Hillary Nyawate
WATEX DATA SOLUTIONS
ENGINEERING CASE STUDIES
Projects
Independent, public and reproducible work. Each project connects a capability to architecture, implementation, testing, results and lessons.
DATA WAREHOUSING / PERFORMANCE
Redshift Performance Engineering
Problem: Understand how query shape, distribution and workload affect analytical performance.
Architecture: Synthetic workload, warehouse tables, explain plans and repeatable benchmark scripts.
Evidence: Measured findings and documented limits.
Lesson: Optimization starts with workload understanding.
DATA ENGINEERING
AWS Data Warehouse Laboratory
Problem: Build a small analytical platform from public or synthetic data.
Architecture: Storage, ingestion, transformation, warehouse modeling and analytics layers.
Testing: Schema, freshness and data-quality checks.
Lesson: Reliability belongs in the pipeline design.
RELIABILITY / QUALITY
Data Quality & Reliability Pipeline
Problem: Detect freshness, schema and constraint failures before downstream analysis.
Implementation: SQL, Python and open-source quality patterns.
Result: Repeatable validation and failure documentation.
Lesson: A trustworthy dataset needs observable contracts.
Future case studies: QuickSight Analytics · Kafka Streaming · Warehouse Migration · Cloud Data Architecture. Public artifacts will be linked when ready.
