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Cloud-Native ETL for Environmental Analytics

A 3-tier AWS Glue pipeline for assessment, measurement & analysis data

Built a sophisticated 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%.

PythonAWS GlueAWS S3Amazon RedshiftAWS LambdaPySparkSQLPower BI
Problem Statement

Surging data volumes in the environmental sector overwhelmed an ageing infrastructure — leading to sluggish processing, data inaccuracies and operational drag. Without a streamlined integration and transformation layer, timely, actionable insight stayed just out of reach.

  • Existing infrastructure could not absorb the scale and complexity of growing datasets.
  • Sluggish processing and data inaccuracies delayed analysis and decision-making.
  • No streamlined integration/transformation layer for structured and semi-structured data.
Headline Outcomes
+30%AWS Glue

Data-processing efficiency

−20%serverless pipelines

Processing time

Elasticcloud-native

Scalability

The Solution

A sophisticated, serverless 3-tier architecture on AWS — a comprehensive AWS Glue ETL process ingesting both structured and semi-structured data, with multiple load strategies (Incremental, Full, SCD Type 1 & Type 2) engineered to keep history accurate and pipelines efficient.

Serverless 3-tier architecture on AWS Glue, S3, Redshift, Lambda and PySpark.

Comprehensive Glue ETL handles seamless extract-transform-load of structured and semi-structured data.

Incremental, Full, SCD1 and SCD2 load strategies optimise both freshness and historical integrity.

Pipelines orchestrated through AWS Glue Studio and Lambda for elastic, hands-off scaling.

System Architecture

How the data flows

01

Raw Ingest

Structured + semi-structured

02

AWS Glue ETL

Extract & transform

03

Load Strategies

Incremental · Full · SCD1/2

04

Redshift

Analytics warehouse

05

Glue Studio + Lambda

Orchestration

Result 01

Enabled faster environmental insights and quicker decision-making.

Result 02

Delivered elastic scalability that adapts to evolving data needs.

Result 03

Replaced brittle batch jobs with resilient, serverless orchestration.

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Further reading

From the blog

Data Engineering

AWS Glue Cost Optimization: Why Your Bill Exploded and How to Cut It

A data engineer's guide to AWS Glue cost optimization: why DPU billing causes bill shock, the 8 traps that inflate your spend, and how to cut Glue costs 50%+.

AWS GlueCost OptimizationData EngineeringETL
Read Article11 min read
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