Real-Time Analytics on Snowflake: Harnessing the Potential of Data Streams
Keywords:
Snowflake, real-time analytics, data streams, cloud data warehouseAbstract
Real-time analytics transforms corporate data use, therefore enabling quick insights and flexible decision-making. Leading this transformation is Snowflake, a cloud-native data warehouse with remarkable processing and analysis capability for real-time access to large data flows. Its adaptable, scalable design and built-in support for semi-structured data formats such as JSON and Parquet help companies to easily manage dynamic, high- Velocity data. Snowflake's relationship with well-known streaming systems like Apache Kafka and AWS Kinesis helps companies to quickly import data and analyze it as it is acquired. The unique way the platform divides compute and storage allows autonomous scalability, ensuring best performance even with a maximum load. Its SQL-based querying simultaneously simplifies analytics for teams ranging in degree of experience. Snowflake's capacity to combine structured and semi-structured data lays a good foundation for deriving insightful analysis from complex datasets without requiring any preprocessing. Applications show Snowflake's ability to effectively and precisely address basic business needs including real-time customer behaviour tracking, supply chain optimization, and fraud detection. Snowflake enables businesses to have real-time insights, therefore enhancing operational effectiveness, customer experiences, and strategic decision-making. By turning data streams into practically limitless flow of value, this paper examines the tools, methods, and best practices that enable Snowflake to be a leader in real-time analytics. Strong architecture, simple interface, and perfect adaptation to changing data requirements of Snowflake help businesses to remain competitive in a dynamic, data-driven market and translate real-time insights into measurable business results.
References
1. Burri, O. (2019). Providing machine level data for cloud based analytics (Master's thesis).
2. Palanivel, K. (2019). Modern network analytics architecture stack to enterprise networks. International Journal for Research in Applied Science & Engineering Technology (IJRASET), 7(4), 2634-2651.
3. Ilijason, R. (2020). Beginning Apache Spark Using Azure Databricks: Unleashing Large Cluster Analytics in the Cloud. Apress.
4. Beryoza, D., Campbell, M., Cardorelle, C., Creasey, T., Cushing, D., Da Silva, V., ... & Zhang, Y. (2015). IBM Cognos Dynamic Cubes. IBM Redbooks.
5. Gerlitz, C., & Helmond, A. (2013). The like economy: Social buttons and the data-intensive web. New media & society, 15(8), 1348-1365.
6. Tien, J. M. (2017). The Sputnik of servgoods: Autonomous vehicles. Journal of systems science and systems engineering, 26, 133-162.
7. Tsou, M. C. (2016). Online analysis process on Automatic Identification System data warehouse for application in vessel traffic service. Proceedings of the Institution of Mechanical Engineers, Part M: Journal of Engineering for the Maritime Environment, 230(1), 199-215.
8. MacLennan, J., Tang, Z., & Crivat, B. (2008). Data mining with Microsoft SQL server 2008. John Wiley & Sons.
9. Godfrey, P., Gryz, J., & Lasek, P. (2016). Interactive visualization of large data sets. IEEE transactions on knowledge and data engineering, 28(8), 2142-2157.
10. Dorndorf, U., & Pesch, E. (2002). Data Warehouses. In Handbook on Data Management in Information Systems (pp. 387-430). Berlin, Heidelberg: Springer Berlin Heidelberg.
11. Iafrate, F. (2018). Artificial intelligence and big data: The birth of a new intelligence. John Wiley & Sons.
12. Patel, J. A. (2019). Efficient Computing Of Big Data Harmonization (Doctoral dissertation, GUJARAT TECHNOLOGICAL UNIVERSITY AHMEDABAD).
13. Fathi Salmi, M. (2016). Processing Big Data in Main Memory and on GPU (Master's thesis, The Ohio State University).
14. Kretz, A. (2019). The data engineering cookbook. Mastering the plumbing of data science.
15. de Murillas, E. G. L. (2019). Process mining on databases: extracting event data from real-life data sources.
17. Gade, K. R. (2020). Data Mesh Architecture: A Scalable and Resilient Approach to Data Management. Innovative Computer Sciences Journal, 6(1).
18. Gade, K. R. (2020). Data Analytics: Data Privacy, Data Ethics, Data Monetization. MZ Computing Journal, 1(1).
19. Katari, A. Conflict Resolution Strategies in Financial Data Replication Systems.
20. Katari, A., & Rallabhandi, R. S. DELTA LAKE IN FINTECH: ENHANCING DATA LAKE RELIABILITY WITH ACID TRANSACTIONS.
21. Komandla, V. Transforming Financial Interactions: Best Practices for Mobile Banking App Design and Functionality to Boost User Engagement and Satisfaction.
22. Thumburu, S. K. R. (2020). Enhancing Data Compliance in EDI Transactions. Innovative Computer Sciences Journal, 6(1).
23. Thumburu, S. K. R. (2020). Leveraging APIs in EDI Migration Projects. MZ Computing Journal, 1(1).
24. Gade, K. R. (2018). Real-Time Analytics: Challenges and Opportunities. Innovative Computer Sciences Journal, 4(1).
