SQL-SnowFlake

Personal Project after learning SQL and SnowFlake

🏀 NBA Player Stats - Snowflake ETL & Analytics Project

This project demonstrates how to perform ETL (Extract, Transform, Load) and analytical queries using Snowflake with a dataset of the top 100 NBA player stats. It includes:

• Uploading a CSV dataset to Snowflake
• Creating and populating tables
• Running analytical SQL queries
• Exploring player and team performance

📂 Dataset

The dataset is based on the top 100 NBA players and includes statistics like:

• Player name
• Team
• Points per game
• Assists
• Rebounds
• Steals
• Blocks
• Turnovers
• Fouls
• Field Goal %, Three Point %, Effective Field Goal%, and Free Throw %

🛠️ Tech Stack

• Snowflake (Cloud Data Warehouse)
• SQL (Snowsight Web UI)
• CSV File (Uploaded manually)

🧱 Database Schema

Table: player_stats

Column	Data Type	Description
Player	TEXT	Player’s name
Age	INT	Player's Age
Team	TEXT	NBA team abbreviation
Position	TEXT	Player's Position
PTS	FLOAT	Points per game
AST	FLOAT	Assists per game
TRB	FLOAT	Total rebounds per game
Steals	FLOAT	Steals per game
Blocks	FLOAT	Blocks per game
Turnovers	FLOAT	Turnovers per game
Fould	FLOAT	Fouls per game
FG%	FLOAT	Field goal percentage
3P%	FLOAT	Three-point percentage
FT%	FLOAT	Free throw percentage
EFG%	FLOAT	Effective FG percentage

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🚀 Steps

1. Upload the CSV

Upload the top100_player_stats.csv file into Snowflake using the Snowsight UI

2. Create the Table

CREATE OR REPLACE TABLE player_stats (
    PLAYER	TEXT
    AGE INT
    TEAM TEXT
    POS	TEXT
    FG	FLOAT
    THREEPOINT	FLOAT
    EFFECTIVEFG	FLOAT
    FREETHROW	FLOAT
    REBOUNDS	FLOAT
    ASSISTS	FLOAT
    STEALS	FLOAT
    BLOCKS	FLOAT
    TURNOVERS	FLOAT
    FOULS	FLOAT
    POINTS	FLOAT
);

3. Load the Data

Used the load data wizard in snowflake to update the data to the table, PLAYER_STAT, in the NBA_STATS_DB database

4. Create and Run Analytical Queries

Examples shown in the .sql files with the results in the .csv files