Demo project (sample data): a sales KPI dashboard built end to end, from the Python ETL pipeline and SQL KPI queries to a one-page view of revenue, orders, average order value, refund rate, monthly revenue trend, category share and top cities. It shows how I take raw data all the way to a report a business owner can read at a glance.
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Demo project (sample data): SQL KPI analysis on a star schema (fact_sales with date, product and customer dimensions). Uses CTEs and window functions (LAG, RANK, SUM OVER) to calculate monthly revenue, order counts, average order value, month-over-month growth, revenue share by category, top cities and refund rate. The same queries feed the KPI dashboard demo.
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Demo project (sample data): a Python ETL pipeline that takes messy CSV and JSON files (duplicates, mixed date formats, inconsistent casing, missing prices), cleans and validates them, writes failed rows to a rejects table with a reason instead of dropping them, and loads a star schema (fact + 3 dimensions). A reconciliation check confirms every input row is accounted for. On the sample run: 5,108 rows in, 108 duplicates removed, 315 rejected with reasons, 4,685 loaded.
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Final year project (BS Computer Science, University of Haripur): a secure cloud storage design on Hadoop. Data passes through an access control module and dual-layer encryption (row-wise and column-wise) before it is stored in HDFS, improving data security on the cluster. Blockchain-based improvements were proposed as future work.
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