Real-time retail data warehouse
Retail transaction processing with optimized joins, star-schema modeling, ETL discipline, and live analytics dashboards for operators.
Results at a glance
High-volume ingest with join logic tuned for reporting paths
Star-schema friendly modeling for predictable BI queries
Near-real-time dashboards instead of end-of-week-only snapshots
Challenge
Retail stakeholders were drowning in raw transactional exports: inconsistent keys, slow joins at reporting time, and dashboards that lagged the sales floor. They needed a compact warehouse-shaped layer plus interfaces leadership would actually open mid-week.
What Habrig built
- Streamlit + Plotly views for KPI drill-downs, store comparisons, and exception highlighting
- Parameterized slices so merchandising and ops can self-serve without new SQL each time
- Python ETL with Pandas and SQLAlchemy against MySQL for staged loads and incremental merges
- Fact and dimension alignment with explicit grain definitions to prevent double-counting
- Validation gates between bronze/silver-style stages so bad feeds fail loudly
- Scheduled jobs with retries and idempotent loads suited to nightly plus intraday refresh windows
- Documented restore assumptions so analysts trust historical parity after reruns
Outcomes
- More trustworthy headline metrics across channels and SKUs
- Shorter path from question to chart during promotions and stock events
- Cleaner handoffs between engineering-maintained pipelines and business-owned dashboards
Technology
frontend
Streamlit shells hosting Plotly visuals with role-appropriate defaults
backend
Python ETL orchestrating MySQL loads via SQLAlchemy with Pandas transforms
database
MySQL schemas biased toward star-schema reporting patterns
infrastructure
Scheduled workers sized to batch windows; connectivity hardened for retail VPC constraints
monitoring
Job success metrics, row-count deltas, and freshness timestamps surfaced to operators
cicd
Tests around transformation helpers plus migration-aware rollout for schema tweaks
Execution detail
Product & frontend
- Streamlit + Plotly views for KPI drill-downs, store comparisons, and exception highlighting
- Parameterized slices so merchandising and ops can self-serve without new SQL each time
Backend & data
- Python ETL with Pandas and SQLAlchemy against MySQL for staged loads and incremental merges
- Fact and dimension alignment with explicit grain definitions to prevent double-counting
- Validation gates between bronze/silver-style stages so bad feeds fail loudly
Platform & delivery
- Scheduled jobs with retries and idempotent loads suited to nightly plus intraday refresh windows
- Documented restore assumptions so analysts trust historical parity after reruns
Plan your next release
Tell us what shipped, what is at risk, and what success looks like. We will respond with a practical path.
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