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This project involved building a robust **End-to-End ETL Pipeline** to process over **2.4 million transaction records**, correlated with real-time weather data, to identify patterns in withdrawal behavior.\n\n### 🎯 Business Objective\nTo analyze the impact of temporal, locational, and environmental factors (weather) on ATM usage, enabling the bank to:\n- 📉 Reduce operational costs by optimizing refill frequency.\n- 💳 Ensure high ATM availability during peak demand periods.\n- 🌦️ Understand how weather conditions affect withdrawal volumes.\n\n---\n\n## 🛠️ Tech Stack \u0026 Tools\n| Category | Technologies |\n| :--- | :--- |\n| **Big Data Engine** | Apache Spark (PySpark) |\n| **Data Ingestion** | Apache Sqoop, HDFS |\n| **Cloud Infrastructure** | AWS S3, AWS Redshift, AWS EMR (EC2) |\n| **Languages** | Python, SQL |\n| **Data Modeling** | Star Schema (Dimensional Modeling) |\n\n---\n\n## 🏗️ Data Pipeline Architecture\n\n1.  **Ingestion**: Leveraged **Apache Sqoop** to ingest raw transactional data from an RDS MySQL database into **HDFS**.\n2.  **Processing (ETL)**: Used **PySpark** to:\n    -   Perform complex data cleaning and schema enforcement.\n    -   Join transactional data with environmental weather datasets.\n    -   Transform flat data into a optimized **Star Schema**.\n3.  **Storage**: Exported processed dimensions and facts to **AWS S3** in optimized CSV/Parquet formats.\n4.  **Warehousing**: Loaded the finalized dimensional model into **Amazon Redshift** for high-performance analytical querying.\n\n---\n\n## 📊 Target Dimensional Model (Star Schema)\n\nThe architecture is built around a centralized Fact table connected to optimized Dimension tables.\n\n### 1. 📍 DIM_LOCATION\n*Captures granular geographical details of ATM placements.*\n- `location_id` (PK), `location_name`, `street_name`, `zipcode`, `latitude`, `longitude`.\n\n### 🏧 2. DIM_ATM\n*Details regarding ATM hardware and status.*\n- `atm_id` (PK), `atm_number`, `manufacturer`, `location_id` (FK).\n\n### 📅 3. DIM_DATE\n*Enables temporal analysis across hours, days, and seasons.*\n- `date_id` (PK), `full_timestamp`, `year`, `month`, `day`, `weekday`, `hour`.\n\n### 💳 4. DIM_CARD_TYPE\n*Categorizes transactions by card issuer.*\n- `card_type_id` (PK), `card_type`.\n\n### 💰 5. FACT_ATM_TRANS\n*The core metrics table containing millions of rows of transaction and weather data.*\n- `trans_id` (PK), `atm_id`, `location_id`, `date_id`, `card_type_id`, `amount`, `weather_id`, `temp`, `pressure`, `humidity`, etc.\n\n---\n\n## 📈 Analytical Capabilities\nWith this pipeline, recruiters and analysts can answer critical questions:\n- **Peak Hour Analysis**: *\"Which weekdays see the highest withdrawal volume between 5 PM and 8 PM?\"*\n- **Weather Impact**: *\"Do customers withdraw more cash during rainy weather compared to sunny days?\"*\n- **Location Performance**: *\"Which zip codes exhibit the highest ATM 'Inactive' status frequency?\"*\n- **Manufacturer Reliability**: *\"Which ATM manufacturer (NCR vs. Diebold) has fewer error messages?\"*\n\n---\n\n## 📁 Project Structure\n```bash\n├── SparkNordBank_SparkETLCode.py.ipynb  # Core ETL Logic (PySpark)\n├── SqoopDataIngestion.pdf               # Ingestion Configuration Details\n├── RedshiftSetup.pdf                    # Warehouse Configuration\n├── Redshift Analytical Queries.pdf       # SQL Insights \u0026 Reporting\n└── README.md                            # Project Documentation\n```\n\n---\n\n## 🚀 Key Takeaways\n- Successfully handled **large-scale data processing** using Spark's distributed computing.\n- Implemented **Industry-Standard Dimensional Modeling** to ensure fast query performance in Redshift.\n- Integrated **disparate datasets** (Banking + Weather) to provide 360-degree business insights.\n\n---\n**Developed by [Ankit Abhishek]** 👨‍💻\n*Passionate about building scalable data solutions and driving business value through analytics.*\n","project_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fankit21111%2Fsparnordetl","html_url":"https://awesome.ecosyste.ms/projects/github.com%2Fankit21111%2Fsparnordetl","lists_url":"https://awesome.ecosyste.ms/api/v1/projects/github.com%2Fankit21111%2Fsparnordetl/lists"}