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International Journal for Research Trends and Innovation
International Peer Reviewed & Refereed Journals, Open Access Journal
ISSN Approved Journal No: 2456-3315 | Impact factor: 8.14 | ESTD Year: 2016
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Issue: April 2026
Volume 11 | Issue 4
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Impact Factor : 8.14
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| Paper Title: | Scalable Big Data Analytics Framework For Financial Time – Series Using Snowflake, Pyspark And Machine Learning |
| Authors Name: | Y. P. Srinath Reddy , Nithin Kodamala , Latheesh Kandhi , Vishnu Madanambeti |
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IJRTI_211287
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| Published Paper Id: | IJRTI2604197 |
| Published In: | Volume 11 Issue 4, April-2026 |
| DOI: | https://doi.org/10.56975/ijrti.v11i4.211287 |
| Abstract: | Massive amounts are created in the financial trading activities. Time-series data on daily basis, which is difficult to deal with. traditional systems of data processing and analysis. This project introduces a big data analytics framework (cloudbased) that is created to. manage and analyze financial timeseries data, with speed and efficiency. specific focus on the price analysis of coffee commodity. The proposed system applies a full data engineering. flow where raw financial data is stored in Amazon S3. then cleaned in the PySpark to a distributed data cleaning, modification, and combination. The transformed data sets are stored in Snowflake which is a scalable cloud data. fast analytical queries and reporting warehouse. A Machine Learning layer is supplemented on the analytics layer. to determine past price dynamics and create future trend. predictions based upon the processed time-series data. The framework is structured on top of a layered architecture, which contains data storage, ETL data processing, cloud data warehousing, analytics and machine. learning components. The findings indicate that the framework is is able to process page-scale size financial time-series data. scaling, flexibility, efficiency, and scalability. performance. Machine learning enhances the presence of machine learning. system analytical abilities by allowing foresight, and the framework can be scaled with the help of the cloud-based design. increasing data volumes. This system is also in favour of future. improvements like real-time data entry, sophisticated. prediction methods and prediction of several commodities. |
| Keywords: | Big Data Analytics, Financial Time-Series Analysis, Coffee Commodity Price Prediction, Cloud-Based Data Engineering, Distributed Data Processing, Machine Learning |
| Cite Article: | "Scalable Big Data Analytics Framework For Financial Time – Series Using Snowflake, Pyspark And Machine Learning", International Journal for Research Trends and Innovation (www.ijrti.org), ISSN:2456-3315, Vol.11, Issue 4, page no.b447-b451, April-2026, Available :http://www.ijrti.org/papers/IJRTI2604197.pdf |
| Downloads: | 00055 |
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2456-3315 | IMPACT FACTOR: 8.14 Calculated By Google Scholar| ESTD YEAR: 2016 An International Scholarly Open Access Journal, Peer-Reviewed, Refereed Journal Impact Factor 8.14 Calculate by Google Scholar and Semantic Scholar | AI-Powered Research Tool, Multidisciplinary, Monthly, Multilanguage Journal Indexing in All Major Database & Metadata, Citation Generator |
| Publication Details: |
Published Paper ID: IJRTI2604197
Registration ID:211287
Published In: Volume 11 Issue 4, April-2026
DOI (Digital Object Identifier): https://doi.org/10.56975/ijrti.v11i4.211287
Page No: b447-b451 Country: Nandyal, Andhra Pradesh, India Research Area: Computer Science & Technology Publisher : IJ Publication Published Paper URL : https://www.ijrti.org/viewpaperforall?paper=IJRTI2604197 Published Paper PDF: https://www.ijrti.org/papers/IJRTI2604197 |
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ISSN: 2456-3315
Impact Factor: 8.14 and ISSN APPROVED,
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