Designing High-Performance Data Pipelines Using Snowflake and Cloud-Native Architectures

Sravan Kumar Kunadi

doi:10.15662/IJRAI.2022.0506030

Authors

Sravan Kumar Kunadi Independent Researcher, USA Author

DOI:

https://doi.org/10.15662/IJRAI.2022.0506030

Keywords:

Snowflake, data pipelines, real time analytics, snowflake, data engineering, scalability, cloud-native architecture

Abstract

The rising volume, velocity and variety of data in enterprises have compelled the acute necessity to possess data pipeline structures that are extendable, effective, and capable. The research article offers the discussion of the methods of creating high-performance data pipelines with Snowflake and cloud-native architecture to address the issues of the contemporary data engineering. To be able to scale elastically, have automatic workload management, and support semi-structured data to improve pipeline performances, flexibility, and cost-effectiveness, the paper will look at the Snowflake effect to decouple compute and storage. It further discusses the ways in which cloud-native components (such as containerized services, serverless processing, event-based orchestration, and automated monitoring) can be leveraged to create resilient end-to-end workflows with data. The key design principles that the paper points out include real time and batch data ingestion, transformation optimization, fault tolerance, security, governance and pipeline observability. As Snowflake will be connected to cloud-native ecosystems, organizations will be capable of creating pipelines, which can be scaled to fit the need of different workloads and provide low latency and high data quality. The article also outlines the best practices in performance tuning, resource allocation, metadata-based processing, and on-going integration and deployment in data operations. The findings demonstrate how Snowflake, deployed on cloud-native design patterns can assist enterprises in modernizing their old data platforms, scaling up analytics, and data-driven decisions. This paper provides a working guide to architects, engineers, and organizations that seek to design future-proof data platforms that are swift, dependable, scalable and simple to work with in more intricate digital environments.

References

[1] H. Wieslander, P. J. Harrison, G. Skogberg, et al., “Deep learning and conformal prediction for hierarchical analysis of large-scale whole-slide tissue images,” IEEE Journal of Biomedical and Health Informatics, vol. 25, no. 2, pp. 371–380, 2021.

[2] B. Blamey, F. Wrede, J. Karlsson, et al., “Adapting the secretary hiring problem for optimal hot-cold tier placement under top-K workloads,” in Proc. IEEE/ACM Int. Symp. Cluster, Cloud and Grid Computing (CCGRID), Larnaca, Cyprus, 2019, pp. 576–583.

[3] J. Kelleher, M. Lin, C. H. Albach, et al., “Htsget: A protocol for securely streaming genomic data,” Bioinformatics, vol. 35, no. 1, pp. 119–121, 2019.

[4] J. A. Novella, P. Emami Khoonsari, S. Herman, et al., “Container-based bioinformatics with Pachyderm,” Bioinformatics, vol. 35, no. 5, pp. 839–846, 2019.

[5] B. Blamey, A. Hellander, and S. Toor, “Apache Spark Streaming, Kafka and HarmonicIO: A performance benchmark and architecture comparison for enterprise and scientific computing,” in Benchmarking, Measuring, and Optimizing (Bench 2019), Cham, Switzerland: Springer, 2019, pp. 1–15.

[6] P. Torruangwatthana, H. Wieslander, B. Blamey, et al., “HarmonicIO: Scalable data stream processing for scientific datasets,” in Proc. IEEE Int. Conf. Cloud Computing (CLOUD), San Francisco, CA, USA, 2018, pp. 879–882.

[7] C. McQuin, A. Goodman, V. Chernyshev, et al., “CellProfiler 3.0: Next-generation image processing for biology,” PLoS Biology, vol. 16, no. 7, 2018, Art. no. e2005970.

[8] U. Sivarajah, M. M. Kamal, Z. Irani, et al., “Critical analysis of big data challenges and analytical methods,” Journal of Business Research, vol. 70, pp. 263–286, 2017.

[9] D. Wang, S. Fong, R. K. Wong, et al., “Robust high-dimensional bioinformatics data streams mining by ODR-ioVFDT,” Scientific Reports, vol. 7, no. 1, Art. no. 43167, 2017.

[10] J. Cuenca-Alba, L. del Cano, J. Gómez Blanco, et al., “ScipionCloud: An integrative and interactive gateway for large scale cryo electron microscopy image processing on commercial and academic clouds,” Journal of Structural Biology, vol. 200, no. 1, pp. 20–27, 2017.

[11] W. Ouyang and C. Zimmer, “The imaging tsunami: Computational opportunities and challenges,” Current Opinion in Systems Biology, vol. 4, pp. 105–113, 2017.

[12] W. Shi and S. Dustdar, “The promise of edge computing,” Computer, vol. 49, no. 5, pp. 78–81, 2016.