📊 Stock Insights Dashboard – Power BI + dbt + PostgreSQL

📌 Overview

A complete Analytics Engineering project that uses cleaned stock data to create a 3-page Power BI dashboard with dbt models and PostgreSQL integration. Built to demonstrate data wrangling, modeling, and visualization skills in a realistic scenario.

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🛠️ Tech Stack

Tool	Purpose
Power BI	Data modeling and visualization
dbt Core	Data transformation and aggregation
PostgreSQL	Data storage
Python	CSV ingestion and preprocessing
Git + GitHub	Version control and sharing

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📁 Folder Structure

stock-insights-dashboard/
├── data/                     # Raw + cleaned CSV files
├── dbt_project/              # dbt models, sources, schema
│   ├── models/
│   └── dbt_project.yml
├── scripts/                  # Python ingestion + cleaning
├── dashboard/                # .pbix file of Power BI dashboard
├── .gitignore
├── README.md
└── requirements.txt

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📊 Dashboard Pages

1️⃣ Page 1 – Overview

• 🔹 Average Volume by Sector (Pie Chart)
• 🔹 Average Close by Sector (Column Chart)
• 🔹 Sector Slicer

2️⃣ Page 2 – Stock Volatility & Movement

• 🔹 Line Chart: Close over Time by Sector
• 🔹 Bar Chart: Sector vs Total Volume
• 🔹 Column Chart: Sector vs Avg Close
• 🔹 Sector Slicer

3️⃣ Page 3 – Deep Dive by Company

• 🔹 Slicer: Ticker
• 🔹 Card KPIs: Avg Volume, Avg Close
• 🔹 Line Chart: Close Price Over Time
• 🔹 Table: Full Record View by Company

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🧹 Data Pipeline

• 🟡 Source: Kaggle June 2025 Stock Dataset
• ⚙️ Cleaned using Python → Loaded into PostgreSQL
• 🔁 Modeled in dbt using stock_summary, sector_aggregates, etc.
• 📈 Visualized with Power BI

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👨‍💼 Author

Mugheer Islam Khattak

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🧠 Future Plans

• Automate data ingestion using APIs or schedulers
• Extend dashboard with financial ratios
• Try hosting Streamlit version