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End to end Production Grade AWS Data Engineering Expertise: Glue, PySpark, S3, Redshift
As an AWS Data Engineer specializing in Glue, PySpark, EC2, S3, and Redshift, I bring comprehensive expertise in building scalable data pipelines and analytics solutions. My experience includes implementing ETL workflows using AWS Glue’s serverless architecture, optimizing PySpark transformations for large datasets, and designing efficient data lakes with S3 and Redshift integration.
I excel at implementing data quality frameworks, handling schema evolution, and building real-time streaming solutions. My technical foundation in both batch and streaming architectures allows me to architect end-to-end solutions that balance performance, cost, and maintainability. With hands-on experience in data governance using Lake Formation and security best practices, I deliver robust data engineering solutions that transform raw data into valuable business insights while maintaining compliance and governance standards.
1. How would you set up an ETL pipeline using AWS Glue to process data from S3 and load it into Amazon Redshift?
To set up an ETL pipeline using AWS Glue that processes data from S3 and loads it into Amazon Redshift, I would follow these steps:
