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OPTIMIZING BIG DATA WORKFLOWS IN AZURE DATABRICKS USING PYTHON AND SCALA (KEY IJW**********436)

• Ravi Kiran Pagidi, Rahul Arulkumaran, Shreyas Mahimkar, Aayush Jain, Dr. Shakeb Khan, Prof.(dr.) Arpit Jain

Abstract

In the era of big data, organizations are increasingly reliant on efficient data processing and analytics solutions. Azure Databricks, a unified analytics platform, offers powerful capabilities for managing large-scale data workflows. This study explores the optimization of big data workflows within Azure Databricks using Python and Scala, two prominent programming languages that cater to diverse analytical needs. The integration of these languages allows for leveraging their unique strengthsPython's simplicity and extensive library support, alongside Scala's performance efficiency and seamless compatibility with Apache Spark.The research highlights various techniques for optimizing data workflows, including data partitioning, caching strategies, and effective resource allocation. By implementing these strategies, users can enhance processing speeds, minimize costs, and improve overall performance. Furthermore, the study examines real-world case studies to illustrate the practical applications of optimized workflows, demonstrating significant improvements in data processing time and resource utilization.Ultimately, this work aims to provide a comprehensive framework for data engineers and analysts seeking to maximize the efficiency of their big data operations in Azure Databricks. The findings underscore the importance of selecting appropriate programming languages and optimization techniques to address the unique challenges posed by large datasets, paving the way for more efficient and scalable data-driven solutions in various industries.