End-to-End Data Analytics Project: SQL, Python-ETL & Power BI in FinTech

End-to-End FinTech Data Analytics Project:
📊 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗢𝘃𝗲𝗿𝘃𝗶𝗲𝘄:
This project focused on analyzing transactional data to extract actionable insights using:
Source Data→ CSV files & SQL queries
Database→ MySQL, managed via DBeaver
ETL Pipeline→ Built and automated with Python
Data Analysis→ Performed with SQL
Visualization→ Insights showcased in Power BI
🔑 Key KPIs:
1️⃣ Total unique customers
2️⃣ Month-wise transaction analysis
3️⃣ Regional and area-wise customer distribution
4️⃣ Transaction type analysis with balances, percentages
5️⃣ Monthly active customers (MAC)
6️⃣ Customer-wise and month-end closing balances
7️⃣ Latest 5-day deposits & withdrawals
8️⃣ 5-day interval transaction analysis
9️⃣ Weekly comparison of deposits & withdrawals
🔟 15-day forecast for deposits & withdrawals
✨ 𝐎𝐮𝐭𝐜𝐨𝐦𝐞: Delivered a robust analytics solution for FinTech decision-makers to monitor performance, identify trends, and forecast effectively.
📌 𝗣𝗿𝗼𝗷𝗲𝗰𝘁 𝗣𝗿𝗼𝗴𝗿𝗲𝘀𝘀 :
🎯 Phase 1: 𝗗𝗮𝘁𝗮𝗯𝗮𝘀𝗲 𝗦𝗲𝘁𝘂𝗽 𝗮𝗻𝗱 𝗘𝗧𝗟 𝗣𝗿𝗼𝗰𝗲𝘀𝘀
1. Created two databases in MySQL:
→ LIVE Database
→ WH Database
2. Performed the following data operations:
→ Inserted data for the 𝗥𝗲𝗴𝗶𝗼𝗻 and 𝗔𝗿𝗲𝗮 tables in the LIVE database, load from the LIVE database to the WH database using full truncate.
→ Imported the 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿𝘀_𝘁𝗿𝗮𝗻𝘀𝗮𝗰𝘁𝗶𝗼𝗻 table into the LIVE database, then loaded it incrementally into the WH database.
→ Extracted the 𝗰𝘂𝘀𝘁𝗼𝗺𝗲𝗿_𝗷𝗼𝗶𝗻𝗶𝗻𝗴_𝗶𝗻𝗳𝗼 table directly from a drive and loaded it into the WH database with full truncate ETL process using Python.
🎯 Phase 2: 𝗦𝗼𝗹𝘃𝗶𝗻𝗴 𝗞𝗣𝗜𝘀 𝘄𝗶𝘁𝗵 𝗦𝗤𝗟
Used SQL to calculate all KPIs by employing a variety of techniques, including:
→ Different statements, operators, and functions
→ Aggregated functions and GROUP BY clauses
→ Various types of joins
→ Window functions
→ Sub-queries and Common Table Expressions (CTEs)
🎯 Phase 3: 𝗟𝗼𝗮𝗱𝗶𝗻𝗴 𝗗𝗮𝘁𝗮 𝘁𝗼 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜
Imported datasets from MySQL databases into Power BI using the ‘localhost' Server and ‘wh_online_banking’ database via SQL statements.
🎯Phase 4: 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴 𝗮𝗻𝗱 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻 𝗶𝗻 𝗣𝗼𝘄𝗲𝗿 𝗕𝗜
1. 𝗗𝗮𝘁𝗮 𝗠𝗼𝗱𝗲𝗹𝗶𝗻𝗴:
→ Created required measures, calculated columns, and custom columns in Power Query using M-Language.
2. 𝗩𝗶𝘀𝘂𝗮𝗹𝗶𝘇𝗮𝘁𝗶𝗼𝗻𝘀:
→ Chose appropriate visuals for better representation:
Card, Card (New), Area Chart, Bar Chart, Column Chart, Donut Chart, and Matrix (Table)
→ Added tooltips, drill-throughs, and page navigation for detailed insights.
→ Used slicers, tile slicers, bookmarks, and conditional formatting for dynamic visualization.
3. 𝗧𝗶𝗺𝗲 𝗦𝗲𝗿𝗶𝗲𝘀 𝗔𝗻𝗮𝗹𝘆𝘀𝗶𝘀:
Used Line Chart with forecasting advanced functions.
🎯 Phase 5: 𝗗𝗲𝗽𝗹𝗼𝘆𝗺𝗲𝗻𝘁
→ Developed an interactive dashboard in Power BI.
→ Published it on the Web Service for live browsing.
𝐂𝐨𝐧𝐜𝐥𝐮𝐬𝐢𝐨𝐧:
To improve the system, negative balances should be addressed as they are entirely forbidden and unexpected in financial operations. Controlling this issue is critical for maintaining data integrity.
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#DataAnalytics #SQL #Python #ETL #PowerBI #FinTechAnalytics #ETLPipeline #MySQL #DataVisualization #BusinessIntelligence #PowerBIDashboard #KPIs #DataModeling #TimeSeriesAnalysis #InteractiveDashboard #DataIntegrity #FinancialAnalytics #TransactionalData #Forecasting #DataPipeline #DAX

[@DataSoldierBelayet]

🎯

[@valentin4069]

could you shere the foldrs and data