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A Practical Guide to MLOps on AWS: Transforming Raw Data into AI-Ready Datasets with AWS Glue (Phase 02)
- Phase 02 involves transforming raw event data from the Bronze zone into cleaned Parquet files in the Silver zone and forecast-specific feature sets in the Gold zone for demand forecasting and personalized recommendations.
- The setup includes using AWS Glue Jobs for cleaning and transforming data, AWS Glue Crawlers for catalog metadata, AWS CDK Stack for provisioning resource, and Athena Queries for data validation.
- The S3 zones involved are the Bronze zone for raw data, Silver zone for cleaned data, and Gold zone for forecasting and recommendations-ready data.
- Steps include creating Glue resources via CDK, defining Glue Jobs and Crawlers, updating ETL scripts, running Glue Jobs to transform data, and validating table creation using Athena queries.
- The goal is to build a production-grade data lake with multi-zone architecture, automated ETL pipelines, schema discovery through Crawlers, and interactive querying via Amazon Athena.
- The article provides detailed instructions, AWS CDK code snippets, and walkthroughs for setting up Glue resources, running ETL jobs, and validating the transformed data.
- It emphasizes infrastructure-as-code with AWS CDK, setting up AI-ready, model-friendly, cost-efficient data pipelines for scaling and real-world cloud data platform design.
- The article concludes by hinting at Phase 3, where the data will be utilized for AI-based demand forecasting with Amazon Bedrock, showcasing the progression towards actionable insights and real-world applications.
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