A 3-tier AWS Glue pipeline for assessment, measurement & analysis data
Built a 3-tier data architecture on AWS for an environmental-solutions provider, using AWS Glue, S3, Redshift, Lambda and PySpark to lift data-processing efficiency 30% and cut processing time 20%.
Data volumes had outgrown the provider’s old infrastructure. Processing was slow, records had accuracy issues, and day-to-day operations dragged. With no single integration and transformation layer, timely insight stayed out of reach.
Data-processing efficiency
Processing time
Scalability
A serverless 3-tier AWS Glue pipeline that ingests both structured and semi-structured data, with incremental, Full and SCD Type 1 & Type 2 loads so each run only touches changed data and history stays accurate.
Serverless 3-tier architecture on AWS Glue, S3, Redshift, Lambda and PySpark.
Glue ETL extracts, transforms and loads both structured and semi-structured data.
Incremental, Full, SCD1 and SCD2 loads balance freshness against historical integrity.
Pipelines orchestrated through AWS Glue Studio and Lambda so scaling is hands-off.
Structured + semi-structured
Extract & transform
Incremental · Full · SCD1/2
Analytics warehouse
Orchestration
Enabled faster environmental insights and quicker decision-making.
Delivered elastic scalability that adapts to evolving data needs.
Replaced brittle batch jobs with resilient, serverless orchestration.
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From messy source data to analytics-ready warehouses that cut cost. Let's scope it. I reply within one business day.