Published 13-12-2021
Keywords
- Snowflake IPO,
- cloud-native data platforms
This work is licensed under a Creative Commons Attribution-NonCommercial-ShareAlike 4.0 International License.
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Abstract
Snowflake Inc.'s initial public offering (IPO) was a watershed moment for the technology and data industries, setting a new benchmark for cloud-based solutions. The company's IPO raised $3.4 billion & valued Snowflake at over $33 billion, marking one of the largest software IPOs in history. This landmark event signified not only the rapid growth of cloud-native technologies but also the rising demand for scalable and high-performance data warehousing platforms that can handle the complexities of modern data. Snowflake's success highlighted a shift in how businesses approach data management, with an increasing preference for cloud solutions that offer flexibility, easy integration, & the ability to scale on demand. Unlike traditional on-premises data warehouses, Snowflake's architecture is fully optimized for the cloud, providing unparalleled data sharing, multi-cloud support, and superior performance across various workloads. As the company positions itself against established players and emerging startups, Snowflake's IPO shines a spotlight on its unique market positioning and ability to disrupt the data industry. The offering also raised important questions about the future of data technologies, with potential implications for big data analytics, real-time processing, and the broader landscape of cloud-based enterprise tools. However, Snowflake's journey ahead will be challenging. As the company grows and competes in a rapidly evolving market, it must continue innovating to maintain its edge over rivals. The IPO marks a defining moment for Snowflake. Yet, the broader impact on the data industry remains an evolving story, as the company's influence could play a pivotal role in shaping the future of cloud data solutions & the broader enterprise technology ecosystem.
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References
- Dageville, B., Cruanes, T., Zukowski, M., Antonov, V., Avanes, A., Bock, J., ... & Unterbrunner, P. (2016, June). The snowflake elastic data warehouse. In Proceedings of the 2016 International Conference on Management of Data (pp. 215-226).
- Ly, D. H. (2019). Data analytics in cloud data warehousing, case company.
- Fernandes, S., & Bernardino, J. (2016). Cloud Data Warehousing for SMEs. In ICSOFT-EA (pp. 276-282).
- Glaser, V. L., Fiss, P. C., & Kennedy, M. T. (2016). Making snowflakes like stocks: Stretching, bending, and positioning to make financial market analogies work in online advertising. Organization Science, 27(4), 1029-1048.
- Chaudhuri, S., & Dayal, U. (1997). An overview of data warehousing and OLAP technology. ACM Sigmod record, 26(1), 65-74.
- Alyeksyeyeva, I. (2017). Defining snowflake in British post-Brexit and US post-election public discourse. Science and Education a New Dimension, 39(143), 7-10.
- Alugubelli, R. (2018). Data mining and analytics framework for healthcare. International Journal of Creative Research Thoughts (IJCRT), ISSN, 2320-2882.
- Mukherjee, R., & Kar, P. (2017, January). A comparative review of data warehousing ETL tools with new trends and industry insight. In 2017 IEEE 7th International Advance Computing Conference (IACC) (pp. 943-948). IEEE.
- Tryfona, N., Busborg, F., & Borch Christiansen, J. G. (1999, November). starER: A conceptual model for data warehouse design. In Proceedings of the 2nd ACM international workshop on Data warehousing and OLAP (pp. 3-8).
- 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.
- Peters, E. E. (1996). Chaos and order in the capital markets: a new view of cycles, prices, and market volatility. John Wiley & Sons.
- Anejionu, O. C., Thakuriah, P. V., McHugh, A., Sun, Y., McArthur, D., Mason, P., & Walpole, R. (2019). Spatial urban data system: A cloud-enabled big data infrastructure for social and economic urban analytics. Future generation computer systems, 98, 456-473.
- Mohammed, K. I. (2014). Data warehouse design and implementation based on quality requirements. International Journal of Advances in Engineering & Technology, 7(3), 642-651.
- O'Leary, D. E. (1999). REAL‐D: A schema for data warehouses. Journal of Information Systems, 13(1), 49-62.
- Ahmad, I., Azhar, S., & Lukauskis, P. (2004). Development of a decision support system using data warehousing to assist builders/developers in site selection. Automation in construction, 13(4), 525-542.
- Gade, K. R. (2019). Data Migration Strategies for Large-Scale Projects in the Cloud for Fintech. Innovative Computer Sciences Journal, 5(1).
- Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
- Komandla, V. Enhancing Security and Fraud Prevention in Fintech: Comprehensive Strategies for Secure Online Account Opening.
- Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
- Gade, K. R. (2017). Integrations: ETL vs. ELT: Comparative analysis and best practices. Innovative Computer Sciences Journal, 3(1).