Log in

Relevant bibliographies by topics / Snowflake cloud data platforms / Journal articles

To see the other types of publications on this topic, follow the link: Snowflake cloud data platforms.

Journal articles on the topic 'Snowflake cloud data platforms'

Author: Grafiati

Published: 5 June 2025

Last updated: 2 August 2025

Create a spot-on reference in APA, MLA, Chicago, Harvard, and other styles

Select a source type:

Consult the top 50 journal articles for your research on the topic 'Snowflake cloud data platforms.'

Next to every source in the list of references, there is an 'Add to bibliography' button. Press on it, and we will generate automatically the bibliographic reference to the chosen work in the citation style you need: APA, MLA, Harvard, Chicago, Vancouver, etc.

You can also download the full text of the academic publication as pdf and read online its abstract whenever available in the metadata.

Browse journal articles on a wide variety of disciplines and organise your bibliography correctly.

1

Karanam, Srinivasa Rao. "Advanced Analytics with Snowflake and Power BI." International Scientific Journal of Engineering and Management 03, no. 09 (2024): 1–7. https://doi.org/10.55041/isjem02074.

Full text

Abstract:

The demand for advanced analytics and near real-time insights has intensified across industries. Organizations are increasingly turning to modern data platforms and powerful business intelligence tools to streamline their analytical processes and drive decision-making. Snowflake has cemented its position as a leading cloud data platform that enables elastic compute scaling, secure data sharing, and powerful governance features. Simultaneously, Microsoft’s Power BI continues to evolve as one of the most widely adopted business intelligence and analytics solutions. The synergy between Snowflake

APA, Harvard, Vancouver, ISO, and other styles

2

Praveen, Borra. "Snowflake: A Comprehensive Review of a Modern Data Warehousing Platform." International Journal of Computer Science and Information Technology Research (IJCSITR) 3, no. 1 (2022): 11–16. https://doi.org/10.5281/zenodo.11617628.

Full text

Abstract:

<em>Snowflake represents a state-of-the-art data warehousing platform, fundamentally altering the landscape of data management within cloud environments. This paper offers an exhaustive examination of Snowflake, delving into its structural intricacies, pivotal functionalities, competitive edges, and far-reaching implications for the sector. By meticulously scrutinizing its inventive methodologies for data retention, processing, and adaptability, this analysis elucidates Snowflake's rise to prominence as a leader in cloud-centric data management solutions. Moreover, it sheds light on Snowflake'

APA, Harvard, Vancouver, ISO, and other styles

3

Prasad, M. Seetharama. "A Comparative Study of Snowflake and SAP BW for Data Analytics." International Journal of Technology, Management and Humanities 11, no. 02 (2025): 1–8. https://doi.org/10.21590/ijtmh.11.02.07.

Full text

Abstract:

The modern data environment demands strong solutions for managing, storing, and analyzing large amounts of data efficiently. Snowflake and SAP Business Warehouse (SAP BW) are two leading platforms in the area of data warehousing and analytics that present different capabilities to handle diverse enterprise needs. The comparative analysis between Snowflake and SAP BW is presented in this study, detailing their architecture, scalability, performance, integration capabilities, and cost-effectiveness. Snowflake is a cloud-native data platform designed for elasticity and scalability, with pay-as-yo

APA, Harvard, Vancouver, ISO, and other styles

4

Venkata, Tadi. "Performance and Scalability in Data Warehousing: Comparing Snowflake's Cloud-Native Architecture with Traditional On-Premises Solutions Under Varying Workloads." European Journal of Advances in Engineering and Technology 9, no. 5 (2022): 127–39. https://doi.org/10.5281/zenodo.13319605.

Full text

Abstract:

This study investigates the performance and scalability of Snowflake's cloud-native architecture compared to traditional on-premises data warehousing solutions under varying workloads. As organizations increasingly migrate to cloud-based platforms for their data management needs, understanding the trade-offs and benefits of such transitions becomes crucial. This research provides a comprehensive analysis of Snowflake's data processing speed and scalability capabilities, examining its efficiency in handling diverse and intensive workloads. By employing a series of benchmark tests and performanc

APA, Harvard, Vancouver, ISO, and other styles

5

Researcher. "REVOLUTIONIZING DATA MANAGEMENT: KEY INNOVATIONS IN TERADATA AND SNOWFLAKE TECHNOLOGIES." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 587–94. https://doi.org/10.5281/zenodo.13347929.

Full text

Abstract:

This article comprehensively examines recent advancements in data engineering, focusing on two leading platforms: Teradata and Snowflake. Through a rigorous methodology encompassing literature review, case study analysis, and industry report examination, the study offers an in-depth exploration of cutting-edge developments in machine learning integration, hybrid cloud solutions, serverless computing, and advanced data governance. The article highlights Teradata's strides in predictive analytics, real-time decision-making capabilities, and performance optimization while showcasing Snowflak

APA, Harvard, Vancouver, ISO, and other styles

6

Dr.A.Shaji, George. "Deciphering the Path to Cost Efficiency and Sustainability in the Snowflake Environment." Partners Universal International Innovation Journal (PUIIJ) 01, no. 04 (2023): 231–50. https://doi.org/10.5281/zenodo.8282654.

