A Comparative Study of Big Data Analytics Tools: Hadoop, Spark, TensorFlow, SAS, and Tableau in Modern Data Pipelines

Abstract

With the exponential growth of data in today\'s digital world, proper analysis has become crucial for extracting meaningful insights that enable predictive capabilities. These capabilities prove invaluable across industrial and business sectors for informed decision-making, risk assessment, and strategic planning. Big Data Analytics serves as the cornerstone of this process, employing specialized tools and techniques to handle massive, complex datasets characterized by volume, velocity, variety, veracity, and value. This paper examines the pivotal role of Big Data Analytics and its transformative impact across various domains. We focus on five powerful analytical tools - Hadoop, Spark, SAS, TensorFlow, and Tableau - each addressing distinct aspects of the data processing pipeline from storage and computation to advanced modeling and visualization. The study provides a comprehensive comparative analysis of these technologies, evaluating their architectural differences, performance efficiency, and real-world applicability in key industries including finance, healthcare, e-commerce, and artificial intelligence.

Introduction

1. What is Big Data?

Big Data refers to extremely large and complex datasets that cannot be managed with traditional analytical tools. It includes structured, semi-structured, and unstructured data generated rapidly in today’s digital world. To manage such data, organizations rely on Big Data Analytics for insight-driven decision-making.

The 5 Vs of Big Data:

• Volume: Size of data (from TB to PB)

• Velocity: Speed of data generation/processing

• Variety: Different data formats (text, video, logs, etc.)

• Veracity: Data quality and reliability

• Value: Usefulness of insights derived from data

2. Key Big Data Tools and Their Roles

3. Comparative Analysis Based on 5 Vs

4. The 5 Cs of Big Data Analytics

Tool Comparison Based on 5 Cs

5. Final Insights

• Hadoop is best for large-scale storage and batch jobs, especially in enterprise data lakes.

• Spark excels in real-time data analytics and machine learning pipelines.

• SAS is ideal for regulated environments requiring accurate statistical models.

• TensorFlow powers AI innovations like NLP, computer vision, and predictive analytics.

• Tableau democratizes data insights via visualization, making it accessible to non-technical users.

Organizations should choose tools based on their specific needs, considering trade-offs between cost, complexity, and capabilities.

Conclusion

Big Data Analytics plays an important function in today\'s environment. It is a key aspect of the organization\'s growth and development in response to developing market trends, as well as maintaining track of the past, present, and future. Handling enormous datasets necessitates the use of appropriate analytical tools for thorough data evaluation, ensuring that the output from those datasets is valuable. The above comparative analysis of several trending and demanding tools will assist users and data analysts in selecting the finest tool that meets their needs. Corporates, industries, and diverse enterprises rely heavily on these technologies to better their projects.

References

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Copyright

Copyright © 2025 Kiran Vishwakarma, Jasleen Kaur, Dr. Goldi Soni. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.