↥MODULE 01
Cloud foundations
Set up secure access, storage, and repeatable development workflows before moving data.
OPEN-SOURCE • HANDS-ON • AWS
Build data systems.
Not just pipelines.
A practical engineering guide to designing reliable data workflows on AWS, from raw ingestion to analytics-ready data. Learn the building blocks, understand the trade-offs, and ship with confidence.
pipeline_flow.py
$ pipeline.run(environment="production")
# orchestration started
✓ ingest.source ────────── complete
✓ validate.schema ──────── complete
✓ transform.records ────── complete
✓ publish.curated ──────── complete
EXECUTION STATUS ● READY
01 / IngestBring data in reliably
02 / TransformShape data at scale
03 / QueryMake data useful
04 / OrchestrateOperate with confidence
The learning path
From first object to
production workflow.
Explore the core capabilities behind modern AWS data platforms. Each area can grow into focused notes, examples, and runnable implementations.
⇄MODULE 02
Data ingestion
Understand batch and streaming ingestion, source connectivity, and event-driven patterns.
⟳MODULE 03
ETL & storage
Transform datasets with distributed compute and design storage layouts for efficient reads.
⌕MODULE 04
Analytics & query
Catalog datasets and use serverless SQL to explore, validate, and serve curated data.
⌘MODULE 05
Orchestration
Coordinate dependencies, retries, schedules, alerts, and observable pipeline runs.
⛭MODULE 06
Delivery & automation
Version infrastructure and automate testing and deployment with controlled changes.
Reference architecture
A clear path from source
to trusted data.
A simple layered model makes ownership, data quality, and operational failures easier to reason about.
DATA FLOW
↥
Source systemsFiles, databases, event streams
↓
▤
Raw layer · Amazon S3Immutable landing data and source traceability
↓
⟳
Transform · AWS Glue / PySparkSchema checks, cleaning, deduplication
↓
◈
Curated layerPartitioned, analytics-ready datasets
↓
⌕
ConsumptionAthena, dashboards, downstream products
ENGINEERING PRINCIPLES
Make the pipeline resilient by design.
Successful pipelines are not only about moving records. They preserve correctness when jobs retry, data arrives late, schemas change, and dependencies fail.
- Idempotent writes and safe retries
- Explicit schemas and data quality checks
- Incremental processing and checkpoints
- Partitioning aligned with query patterns
- Logs, metrics, alerts, and recovery steps
- Least-privilege access and secrets hygiene
Build in stages
A roadmap you can execute.
Start with the fundamentals, then add operational complexity only when the workflow needs it.
PHASE 01
Foundations
IAM, S3, CLI, SDK basics, project structure.
PHASE 02
Ingestion & ETL
Load source data, transform with Glue, write curated outputs.
PHASE 03
Query & orchestration
Catalog data, query with Athena, schedule and monitor jobs.
PHASE 04
Production patterns
Infrastructure as code, CI/CD, testing, recovery, cost control.
Open-source guide
Learn it. Build it. Improve it.
This guide is a living reference for hands-on AWS data engineering. Browse the repository, follow the implementation notes, and contribute improvements as the project evolves.