Full text

Abstract:

As adoption of the Snowflake cloud data platform continues to accelerate, organizations are seeking ways to optimize costs and resource utilization amidst Snowflake's unique architecture. This paper examines best practices and considerations for attaining efficiency, managing expenses, and upholding sustainability initiatives within Snowflake implementations. The study begins by providing background on Snowflake's novel cloud-native architecture, which separates storage from computing. This elasticity enables immense scalability, but can also lead to cost overruns if workloads and reso

APA, Harvard, Vancouver, ISO, and other styles

7

Kukkuhalli, Shreesha Hegde. "Optimizing Snowflake Enterprise Data Platform Cost Through Predictive Analytics and Query Performance Optimization." Journal of Artificial Intelligence & Cloud Computing 3, no. 6 (2024): 1–3. https://doi.org/10.47363/jaicc/2024(3)406.

Full text

APA, Harvard, Vancouver, ISO, and other styles

8

Ramesh, Betha. "The Rise of Cloud Data Warehousing: AWS Redshift vs Snowflake." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 5, no. 6 (2019): 1–9. https://doi.org/10.5281/zenodo.14866601.

Full text

Abstract:

The emergence of cloud data warehousing has fundamentally transformed how organizations manage and analyze their data assets. This paper presents a comprehensive analysis of two leading cloud data warehouse solutions: Amazon Redshift and Snowflake. Through detailed examination of their architectures, performance characteristics, and economic models, we provide insights into the evolving landscape of cloud-based analytics. Our analysis reveals the distinct approaches these platforms take to address modern data warehousing challenges, including scalability, concurrency, and data sharing. The fin

APA, Harvard, Vancouver, ISO, and other styles

9

Researcher. "BUILDING SCALABLE DATA ARCHITECTURES WITH INFORMATICA IICS AND SNOWFLAKE." International Journal of Information Technology and Management Information Systems (IJITMIS) 15, no. 2 (2024): 85–93. https://doi.org/10.5281/zenodo.14498317.

Full text

Abstract:

Modern organisations generate vast and ever-changing amounts of data, requiring optimised, flexible, and expandable data handling environments. This research paper seeks to assess the compatibility of IICS and Snowflake in complex integration, processing, and analytical cloud structures that address today’s data challenges. To be more specific, employing those integrated platforms facilitates attaining novel paradigms of scale, speed, and expenses for organisational data management gestures.

APA, Harvard, Vancouver, ISO, and other styles

10

Karanam, Srinivasa Rao. "Revefi for Snowflake Operations." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 03 (2025): 1–8. https://doi.org/10.55041/ijsrem43472.

Full text

Abstract:

Snowflake has emerged as one of the leading cloud data warehouse platforms, offering near-infinite scalability, on-demand compute, and separation of storage and compute resources. While these characteristics facilitate a wide range of data-driven use cases, enterprises often face challenges around costs, performance, and data quality. Revefi, an emerging Data Operations Cloud platform, addresses these core challenges through AI-driven insights, rapid setup, and a metadata-focused architecture designed to bolster security. This technical exploration delves into the key aspects of Revefi for Sno

APA, Harvard, Vancouver, ISO, and other styles

11

Khushmeet Singh. "Performance Optimization and Cost Control in Snowflake: A Strategic Approach." International Research Journal on Advanced Engineering and Management (IRJAEM) 3, no. 05 (2025): 1623–29. https://doi.org/10.47392/irjaem.2025.0262.

Full text

Abstract:

As organizations increasingly migrate critical data workloads to cloud-native platforms, Snowflake has emerged as a leading data warehouse solution offering flexibility, scalability, and performance. However, its utility-based pricing model introduces new complexities in managing cost and optimizing performance. This review provides a strategic analysis of Snowflake’s architectural elements, AI-driven optimization approaches, cost governance techniques, and workload management best practices. Experimental results demonstrate that intelligent orchestration, auto-scaling, and query optimization

APA, Harvard, Vancouver, ISO, and other styles

12

Shreesha, Hegde Kukkuhalli. "Optimizing Snowflake Enterprise Data Platform Cost Through Predictive Analytics and Query Performance Optimization." International Journal on Science and Technology 15, no. 4 (2024): 1–6. https://doi.org/10.5281/zenodo.14473872.

