International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

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Enhancing Scalability and Reliability of Batch Data Transformation Workflows Using Automation and Orchestration Tools

Author(s) Varun Garg
Country USA
Abstract Moving data around in large volumes within big businesses is a natural happening of business nowadays. With this exponential growth, the need for more reliable, scalable, and effective batch data transformation techniques becomes increasingly important. As the need for data processing increases, so too has the complexity of managing and overseeing such systems. Automation and orchestration technologies as Apache Airflow and AWS Step Functions greatly help to maximize batch operations by automating job execution, managing problematic dependencies, and improving fault tolerance. Apache Airflow is perfect for very flexible, code-driven procedures with simplicity for complex data pipelines. Conversely, AWS Step Functions provide a serverless architecture with strong connection with the AWS environment, therefore enabling perfect scaling and robust error-handling capability. Together with research of how different technologies manage scalability, reliability, and dependency management—the main challenges with batch data transformation—are examined in this paper. Moreover, a comparison of their benefits and disadvantages guides businesses in choosing the technology most appropriate for their specific need. Discussed are best practices for implementation and future trends in workflow automation including the integration of machine learning, real-time monitoring, and multi-cloud installations, therefore providing a whole picture of the shifting terrain of data engineering.
Keywords Batch Data Transformation, Apache Airflow, AWS Step Functions, Scalability, Reliability, Workflow Automation, Orchestration Tools, Fault Tolerance
Published In Volume 2, Issue 6, November-December 2020
Published On 2020-11-25
DOI https://doi.org/10.36948/ijfmr.2020.v02i06.22568
Short DOI https://doi.org/g82jbg
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