Overview
Cloud-native data engineering work spans Glue, S3, EMR, Spark, Athena and Redshift. These services support ingestion, transformation and analytical access across the data lifecycle.
Problem
A collection of cloud services needs clear data contracts, predictable execution and coherent operational ownership to function as a dependable platform.
My role
Develop ingestion and transformation pipelines, integrate data sources, orchestrate processing with Airflow and connect prepared data to analytics workloads.
Architecture
The conceptual flow stages source data in S3, prepares it through Glue and Spark / EMR, and makes it available through warehouse and analytical services. Athena supports querying cloud data.
- 01Sources
- 02S3
- 03AWS Glue
- 04Spark / EMR
- 05Redshift
- 06Analytics
Technical decisions
Separate storage, compute and serving responsibilities. Keep orchestration explicit, transformations maintainable and integration boundaries clear.
Challenges
Batch processing and downstream analytical requirements must fit together. Dependency management and monitoring are part of the platform design.
Data validation
Check data contracts at ingestion and transformation boundaries. Source-to-target reconciliation is a useful safeguard for analytical delivery.
- Schema
- Row count
- Partitions
- Null values
- Duplicates
- Data types
- Reconciliation
Performance
Use Spark execution plans and storage access patterns to guide distributed processing decisions. Avoid optimizing without evidence of a bottleneck.
Outcome
The work connects ingestion, scalable processing, cloud storage and warehousing into analytical pipelines.
Lessons learned
Cloud architecture is most understandable when each service has a clear responsibility and each handoff has an explicit data contract.