Full text

Abstract:

The rapid adoption of cloud-based data platforms, such as Snowflake, has led to significant benefits in terms of scalability, flexibility, and performance for modern enterprises. However, managing costs in such environments remains a challenge, especially as data volumes and query complexities increase. This paper explores a comprehensive strategy to optimize Snowflake costs through the implementation of predictive analytics and performance optimization techniques. By leveraging machine learning models to forecast resource utilization and employing query optimization techniques, organizations

APA, Harvard, Vancouver, ISO, and other styles

13

Santosh, Vinnakota. "Streamlining Legacy Migrations: A Comparative Analysis of Teradata to Snowflake Transformation." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 8, no. 4 (2020): 1–7. https://doi.org/10.5281/zenodo.15054566.

Full text

Abstract:

The increasing need for scalable, cost-effective, and cloud-native data solutions has propelled organizations to migrate from legacy systems like Teradata to modern platforms such as Snowflake. This paper delves into comprehensive strategies, explores technical challenges, and presents robust solutions for migrating Teradata workloads to Snowflake. It emphasizes advanced techniques for schema conversion, SQL translation, query performance tuning, and cost optimization. Detailed workflows, in-depth technical comparisons, and actionable insights are accompanied by diagrams, flowcharts, and examp

APA, Harvard, Vancouver, ISO, and other styles

14

Srikanth Dandolu. "Cloud-Native Architecture for AI Data Platforms: A Snowflake Implementation Case Study." World Journal of Advanced Engineering Technology and Sciences 15, no. 3 (2025): 475–85. https://doi.org/10.30574/wjaets.2025.15.3.0931.

Full text

Abstract:

This architectural analysis presents a comprehensive implementation of a cloud-native Snowflake-based data platform optimized for enterprise AI workloads. The design decisions, scalability strategies, and performance optimization techniques address the unique challenges of supporting machine learning pipelines in large-scale enterprise environments. The architecture leverages dynamic resource allocation, advanced partitioning strategies, and zero-copy cloning to enable efficient AI experimentation while maintaining governance and security. The multi-layer design approach effectively separates

APA, Harvard, Vancouver, ISO, and other styles

15

Mahesh Thoutam. "Comparative Analysis of Data Warehousing Solutions in the Cloud: A Focus on Azure PostgreSQL." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 423–31. http://dx.doi.org/10.32628/cseit241051016.

Full text

Abstract:

This article provides a comprehensive analysis of cloud data warehousing solutions, with a focus on Azure PostgreSQL. It examines the rapidly growing cloud data warehouse market, highlighting key advantages such as scalability, cost-effectiveness, and advanced analytics capabilities. The article compares major platforms including Azure PostgreSQL, Amazon Redshift, Google BigQuery, and Snowflake, detailing their features, strengths, and market positions. Special attention is given to Azure PostgreSQL, outlining scenarios where it excels and providing best practices for leveraging its features.

APA, Harvard, Vancouver, ISO, and other styles

16

Jaya Krishna Vemuri. "Business Continuity and Disaster Recovery in Snowflake: A Technical Deep Dive." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 1 (2025): 2341–50. https://doi.org/10.32628/cseit251112254.

Full text

Abstract:

This technical deep dive examines Snowflake's comprehensive approach to Business Continuity and Disaster Recovery (BC/DR) in cloud-based data warehousing environments. The article explores how Snowflake addresses the challenges of exponential data growth and increasing real-time processing demands through advanced features including geo-redundant storage, time travel capabilities, and automated failover mechanisms. It article the platform's implementation of security frameworks, continuous data protection, and audit logging while providing detailed insights into best practices for organization

APA, Harvard, Vancouver, ISO, and other styles

17

Vijayarangan, Kumaran, and Saravanakumar Velusamy. "Comparative Insights on Centralized and Individual Models Using Snowflake and Google Big Query." Asian Journal of Research in Computer Science 18, no. 7 (2025): 155–63. https://doi.org/10.9734/ajrcos/2025/v18i7726.

Full text

Abstract:

Data sharing plays a vital role in today’s digital ecosystem, allowing businesses, governments, and individuals to exchange information on a massive scale. Cloud-native data platforms have significantly transformed traditional data management practices by introducing scale architectures, decoupled storage and compute models, cost-efficiency and resource governance. This paper investigates two distinct paradigms, (Centralized and Individual) data sharing models. Centralized architecture offers consolidated governance, multi-tenant scalability, and advanced analytics enablement, whereas individu

APA, Harvard, Vancouver, ISO, and other styles

18

Srikanth Dandolu. "Infrastructure as Code for Cloud-Native Data Platforms: Automation and Best Practices." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 451–88. https://doi.org/10.32996/jcsts.2025.7.5.55.

Full text

Abstract:

Infrastructure as Code (IaC) has revolutionized the management of cloud-native data platforms by transforming manual processes into programmatic declarations. This transformation enables organizations to achieve remarkable improvements in deployment efficiency, security posture, and operational reliability. Through the implementation of modular architecture, robust state management, and comprehensive security controls, enterprises can effectively automate their infrastructure while maintaining consistency and compliance. The integration of Terraform with Snowflake resources demonstrates substa

APA, Harvard, Vancouver, ISO, and other styles

19

Jagan Nalla. "Performance Engineering in Cloud Data Warehouses: A Systematic Approach to Optimization." Journal of Computer Science and Technology Studies 7, no. 5 (2025): 612–20. https://doi.org/10.32996/jcsts.2025.7.5.67.

