Build AWS ETL Pipelines

I want to stand up a set of robust, production-ready ETL pipelines on AWS that move both structured and semi-structured data from raw ingestion through to curated and reporting layers in our S3 data lake. The core services in scope are AWS Glue for orchestration and PySpark transforms, S3 for the lake storage, and AWS Lambda for lightweight triggers, monitoring, and automated recoveries.

Here is the high-level flow I have in mind:

• Land daily and near real-time source files (CSV, JSON) in an S3 raw zone.
• Use Glue jobs written in PySpark to clean, join, and enrich the data, then write partitioned Parquet into a curated zone, applying SCD-1/2 logic where needed.
• Persist final, analytics-ready tables in a reporting zone, queryable from Athena and Redshift Spectrum.
• Wire Lambda functions to kick off Glue jobs on new file arrival, push CloudWatch metrics, and send SNS alerts on failures or SLA breaches.

Acceptance criteria
• Pipelines run end-to-end through dev, test, and prod accounts using IaC (CloudFormation or Terraform).
• Average transformation job completes within the agreed SLA and shows at least the 40 % query-time improvement we see from Parquet/partitioning.
• All operational dashboards, retries, and alerts are in place and demonstrated during hand-over.
• Code, configs, and documentation are delivered in a Git repo with clear read-me instructions for future maintenance.

If you have hands-on experience building Glue-based ETL for mixed data types and can bring best practices around schema evolution, cost optimisation, and monitoring, let’s get started.