End-to-End Data Engineering Pipeline

Table of Contents

• Project Overview
• Architecture
• Technologies
• Project Objectives
• Data Flow
• Setup and Configuration
• Pipeline Stages
• Visualization
• Machine Learning Models
• Folder Structure

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Project Overview

This project aims to build an end-to-end data engineering pipeline designed to extract, transform, and load (ETL) data into a central data warehouse for analysis and insights. The project integrates with cloud-based solutions such as Azure Data Factory for orchestrating pipelines, Azure Synapse Analytics for data storage and querying, and Power BI for visualization.

Additionally, machine learning models are incorporated to provide predictive analytics and forecasting for improved decision-making.

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Architecture

The architecture consists of several integrated Azure services for an efficient, scalable, and secure data pipeline.

• Azure Data Factory (ADF): Manages ETL pipelines.
• Azure Synapse Analytics: Acts as a data warehouse for storage and large-scale querying.
• Databricks: Enables advanced data transformation and machine learning.
• Power BI: Generates visual insights and dashboards.
• Azure Machine Learning: Supports machine learning model development and deployment.

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Technologies

This project uses the following tools and platforms:

• SQL Server or Relational Databases: Stores transactional data.
• Azure Data Factory: Orchestrates ETL operations.
• Azure Databricks: Handles large-scale data transformation and machine learning.
• Azure Synapse Analytics: Centralized data warehouse.
• Power BI: Visualization platform.
• Azure Machine Learning: For building and deploying predictive models.
• Python: Used for scripting transformations and machine learning.

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Project Objectives

1. Data Extraction: Pull data from structured or semi-structured sources.
2. Data Transformation: Clean, aggregate, and normalize data.
3. Data Loading: Store the processed data in a centralized data warehouse.
4. Data Visualization: Create dashboards for reporting and analytics.
5. Predictive Modeling: Leverage machine learning to forecast trends and provide insights.

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Data Flow

1. Source (SQL, CSV, etc.): Data is pulled from different data sources.
2. ETL in Azure Data Factory: ADF orchestrates the data extraction and transformation.
3. Data Transformation (Databricks): Data is processed, cleaned, and prepared for analytics.
4. Azure Synapse Analytics: The transformed data is loaded into Synapse for further analysis.
5. Power BI Dashboards: Connect to Synapse to visualize trends and insights.
6. Machine Learning Models: Predictive models are developed to forecast trends.

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Setup and Configuration

Prerequisites

• Azure Subscription: Access to Azure services like Data Factory, Synapse, Databricks, and Power BI.
• Database: A SQL Server instance or any other source where data is stored.
• Power BI Desktop: For designing data visualizations.

Azure Resource Setup

1. Create SQL Database: Import your dataset into a SQL Server.
2. Create Azure Data Factory (ADF): Set up data pipelines to extract and transform data.
3. Create Azure Synapse Analytics: Use Synapse for data storage and querying.
4. Create Azure Databricks: Perform large-scale data processing and machine learning tasks.
5. Power BI: Design dashboards to visualize insights from the data.

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Pipeline Stages

1. Data Extraction (Azure Data Factory)

• Use ADF to orchestrate the data extraction from various sources (SQL, CSV, API).

2. Data Transformation (Databricks)

• Perform complex transformations using Databricks and Apache Spark for distributed data processing.

3. Data Loading (Azure Synapse Analytics)

• Load the cleaned and transformed data into Azure Synapse Analytics for storage and analysis.

4. Machine Learning (Databricks)

• Build and train machine learning models using Databricks and track experiments with MLflow.

5. Data Visualization (Power BI)

• Create interactive dashboards to visualize KPIs, trends, and predictive insights.

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Visualization

Power BI Dashboards:

• Performance Overview: Analyze KPIs like sales, revenue, and customer retention.
• Predictive Analysis: Use historical data to forecast trends and behaviors.
• Inventory and Sales Insights: Manage stock levels and predict demand.

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Machine Learning Models

• Forecasting Models: Predict trends based on historical data.
• Classification Models: Segment customers based on behavior and preferences.
• Demand Prediction: Optimize inventory and supply chain using demand forecasting.

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Folder Structure

├── datasets                # Raw and processed data files
├── notebooks               # Jupyter notebooks for data exploration and ML modeling
├── pipelines               # Azure Data Factory pipeline definitions
├── scripts                 # Python scripts for data transformation and ML
├── visuals                 # Power BI report files and dashboards
└── README.md               # Project documentation