Full text

Abstract:

Cloud data warehouses have emerged as the cornerstone of modern enterprise analytics infrastructure, yet achieving optimal performance across platforms like Redshift, Snowflake, and Synapse requires specialized knowledge that extends beyond traditional on-premises optimization techniques. This article presents a systematic framework for performance tuning in cloud data warehouse environments, encompassing critical aspects from foundational data modeling principles to advanced query optimization strategies. The interplay between schema design decisions, partitioning schemes, and indexing mechan

APA, Harvard, Vancouver, ISO, and other styles

20

Reddy Adavelli, Sateesh. "Multi-Cloud Data Resilience: Implementing Cross-Platform Data Strategies with Snowflake for P and C Insurance Operations." International Journal of Science and Research (IJSR) 12, no. 1 (2023): 1387–98. https://doi.org/10.21275/sr230115085206.

Full text

APA, Harvard, Vancouver, ISO, and other styles

21

Dashora, Shalin. "Cloud-based Data Analytics for Business Intelligence." International Journal for Research in Applied Science and Engineering Technology 11, no. 11 (2023): 2758–65. http://dx.doi.org/10.22214/ijraset.2023.57219.

Full text

Abstract:

Abstract: In the era of data-driven decision-making, organizations are turning to cloud-based data analytics for business intelligence to overcome the limitations of traditional on-premises systems. This paradigm shift offers the promise of scalable, agile, and advanced analytics capabilities. This paper explores the landscape of cloud- based data analytics for business intelligence by investigating existing systems, challenges, and opportunities. The study first examines leading cloud platforms such as Amazon Web Services (AWS) Redshift, Microsoft Azure Synapse Analytics, Google BigQuery, and

APA, Harvard, Vancouver, ISO, and other styles

22

Klimovic, Ana. "The Case for a New Cloud-Native Programming Model with Pure Functions." ACM SIGMOD Record 54, no. 2 (2025): 50–51. https://doi.org/10.1145/3749116.3749128.

Full text

Abstract:

Cloud evolution: Over the past two decades, the cloud has become the dominant platform for running all kinds of applications, from data analytics to web services. In the process, cloud platforms have evolved from renting virtual machines (VMs) on-demand to offering elastic compute and storage services. While the ability to support legacy applications was critical in the early days of cloud to ease migration from on-premise, today's users commonly develop cloud-native applications by composing cloud storage services (e.g., S3), compute services (e.g., AWS Lambda), data analytics services (e.g.,

APA, Harvard, Vancouver, ISO, and other styles

23

Parth Vyas. "Demystifying Dimensional Modeling for Modern Data Warehousing." Journal of Computer Science and Technology Studies 7, no. 2 (2025): 174–80. https://doi.org/10.32996/jcsts.2025.7.2.16.

Full text

Abstract:

This article demystifies dimensional modeling for data warehousing professionals by breaking down complex concepts into accessible components. It explores the foundational elements of dimensional design—fact tables, dimension tables, and star schemas—while delving into advanced topics like slowly changing dimensions, conformed dimensions, and hierarchical structures. The article examines implementation considerations, including surrogate keys versus natural keys, star versus snowflake schemas, and aggregation strategies that impact performance. It demonstrates how dimensional modeling principl

APA, Harvard, Vancouver, ISO, and other styles

24

Urvangkumar, Kothari. "Exploring the Convergence of Cloud Computing and Data Warehousing for Smarter Technology Solutions." International Journal of Leading Research Publication 6, no. 4 (2025): 1–13. https://doi.org/10.5281/zenodo.15125151.

Full text

Abstract:

The paper analyzes cloud computing solutions that boost data warehouses through enhanced scalability combined with cost optimization features together with real-time analytic capabilities. Data warehousing systems that operate from on-site locations struggle with expenses that are high and encounter limitations concerning scalability together with slow processing of data. Organizations choose cloud-based data warehousing solutions because they obtain scalable management capabilities for large datasets which help decrease their infrastructure expenses. The research investigates the cloud-native

APA, Harvard, Vancouver, ISO, and other styles

25

Mohan Gajula. "The evolution of data engineering: From ETL to real-time, AI-driven pipelines." World Journal of Advanced Research and Reviews 26, no. 2 (2025): 3273–80. https://doi.org/10.30574/wjarr.2025.26.2.1824.

