Netflix Data Cleaning & Analysis Pipeline
Syed Muhammad Adil
Data Visualizer
Data Analyst
Data Engineer
Matplotlib
Microsoft SQL Server
Python
Overview
This project demonstrates a comprehensive data engineering pipeline for cleaning and analyzing Netflix data. The pipeline covers the entire ETL (Extract, Transform, Load) process, in-depth exploratory data analysis (EDA) using Python and SQL, and the generation of business reports based on the analysis.
Project Workflow
Data Sources: Extracting data from Netflix CSV files.
ETL Process:
Extract: Loading raw data into Python.
Transform: Cleaning and processing data to extract insights, such as unique directors, country counts, top 5 genres, and TV shows with more than one season.
Load: Storing the cleaned data into SQL Server.
Store Transformation Results: Saving transformation results into Excel files for business use.
Data Storage: Ensuring data is stored securely in SQL Server.
EDA & SQL Analysis: Conducting exploratory data analysis and additional SQL-based analysis.
Business Reports: Creating detailed reports to facilitate data-driven decision-making.
Features
Data Extraction: Pulled raw data from Netflix using CSV files.
Data Transformation: Cleaned and transformed data to extract meaningful insights.
Data Loading: Loaded the clean data into SQL Server for robust storage.
Exploratory Data Analysis (EDA): Performed in-depth analysis using Python and SQL to uncover valuable trends.
Business Use Cases: Generated reports to assist in strategic business decisions.
