AD
adharsh277/INTERNSHIP-PROJECT-
End-to-end Azure-based data engineering pipeline for CRM analytics using Data Factory, Databricks, Synapse, and Power BI.
📊 Azure-Based CRM Data Engineering & Analytics Platform
📌 Project Overview
This project demonstrates the complete data engineering pipeline lifecycle for processing and analyzing CRM (Customer Relationship Management) data using Microsoft Azure cloud-native services. It showcases best practices in automation, scalability, and modular data workflows.
The project involves:
- Centralized cloud storage using Azure Data Lake
- ETL orchestration via Azure Data Factory
- Transformation using Databricks & PySpark
- Analytics modeling in Azure Synapse
- Reporting and dashboards via Power BI
- Source control via GitHub
Built with a real-world enterprise mindset, this system empowers organizations to extract valuable insights from their customer data and improve decision-making through visual analytics.
🚀 Technologies Used
| Stack | Tools/Services |
|---|---|
| ☁️ Cloud | Azure Data Lake, Data Factory, Databricks, Synapse Analytics |
| 🔁 ETL | Azure Data Factory Pipelines |
| 🔥 Processing | Databricks (Apache Spark, PySpark) |
| 🧠 Analytics | Azure Synapse (SQL on-demand, serverless pools) |
| 📊 BI | Power BI (CRM Dashboards & KPIs) |
| 💻 SCM | Git + GitHub |
| 📜 IaC | JSON (Factory pipelines), notebooks, SQL scripts |
🏗️ Architecture
CRM Raw Data
│
▼
Azure Data Factory (ETL Orchestration)
├── Load to Azure Data Lake (Raw Zone)
├── Trigger Databricks for transformation
│ └── PySpark jobs to clean & join data
└── Load to Azure Synapse SQL tables (Curated Zone)
└── Use in Power BI via Direct Query or Import
⚙️ Pipeline Flow
🔹 Ingestion Stage
Raw CRM datasets are imported into Azure Data Lake via Data Factory.
🔹 Transformation Stage
Databricks processes raw data using PySpark.
Data is cleaned, normalized, and transformed into analytics-ready format.
🔹 Analytics & Modeling
Transformed datasets are stored in Azure Synapse for SQL querying and modeling.
🔹 Dashboarding
Power BI connects to Synapse and delivers visual insights like:
📈 Customer Lifetime Value (CLV)
🔁 Retention & Churn Trends
🌍 Regional Behavior Analysis
📊 Sales Funnel Conversion
📁 Project Structure
bash
Copy
Edit
crm-data-platform/
├── data_factory/ # ADF pipeline JSONs
├── databricks/ # Notebooks (.dbc/.ipynb) for PySpark transformations
├── synapse/ # SQL scripts & table schema
├── powerbi/ # .pbix reports for CRM analysis
├── diagrams/ # Architecture PNGs or draw.io files
├── README.md # Documentation
└── .gitignore
🛠️ How to Run (Simplified View)
This is an Azure-native project and assumes that the resources are already provisioned.
Upload raw CRM CSV files into Azure Data Lake Gen2
Trigger Azure Data Factory to start the ETL pipeline
Review transformed output in Azure Synapse tables
Connect Power BI to Synapse (via Direct Query or Import)
Publish dashboards to Power BI Service
📌 Key Highlights
✅ End-to-End Data Engineering Lifecycle
✅ Real-World CRM Dataset Processing
✅ Scalable, Modular Pipeline Design
✅ Advanced Visual Reporting with Power BI
✅ Hands-on with Azure-native tools & automation
✅ Developed under Azure for Students subscription
📸 Sample Outputs (Screenshots)
Add these manually to your repo later:
🔄 ADF pipeline workflow
🧹 Databricks notebook transformation preview
📊 Power BI dashboard showcasing KPIs
📍 Use Cases
💼 Business Intelligence for CRM platforms
🛒 E-Commerce customer insights
📢 Sales + Marketing funnel optimization
🧱 Base pipeline architecture for data teams
🙋♂️ Author
Adharsh U
💡 Cloud & DevOps Enthusiast | Data Engineering | Python | Azure
📧 adharsh277@gmail.com