Database Manager

Database Manager

Universal database management tool supporting multiple database types with migration, backup, and data operations.

What this Skill does

• Execute SQL queries across multiple database types
• Create and manage database migrations
• Automated backup and restore operations
• Import/export data from CSV, JSON, SQL files
• Database health monitoring and diagnostics
• Query optimization suggestions
• Generate database documentation
• Transaction management with rollback support

Supported Databases

• PostgreSQL
• MySQL / MariaDB
• SQLite
• MongoDB (NoSQL)

Requirements

Install required packages:

# For SQL databases
pip install sqlalchemy psycopg2-binary mysql-connector-python

# For MongoDB
pip install pymongo

# For migrations
pip install alembic

Set environment variables:

export DATABASE_URL="postgresql://user:pass@localhost/dbname"
export DB_BACKUP_DIR="/path/to/backups"

How to use

Execute SQL query

import os
from sqlalchemy import create_engine, text

engine = create_engine(os.getenv('DATABASE_URL'))

with engine.connect() as conn:
    result = conn.execute(text("SELECT * FROM users WHERE active = true"))
    for row in result:
        print(row)

Create migration

# Initialize migration environment
alembic init migrations

# Create new migration
alembic revision --autogenerate -m "Add user table"

# Apply migrations
alembic upgrade head

Backup database

# PostgreSQL
pg_dump $DATABASE_URL > backup_$(date +%Y%m%d).sql

# MySQL
mysqldump -u $DB_USER -p$DB_PASS $DB_NAME > backup_$(date +%Y%m%d).sql

# SQLite
sqlite3 database.db ".backup backup_$(date +%Y%m%d).db"

Import CSV data

import pandas as pd
from sqlalchemy import create_engine

df = pd.read_csv('data.csv')
engine = create_engine(os.getenv('DATABASE_URL'))
df.to_sql('table_name', engine, if_exists='append', index=False)

MongoDB operations

from pymongo import MongoClient

client = MongoClient(os.getenv('MONGODB_URL', 'mongodb://localhost:27017'))
db = client['mydb']
collection = db['documents']

# Insert document
result = collection.insert_one({'name': 'John', 'age': 30})

# Query documents
documents = collection.find({'age': {'$gt': 25}})

Examples

When to use this Skill:

• "Create a migration to add a new table"
• "Backup the production database"
• "Import CSV data into a table"
• "Execute a complex SQL query"
• "Monitor database performance"
• "Restore data from a backup file"

Tips

• Always backup before major operations
• Use transactions for multi-step operations
• Index frequently queried columns
• Monitor query performance
• Use connection pooling for production