SQL Database Analysis: Instagram Clone Project
May 20, 2025
Marketing Analytics
Project Description
This project simulates a simplified version of Instagram’s backend data system to analyze user behavior, content engagement, and platform activity using SQL. It includes a relational database schema, analytical queries, and marketing insights based on real-world patterns like followers, likes, hashtags, and post behavior.
It was built using MySQL, managed using MySQL Workbench, and version-controlled through GitHub Desktop. The project is designed as a portfolio case study to demonstrate real-world SQL querying, data structuring, and analytical thinking.
Introduction
The goal of this project was to mimic the structure of a social media platform like Instagram and to extract meaningful insights through raw SQL queries. The database includes core tables such as users, photo, comment, likes, follows, tags, and photo_tags, all normalized and enforced through primary and foreign key constraints.
Over a series of exploratory and analytical queries, the project surfaces patterns in user behavior, platform usage, content performance, and social interactions — enabling a mock marketing or product team to make informed decisions.
Key Stats and Insights
User Base Activity
The platform has 99 users.
26 users (about 26%) are inactive — they’ve never posted anything.
The average number of posts per user is 2.57, but is skewed by inactive users.
Registration Patterns
Registrations are fairly evenly distributed across the week, with Thursday being most popular and Saturday the least.
Top 5 oldest users registered over a span of 21 days, newer users within 16 days — an indication of slow but steady growth.
Engagement Highlights
The most liked photo (ID 145) belongs to Zack-Kemmer93 with 48 likes, nearly 50% of the entire user base.
The average likes per photo is a bit misleading — consistent posters aren’t necessarily the most liked users.
Top hashtags include common terms like #smile, #beach, and #party, indicating general content themes.
Social Network Dynamics
On average, each user has 76.23 followers, showing strong user interconnectivity.
The followers-to-following ratios reveal anomalies, likely caused by bot accounts, suggesting the importance of filtering quality vs quantity in user metrics.
Key Learnings
Schema design matters: Normalizing the schema upfront makes writing and scaling queries much easier later.
SQL alone can reveal marketing value: Even without dashboards or BI tools, well-crafted SQL provides deep product and user insights.
Raw averages can lie: When inactive users or outliers exist, average-based metrics (like posts or likes) need contextual interpretation.
Data integrity is essential: Enforcing foreign keys and using clear constraints prevents logical errors and supports accurate joins.
Following is the link attached to access the Github repo with a readme file to understand the repo.
SQL Database Analysis: Instagram Clone Project
May 20, 2025
Marketing Analytics
Project Description
This project simulates a simplified version of Instagram’s backend data system to analyze user behavior, content engagement, and platform activity using SQL. It includes a relational database schema, analytical queries, and marketing insights based on real-world patterns like followers, likes, hashtags, and post behavior.
It was built using MySQL, managed using MySQL Workbench, and version-controlled through GitHub Desktop. The project is designed as a portfolio case study to demonstrate real-world SQL querying, data structuring, and analytical thinking.
Introduction
The goal of this project was to mimic the structure of a social media platform like Instagram and to extract meaningful insights through raw SQL queries. The database includes core tables such as users, photo, comment, likes, follows, tags, and photo_tags, all normalized and enforced through primary and foreign key constraints.
Over a series of exploratory and analytical queries, the project surfaces patterns in user behavior, platform usage, content performance, and social interactions — enabling a mock marketing or product team to make informed decisions.
Key Stats and Insights
User Base Activity
The platform has 99 users.
26 users (about 26%) are inactive — they’ve never posted anything.
The average number of posts per user is 2.57, but is skewed by inactive users.
Registration Patterns
Registrations are fairly evenly distributed across the week, with Thursday being most popular and Saturday the least.
Top 5 oldest users registered over a span of 21 days, newer users within 16 days — an indication of slow but steady growth.
Engagement Highlights
The most liked photo (ID 145) belongs to Zack-Kemmer93 with 48 likes, nearly 50% of the entire user base.
The average likes per photo is a bit misleading — consistent posters aren’t necessarily the most liked users.
Top hashtags include common terms like #smile, #beach, and #party, indicating general content themes.
Social Network Dynamics
On average, each user has 76.23 followers, showing strong user interconnectivity.
The followers-to-following ratios reveal anomalies, likely caused by bot accounts, suggesting the importance of filtering quality vs quantity in user metrics.
Key Learnings
Schema design matters: Normalizing the schema upfront makes writing and scaling queries much easier later.
SQL alone can reveal marketing value: Even without dashboards or BI tools, well-crafted SQL provides deep product and user insights.
Raw averages can lie: When inactive users or outliers exist, average-based metrics (like posts or likes) need contextual interpretation.
Data integrity is essential: Enforcing foreign keys and using clear constraints prevents logical errors and supports accurate joins.
Following is the link attached to access the Github repo with a readme file to understand the repo.
