🚀 📃 Data Engineering for Retail Sales Analytics 🔨

Introduction

Data engineering is fundamental for extracting insights from vast datasets in today's digital age. In the context of a retail sales analytics platform, efficient data engineering ensures seamless data processing, analysis, and deriving actionable insights.

Data Engineering

• Data engineering involves collecting, processing, and transforming raw data into a usable format for analysis.
• It focuses on building robust data pipelines, ensuring data quality, and optimizing data storage and retrieval.

Technologies Used in Data Engineering

• Teradata: Used for enterprise data warehousing and analytics.
• AWS (Amazon Web Services): Cloud computing platform offering various services like storage, computing, and analytics.
• Python: A versatile programming language used for data manipulation, analysis, and scripting.
• PySpark: Python API for Apache Spark, ideal for handling big data processing and analytics.
• Snowflake: Cloud-based data warehousing platform for storing and analyzing large volumes of data.

Retail Data

• Retail data encompasses information crucial for retailers, aiding in understanding customers, sales patterns, and inventory management.
• It plays a pivotal role in decision-making processes, enabling retailers to make informed choices to improve performance.

Pyspark Architecture Overview

1. Driver Program: Initiates Spark applications and manages their execution.
2. Cluster Manager: Orchestrates resources across a cluster for efficient computation.
3. Worker Node: Executes tasks assigned by the driver program within the cluster.

Business Requirements

Description:

• Develop a retail sales analytics platform to analyze sales data, customer behavior, and product trends.

Features:

1. Data Ingestion: Retrieve sales data from various sources like retail channels, POS systems, or databases.
2. Data Transformation: Clean, transform, and aggregate sales data to calculate metrics such as total sales and average order value.
3. Customer Segmentation: Segment customers based on purchase history, demographics, or behavior.
4. Trend Analysis: Identify product trends, seasonal patterns, and sales trends over time.

Challenges:

• Handling and integrating diverse data sources.
• Efficiently summarizing large volumes of sales data.
• Performing complex analytical queries for meaningful insights.

GitHub Repository Overview

• Report Link: GitHub Repository

Readme Highlights:

1. Overview: Introduction to the project, its significance, and tools used.
2. Retail Data: Explanation of retail data and its importance.
3. Technologies Used: Overview of the technologies employed in data engineering.
4. Architecture: Overview of the project's architecture.
5. Business Requirements: Description of project goals, features, and challenges.