AWS Learning Track
AWS Data Engineer Learning Path
Build real-world data engineering systems using AWS-native and open-source technologies.
Deploy production-style pipelines covering ingestion, CDC, lakehouse architecture, orchestration, analytics, CI/CD, and exports — directly inside your own AWS account.
- 5+ production-style pipelines
- Glue, MWAA, Redshift, Iceberg, dbt, Spark, Kafka
- Batch ingestion, CDC, lakehouse, analytics, CI/CD
- Real deployment flows and system integration patterns
Follow the progression from ingestion to lakehouse, analytics, streaming, CI/CD, and exports.
INGESTION
Build ingestion systems using batch and CDC patterns to move source data into the platform reliably.
P01 — Batch Ingestion Pipeline
Build metadata-driven batch ingestion into the lake using cloud storage and processing services.
AWS • Glue • S3 • Batch
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P02 — Batch Ingestion Pipeline
Build metadata-driven batch ingestion into the lake using cloud storage and processing services.
AWS • Glue • S3 • Batch
Explore Pipeline →
LAKEHOUSE
Build scalable lakehouse architectures using Bronze, Silver, and Gold data layers with modern AWS tooling.
P03 — Silver Lakehouse Layer
Build metadata-driven batch ingestion into the lake using cloud storage and processing services.
AWS • Glue • S3 • Batch
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P04 — Gold Core Layer
Build metadata-driven batch ingestion into the lake using cloud storage and processing services.
AWS • Glue • S3 • Batch
Explore Pipeline →