Full text

Abstract:

The field of data engineering has transformed dramatically, evolving from traditional Extract, Transform, Load (ETL) processes toward sophisticated real-time, AI-enhanced data pipelines. This comprehensive article examines this transition, beginning with an assessment of conventional ETL limitations before exploring the revolutionary impact of streaming technologies such as Apache Kafka and Apache Flink. It extends to cloud-native architectures that have reshaped data infrastructure through platforms like Snowflake and Databricks, while highlighting the growing importance of advanced observabi

APA, Harvard, Vancouver, ISO, and other styles

26

Ramalakshmaiah Panguluri. "Developing a Competitive Edge: Building an Effective Portfolio in Snowflake and Teradata Data Engineering." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 10, no. 5 (2024): 601–10. http://dx.doi.org/10.32628/cseit241051045.

Full text

Abstract:

This article examines the critical components of an effective portfolio for data engineering professionals specializing in Snowflake and Teradata platforms. As the data landscape evolves, the ability to showcase practical skills alongside theoretical knowledge has become paramount for career advancement. Through an analysis of industry trends and expert interviews, we identify key elements that contribute to a compelling portfolio, including project showcases demonstrating complex problem-solving, relevant certifications, and evidence of continuous learning. The article highlights the importan

APA, Harvard, Vancouver, ISO, and other styles

27

Sullivan, Henry, and Mei Lin. "Cloud-Centric IoT Data Processing: A Multi-Platform Approach Using AWS, Azure, and Snowflake." International Journal of AI, BigData, Computational and Management Studies 2 (2021): 12–23. https://doi.org/10.63282/3050-9416.ijaibdcms-v2i1p102.

Full text

APA, Harvard, Vancouver, ISO, and other styles

28

Pendyala, Santhosh Kumar. "Healthcare Value-Based Reimbursement: A Predictive Analytics and Machine Learning Framework for Cost Optimization and Quality Improvement." International Journal of Advanced Robotics and Automation 7, no. 1 (2024): 1–9. https://doi.org/10.15226/2473-3032/7/1/00143.

Full text

Abstract:

Value-based reimbursement (VBR) models are revolutionizing healthcare by prioritizing quality outcomes and cost efficiency over service volume. However, their success hinges on the effective integration of healthcare data, advanced predictive analytics, and machine learning (ML) models to address cost forecasting, risk stratification, and compliance with HEDIS (Healthcare Effectiveness Data and Information Set) measures. This study proposes a robust framework leveraging cloud platforms, AI-driven analytics, and scalable data integration solutions to address these needs. Utilizing tools like AW

APA, Harvard, Vancouver, ISO, and other styles

29

Jayavelan Jayabalan and Devanand Ramachandran. "Optimizing Enterprise Intelligence: A Strategic Framework for Integrating Salesforce with Modern Cloud-Based Data Warehouses for Real-Time Unified Analytics." International Research Journal on Advanced Engineering Hub (IRJAEH) 3, no. 06 (2025): 3010–16. https://doi.org/10.47392/irjaeh.2025.0443.

Full text

Abstract:

Organizations need to turn silced data into real-time actionable intelligence in the modern data-driven economy to stay ahead of their competition. Now that organizations are rapidly embracing hybrid and multi-cloud environments, integrating customer relationship management (CRM) systems such as Salesforce with today’s modern cloud-based data warehouses (e.g., Snowflake, BigQuery, Redshift) is more important than ever. This paper provides a strategic model to leverage the full potential of enterprise intelligence by linking Salesforce with cloud-based data warehouses for real-time, hybrid anal

APA, Harvard, Vancouver, ISO, and other styles

30

Lawson, R. Paul, and Paquita Zuidema. "Aircraft Microphysical and Surface-Based Radar Observations of Summertime Arctic Clouds." Journal of the Atmospheric Sciences 66, no. 12 (2009): 3505–29. http://dx.doi.org/10.1175/2009jas3177.1.

Full text

Abstract:

Abstract Updated analyses of in situ microphysical properties of three Arctic cloud systems sampled by aircraft in July 1998 during the Surface Heat Budget of the Arctic Ocean (SHEBA)/First International Satellite Cloud Climatology Project (ISCCP) Regional Experiment–Arctic Clouds Experiment (FIRE–ACE) are examined in detail and compared with surface-based millimeter Doppler radar. A fourth case is given a cursory examination. The clouds were at 78°N over a melting ice surface, in distinctly different yet typical synoptic conditions. The cases comprise a midlevel all-ice cloud on 8 July; a dee

APA, Harvard, Vancouver, ISO, and other styles

31

Srikanth Yerra. "Optimizing Supply Chain Efficiency Using AI-Driven Predictive Analytics in Logistics." International Journal of Scientific Research in Computer Science, Engineering and Information Technology 11, no. 2 (2025): 1212–20. https://doi.org/10.32628/cseit25112475.

Full text

Abstract:

In modern supply chain management, shipping de- lays remain a significant issue, impacting customer satisfaction, operational effectiveness, and overall profitability. Traditional data processing methods don’t provide real-time information due to the latency in extracting, transforming, and loading (ETL) data from disparate sources. To alleviate this challenge, automated ETL processing combined with real-time data analytics offers an effective and scalable approach to minimizing shipping delays. This research explores the ways in which automated ETL workflows streamline shipping operations thr

APA, Harvard, Vancouver, ISO, and other styles

32

Karanam, Srinivasa Rao. "Understanding Snowflake Data Lake." International Scientific Journal of Engineering and Management 03, no. 12 (2024): 1–8. https://doi.org/10.55041/isjem02194.

