Utilizing Python and PySpark for Automating Data Workflows in Big Data Environments

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@article{1702916,
      author = {Afroz Shaik, Rahul Arulkumaran, Ravi Kiran Pagidi, Dr S P Singh, Prof. (Dr) Sandeep Kumar; Shalu Jain},
      title = {Utilizing Python and PySpark for Automating Data Workflows in Big Data Environments},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {5},
      number = {4},
      pages = {153-174},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1702916.pdf},
      abstract = {In the age of big data, organizations are increasingly seeking efficient solutions for managing and automating data workflows to process large volumes of data at scale. Python, a versatile programming language, and PySpark, an interface for Apache Spark, offer powerful tools for automating data workflows in distributed environments. This study explores the synergy between Python and PySpark to streamline data processing pipelines, reduce operational overhead, and enhance performance in big data ecosystems. Key areas of focus include automated data ingestion, transformation, and loading (ETL) processes, as well as the optimization of query performance using PySpark's distributed computing capabilities. By leveraging Python libraries and Spark?s parallelism, the integration facilitates real-time analytics, reduces latency, and ensures scalability in large-scale data environments. The research further delves into best practices for implementing automation frameworks, such as workflow schedulers and CI/CD pipelines, which ensure continuous deployment and data consistency. Ultimately, this study demonstrates how combining Python?s flexibility with PySpark?s scalability can significantly improve the efficiency of data operations, enabling organizations to derive actionable insights more quickly and effectively in today?s data-driven landscape.},
      keywords = {Python, PySpark, Big Data, Data Workflows, ETL Automation, Distributed Computing, Real-Time Analytics, Workflow Scheduling, Data Pipelines, Scalability, CI/CD, Data Processing Optimization, Parallel Computing, Spark Ecosystem, Automation Frameworks.},
      month = {October},
  }