SQL Database Analysis: Instagram Clone Project
May 20, 2025
Marketing Analytics
Project Description
This project simulates a simplified version of Instagram’s backend data system to analyze user behavior, content engagement, and platform activity using SQL. It includes a relational database schema, analytical queries, and marketing insights based on real-world patterns like followers, likes, hashtags, and post behavior.
It was built using MySQL, managed using MySQL Workbench, and version-controlled through GitHub Desktop. The project is designed as a portfolio case study to demonstrate real-world SQL querying, data structuring, and analytical thinking.
Introduction
The goal of this project was to mimic the structure of a social media platform like Instagram and to extract meaningful insights through raw SQL queries. The database includes core tables such as users, photo, comment, likes, follows, tags, and photo_tags, all normalized and enforced through primary and foreign key constraints.
Over a series of exploratory and analytical queries, the project surfaces patterns in user behavior, platform usage, content performance, and social interactions — enabling a mock marketing or product team to make informed decisions.
Key Stats and Insights
User Base Activity
The platform has 99 users.
26 users (about 26%) are inactive — they’ve never posted anything.
The average number of posts per user is 2.57, but is skewed by inactive users.
Registration Patterns
Registrations are fairly evenly distributed across the week, with Thursday being most popular and Saturday the least.
Top 5 oldest users registered over a span of 21 days, newer users within 16 days — an indication of slow but steady growth.
Engagement Highlights
The most liked photo (ID 145) belongs to Zack-Kemmer93 with 48 likes, nearly 50% of the entire user base.
The average likes per photo is a bit misleading — consistent posters aren’t necessarily the most liked users.
Top hashtags include common terms like #smile, #beach, and #party, indicating general content themes.
Social Network Dynamics
On average, each user has 76.23 followers, showing strong user interconnectivity.
The followers-to-following ratios reveal anomalies, likely caused by bot accounts, suggesting the importance of filtering quality vs quantity in user metrics.
Key Learnings
Schema design matters: Normalizing the schema upfront makes writing and scaling queries much easier later.
SQL alone can reveal marketing value: Even without dashboards or BI tools, well-crafted SQL provides deep product and user insights.
Raw averages can lie: When inactive users or outliers exist, average-based metrics (like posts or likes) need contextual interpretation.
Data integrity is essential: Enforcing foreign keys and using clear constraints prevents logical errors and supports accurate joins.
Following is the link attached to access the Github repo with a readme file to understand the repo.
SQL Database Analysis: Instagram Clone Project
May 20, 2025
Marketing Analytics
Project Description
This project simulates a simplified version of Instagram’s backend data system to analyze user behavior, content engagement, and platform activity using SQL. It includes a relational database schema, analytical queries, and marketing insights based on real-world patterns like followers, likes, hashtags, and post behavior.
It was built using MySQL, managed using MySQL Workbench, and version-controlled through GitHub Desktop. The project is designed as a portfolio case study to demonstrate real-world SQL querying, data structuring, and analytical thinking.
Introduction
The goal of this project was to mimic the structure of a social media platform like Instagram and to extract meaningful insights through raw SQL queries. The database includes core tables such as users, photo, comment, likes, follows, tags, and photo_tags, all normalized and enforced through primary and foreign key constraints.
Over a series of exploratory and analytical queries, the project surfaces patterns in user behavior, platform usage, content performance, and social interactions — enabling a mock marketing or product team to make informed decisions.
Key Stats and Insights
User Base Activity
The platform has 99 users.
26 users (about 26%) are inactive — they’ve never posted anything.
The average number of posts per user is 2.57, but is skewed by inactive users.
Registration Patterns
Registrations are fairly evenly distributed across the week, with Thursday being most popular and Saturday the least.
Top 5 oldest users registered over a span of 21 days, newer users within 16 days — an indication of slow but steady growth.
Engagement Highlights
The most liked photo (ID 145) belongs to Zack-Kemmer93 with 48 likes, nearly 50% of the entire user base.
The average likes per photo is a bit misleading — consistent posters aren’t necessarily the most liked users.
Top hashtags include common terms like #smile, #beach, and #party, indicating general content themes.
Social Network Dynamics
On average, each user has 76.23 followers, showing strong user interconnectivity.
The followers-to-following ratios reveal anomalies, likely caused by bot accounts, suggesting the importance of filtering quality vs quantity in user metrics.
Key Learnings
Schema design matters: Normalizing the schema upfront makes writing and scaling queries much easier later.
SQL alone can reveal marketing value: Even without dashboards or BI tools, well-crafted SQL provides deep product and user insights.
Raw averages can lie: When inactive users or outliers exist, average-based metrics (like posts or likes) need contextual interpretation.
Data integrity is essential: Enforcing foreign keys and using clear constraints prevents logical errors and supports accurate joins.
Following is the link attached to access the Github repo with a readme file to understand the repo.
Let’s work together, to bring the
light to your brand.
Let’s work together, to bring the
light to your brand.
Let’s work together, to bring the light to your brand