Full text

Abstract:

Snowflake’s cloud-native design, decoupled storage-compute model, and capacity to handle semi- structured data might suggest a data lake–like architecture, its proprietary formats and higher costs under continuous workloads can hamper its effectiveness for large-scale raw data ingestion. Instead, organizations find it valuable to store the majority of raw or historical data in a dedicated data lake based on object storage (e.g., S3 or ADLS) and then selectively push curated data sets into Snowflake for advanced analytics and concurrency advantages. We examine the evolution of cloud-based data

APA, Harvard, Vancouver, ISO, and other styles

33

Singu, Santosh Kumar. "Leveraging Snowflake for Scalable Financial Data Warehousing." International Journal of Computing and Engineering 6, no. 5 (2024): 41–51. http://dx.doi.org/10.47941/ijce.2296.

Full text

Abstract:

Purpose: The study discusses the increasing challenges faced by financial services due to fast-growing transaction, regulatory, and client data, and the need for more flexible, scalable, and affordable data management systems. It examines the potential of Snowflake, a cloud-based data warehousing platform, to address these issues through its multi-cluster shared data architecture Methodology: The paper analyzes Snowflake's architecture, focusing on its ability to decouple storage from compute, allowing organizations to scale resources as needed. Case studies of financial institutions implement

APA, Harvard, Vancouver, ISO, and other styles

34

Chandrakanth, Lekkala. "Cloud-Based Data Warehousing Optimization Techniques." Journal of Scientific and Engineering Research 9, no. 5 (2022): 114–18. https://doi.org/10.5281/zenodo.12789974.

Full text

Abstract:

This article delves into enhancing cloud-based data warehousing's efficiency with the accompanying expert on Snowflake and Amazon Web Services (AWS). Companies are relying more and more on cloud systems for storing and analyzing data, and optimizing data warehousing now seems to be a really important part for performing well during queries, cutting down data storage costs, and managing data in a good way. This study delivers a case study that describes the optimization methods utilized in a Snowflake installation on AWS. This approach leads to performance improvements and cost savings. The opt

APA, Harvard, Vancouver, ISO, and other styles

35

Chinnathambi, Jinesh Kumar. "Amplifying Big Data Utilization in Healthcare Analytics Through Cloud and Snowflake Migration." European Journal of Computer Science and Information Technology 12, no. 6 (2024): 15–23. http://dx.doi.org/10.37745/ejcsit.2013/vol12n61523.

Full text

Abstract:

Amplifying the utilization of big data in healthcare analytics through cloud and Snowflake migration presents a significant opportunity to enhance data-driven insights and decision-making in the healthcare sector. This migration makes it easier to move large amounts of healthcare data to the cloud. Applications deployed in could are scalable for in-depth analysis in Health Care industry. The cloud is becoming more popular for storing data and running applications because it can easily grow with your needs, requires little to no management, improves security, and offers budget flexibility. The

APA, Harvard, Vancouver, ISO, and other styles

36

van Renen, Alexander, and Viktor Leis. "Cloud Analytics Benchmark." Proceedings of the VLDB Endowment 16, no. 6 (2023): 1413–25. http://dx.doi.org/10.14778/3583140.3583156.

Full text

Abstract:

The cloud facilitates the transition to a service-oriented perspective. This affects cloud-native data management in general, and data analytics in particular. Instead of managing a multi-node database cluster on-premise, end users simply send queries to a managed cloud data warehouse and receive results. While this is obviously very attractive for end users, database system architects still have to engineer systems for this new service model. There are currently many competing architectures ranging from self-hosted (Presto, PostgreSQL), over managed (Snowflake, Amazon Redshift) to query-as-a-

APA, Harvard, Vancouver, ISO, and other styles

37

Mantri, Arjun. "Advanced ML (Machine Learning) Techniques for Optimizing ETL Workflows with Apache Spark and Snowflake." Journal of Artificial Intelligence & Cloud Computing 2, no. 3 (2023): 1–6. http://dx.doi.org/10.47363/jaicc/2023(2)339.

Full text

Abstract:

The optimization of ETL (Extract, Transform, Load) pipelines using Apache Spark and Snowflake. Apache Spark is a powerful open-source distributed data processing platform, while Snowflake is a cloud-native data warehousing solution. It discusses the challenges and solutions in tuning Spark configurations using machine learning techniques and optimizing Snowflake's architecture for cost efficiency and performance. Experimental results demonstrate significant performance gains and cost savings through these optimizations.

