International Journal For Multidisciplinary Research

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Orchestrating Data Pipelines on AWS: Leveraging Step Functions and SageMaker

Author(s) Syed Ziaurrahman Ashraf
Country USA
Abstract In this paper, we explore the orchestration of data pipelines on Amazon Web Services (AWS) using AWS Step Functions and Amazon SageMaker. These two services provide a powerful combination to streamline, automate, and scale complex workflows that involve data ingestion, transformation, model training, and inference for machine learning. This paper delves into the architecture, components, and best practices for leveraging AWS Step Functions and SageMaker to build efficient data pipelines. By using a serverless approach, organizations can minimize infrastructure overhead, scale easily, and focus on extracting value from their data. Visualizations such as diagrams and pseudocode are provided to guide developers in implementing their solutions. By combining these two, we can create end-to-end pipelines that handle everything from raw data ingestion, model training, and deployment to real-time inference. We provide detailed architecture diagrams, flowcharts, pseudocode, and example scripts to simplify implementation. The goal is to help data engineers and machine learning developers build scalable, automated pipelines on the cloud without managing servers.
Keywords AWS Step Functions, Amazon SageMaker, Data Pipelines, Machine Learning, Orchestration, Serverless, Model Training, Data Ingestion, Automati
Field Engineering
Published In Volume 2, Issue 2, March-April 2020
Published On 2020-03-25
Cite This Orchestrating Data Pipelines on AWS: Leveraging Step Functions and SageMaker - Syed Ziaurrahman Ashraf - IJFMR Volume 2, Issue 2, March-April 2020. DOI 10.36948/ijfmr.2020.v02i02.19097
DOI https://doi.org/10.36948/ijfmr.2020.v02i02.19097
Short DOI https://doi.org/g82h77
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