., Schneider, M., & Sellis, T. (2004). Data warehousing at the crossroads. In actas del Dagstuhl Perspectives Workshop, Dagstuhl.

Kimball, R., & Ross, M. (2016). The kimball group reader: Relentlessly practical tools for data warehousing and business intelligence remastered collection. John Wiley & Sons.

Moscoso-Zea, O., Paredes-Gualtor, J., & Luján-Mora, S. (2018). A holistic view of data warehousing in education. IEEE access, 6, 64659-64673.

Seyed-Abbassi, B. (2001). Teaching Effective Methodologies to Design a Data Warehouse. In Proc of the 18th Annual Information Systems Education Conference (pp. 1-4).

Sen, S., Ghosh, R., Paul, D., & Chaki, N. (2012). Integrating XML data into multiple Rolap data warehouse schemas. International Journal of Software Engineering & Applications, 3(1), 197.

Subekti, M., Warnars, H. L. H. S., & Heryadi, Y. (2017, November). The 3 steps of best data warehouse model design with leaning implementation for sales transaction in franchise restaurant. In 2017 IEEE International Conference on Cybernetics and Computational Intelligence (CyberneticsCom) (pp. 170-174). IEEE.

Yuhanna, N., Leganza, G., & Lee, J. (2017). The Forrester Wave™: Big Data Warehouse, Q2 2017. Adoption Grows As Enterprises Look To Revive Their EDW Strategy, 17.

Feldman, R., & Dori, D. (2006). Designing Data Warehouses with Object Process Methodology (Doctoral dissertation, Computer Science Department, Technion).

Brodny, J., Tutak, M., & Michalak, M. (2017). A data warehouse as an indispensable tool to determine the effectiveness of the use of the longwall shearer. In Beyond Databases, Architectures and Structures. Towards Efficient Solutions for Data Analysis and Knowledge Representation: 13th International Conference, BDAS 2017, Ustroń, Poland, May 30-June 2, 2017, Proceedings 13 (pp. 453-465). Springer International Publishing.

Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).

Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).

Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.

Downloads

Published

11-04-2019

How to Cite

[1]
Naresh Dulam and Karthik Allam, “Snowflake Innovations: Expanding Beyond Data Warehousing ”, Distrib Learn Broad Appl Sci Res, vol. 5, Apr. 2019, Accessed: Dec. 27, 2024. [Online]. Available: https://dlabi.org/index.php/journal/article/view/236

Most read articles by the same author(s)

1 2 3 > >> 

Similar Articles

121-130 of 166

You may also start an advanced similarity search for this article.