APA, Harvard, Vancouver, ISO, and other styles

38

Nookala, Guruprasad. "Snowflake’s Role in Multi-Cloud Environments: Exploring the Integration and Interoperability of Snowflake across Different Cloud Platforms." International Journal of AI, BigData, Computational and Management Studies 6 (2025): 48–56. https://doi.org/10.63282/3050-9416.ijaibdcms-v6i2p106.

Full text

APA, Harvard, Vancouver, ISO, and other styles

39

Koreeda, Tatsuya, Hiroshi Honda, and Jun-ichi Onami. "Snowflake Data Warehouse for Large-Scale and Diverse Biological Data Management and Analysis." Genes 16, no. 1 (2024): 34. https://doi.org/10.3390/genes16010034.

Full text

Abstract:

With the increasing speed of genomic, transcriptomic, and metagenomic data generation driven by the advancement and widespread adoption of next-generation sequencing technologies, the management and analysis of large-scale, diverse data in the fields of life science and biotechnology have become critical challenges. In this paper, we thoroughly discuss the use of cloud data warehouses to address these challenges. Specifically, we propose a data management and analysis framework using Snowflake, a SaaS-based data platform. We further demonstrate its convenience and effectiveness through concret

APA, Harvard, Vancouver, ISO, and other styles

40

Satish Vadlamani, Raja Kumar Kolli, Chandrasekhara Mokkapati, Om Goel, Dr. Shakeb Khan, and Prof.(Dr.) Arpit Jain. "Enhancing Corporate Finance Data Management Using Databricks And Snowflake." Universal Research Reports 9, no. 4 (2022): 682–02. http://dx.doi.org/10.36676/urr.v9.i4.1394.

Full text

Abstract:

In today’s data-driven landscape, effective corporate finance data management is critical for informed decision-making and strategic planning. This study explores the integration of Databricks and Snowflake as a transformative solution for managing and analyzing corporate finance data. Databricks, with its robust analytics capabilities, provides a collaborative environment for data engineers and analysts, enabling real-time data processing and machine learning. Meanwhile, Snowflake offers a powerful cloud-based data warehousing platform that allows for scalable data storage and seamless integr

APA, Harvard, Vancouver, ISO, and other styles

41

Ramesh, Betha. "Databricks and Snowflake: The Battle of Big Data Supremacy." International Journal of Leading Research Publication 1, no. 1 (2020): 1–10. https://doi.org/10.5281/zenodo.14866788.

Full text

Abstract:

This paper examines the emerging competition between Databricks and Snowflake in the big data analytics market as of 2020. Through comparative analysis of their architectures, business models, and market positioning, we explore how these platforms are reshaping enterprise data management. The study highlights key differentiators in their approaches to data lake and data warehouse implementations, processing frameworks, and developer experiences. Our findings indicate that while both companies are driving innovation in big data analytics, their divergent strategies are creating distinct value p

APA, Harvard, Vancouver, ISO, and other styles

42

Researcher. "DATA WAREHOUSING WITH AMAZON REDSHIFT: REVOLUTIONIZING BIG DATA ANALYTICS." International Journal of Computer Engineering and Technology (IJCET) 15, no. 4 (2024): 395–405. https://doi.org/10.5281/zenodo.13270530.

Full text

Abstract:

The article talks about Amazon Redshift, a cutting-edge cloud-based data warehouse that is changing the way big data analytics is done. In it, the architecture, main features, and benefits of Redshift are discussed in detail. Columnar storage, massively parallel processing, and a distributed system design are emphasized. The article discusses how business intelligence, data science, operational analytics, customer analytics, and financial analytics are used in the real world. It also compares and contrasts with other cloud data stores, such as Snowflake and Google BigQuery, pointing out their

APA, Harvard, Vancouver, ISO, and other styles

43

Harsha Vardhan Reddy Goli. "Energy-aware workload scheduling in snowflake for sustainable big data computing." World Journal of Advanced Engineering Technology and Sciences 15, no. 2 (2025): 1572–83. https://doi.org/10.30574/wjaets.2025.15.2.0717.

Full text

Abstract:

With rising concerns over cloud energy consumption, this research proposes a novel energy-aware workload scheduler for Snowflake's virtual warehouses. The study integrates energy-efficiency metrics into Snowflake’s resource provisioning mechanisms, aiming to minimize the environmental footprint of Big Data queries. Using a dataset of 10 million historical job runs, the scheduler predicts compute demands using LSTM-based time series models and defers non-urgent workloads to periods of lower grid carbon intensity. Simulation results show a 35% reduction in carbon footprint with only a 5% increas

APA, Harvard, Vancouver, ISO, and other styles

44

Gehring, Josué, Alfonso Ferrone, Anne-Claire Billault-Roux, et al. "Radar and ground-level measurements of precipitation collected by the École Polytechnique Fédérale de Lausanne during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games." Earth System Science Data 13, no. 2 (2021): 417–33. http://dx.doi.org/10.5194/essd-13-417-2021.

Full text

Abstract:

Abstract. This article describes a 4-month dataset of precipitation and cloud measurements collected during the International Collaborative Experiments for PyeongChang 2018 Olympic and Paralympic winter games (ICE-POP 2018). This paper aims to describe the data collected by the Environmental Remote Sensing Laboratory of the École Polytechnique Fédérale de Lausanne. The dataset includes observations from an X-band dual-polarisation Doppler radar, a W-band Doppler cloud profiler, a multi-angle snowflake camera and a two-dimensional video disdrometer (https://doi.org/10.1594/PANGAEA.918315, Gehri

APA, Harvard, Vancouver, ISO, and other styles

45

Ujjawal, Nayak. "Building a Scalable ETL Pipeline with Apache Spark, Airflow, and Snowflake." INTERNATIONAL JOURNAL OF INNOVATIVE RESEARCH AND CREATIVE TECHNOLOGY 11, no. 2 (2025): 1–3. https://doi.org/10.5281/zenodo.15125062.

Full text

Abstract:

Extract, Transform, and Load (ETL) pipelines are critical in modern data engineering, enabling efficient data integration and analytics. This paper presents a scalable ETL pipeline leveraging Apache Spark for distributed data processing, Apache Airflow for workflow orchestration, and Snowflake as a cloud-based data warehouse. The proposed architecture ensures fault tolerance, cost efficiency, and high scalability, making it suitable for handling large-scale enterprise data workloads.

APA, Harvard, Vancouver, ISO, and other styles

46

Kumar, Rohit. "AI-Augmented Data Security in Cloud Migration: Leveraging Generative AI and Snowflake for Secure Financial Data Processing." INTERNATIONAL JOURNAL OF SCIENTIFIC RESEARCH IN ENGINEERING AND MANAGEMENT 09, no. 06 (2025): 1–8. https://doi.org/10.55041/ijsrem50146.

Full text

Abstract:

This paper offers a complete framework combining Snowflake's cloud data platform with AI- augmented methods to improve data security during cloud migration. Six strategic phases—from preprocessing to evaluation—each help to contribute to better performance measures in the suggested approach. Visual studies show a notable decrease in system response time (250 ms to 140 ms) as well as a continuous increase in security score (70% to 95%), and detection accuracy (68% to 94%). Moreover, accuracy and precision measures show clear development throughout the phases, reaching respectively 93% and 91%.

APA, Harvard, Vancouver, ISO, and other styles

47

Kashyap, Ravi. "Data Sharing, Disaster Management, and Security Capabilities of Snowflake a Cloud Datawarehouse." International Journal of Computer Trends and Technology 71, no. 02 (2023): 78–86. http://dx.doi.org/10.14445/22312803/ijctt-v71i2p112.

Full text

APA, Harvard, Vancouver, ISO, and other styles

48

Srinivasa, Rao Karanam. "Data Governance in the Cloud: Best Practices for Snowflake and Azure Synapse." International Journal of Innovative Research in Engineering & Multidisciplinary Physical Sciences 11, no. 4 (2023): 1–9. https://doi.org/10.5281/zenodo.15054578.

Full text

APA, Harvard, Vancouver, ISO, and other styles

49

Godleti, Srihari Babu. "Leveraging SonarQube and Snowflake for Advanced ETL Solutions." European Journal of Computer Science and Information Technology 13, no. 49 (2025): 153–62. https://doi.org/10.37745/ejcsit.2013/vol13n49153162.

Full text

Abstract:

This article examines the integration of SonarQube for code quality and Snowflake's cloud platform to address critical challenges in ETL (Extract, Transform, Load) processes. Organizations processing large datasets frequently encounter pipeline failures due to code inefficiencies and resource constraints. SonarQube's static analysis capabilities identify optimization opportunities and memory management issues before deployment, while Snowflake's decoupled architecture enables independent scaling of compute and storage resources. When combined, these technologies create a synergistic effect tha

APA, Harvard, Vancouver, ISO, and other styles

50

Researcher. "INTEGRATING SNOWFLAKE AND AI FOR CLOUD-BASED DATA WAREHOUSING IN OIL & GAS OPERATIONS." International Journal of Research In Computer Applications and Information Technology (IJRCAIT) 7, no. 2 (2024): 1449–59. https://doi.org/10.5281/zenodo.14191901.

Full text

Abstract:

This article explores the transformative integration of Snowflake and artificial intelligence technologies in cloud-based data warehousing for oil and gas operations. The article examines how this technological convergence addresses the industry's mounting challenges in managing vast data volumes generated from exploration, production, and operational activities. The investigation encompasses several key areas: scalable cloud infrastructure implementation, real-time processing capabilities through autonomic coordination systems, AI-driven predictive analytics, comprehensive data integration ar

APA, Harvard, Vancouver, ISO, and other styles

We offer discounts on all premium plans for authors whose works are included in thematic literature selections. Contact us to get a unique promo code!