database

# Database Workflow Bundle

## Overview

Comprehensive database workflow for database design, development, optimization, migrations, and data engineering. Covers SQL, NoSQL, and modern data platforms.

## When to Use This Workflow

Use this workflow when:
- Designing database schemas
- Implementing database migrations
- Optimizing query performance
- Setting up data pipelines
- Managing database operations
- Implementing data quality

## Workflow Phases

### Phase 1: Database Design

#### Skills to Invoke
- `database-architect` - Database architecture
- `database-design` - Schema design
- `postgresql` - PostgreSQL design
- `nosql-expert` - NoSQL design

#### Actions
1. Gather requirements
2. Design schema
3. Define relationships
4. Plan indexing strategy
5. Design for scalability

#### Copy-Paste Prompts
```
Use @database-architect to design database schema
```

```
Use @postgresql to design PostgreSQL schema
```

### Phase 2: Database Implementation

#### Skills to Invoke
- `prisma-expert` - Prisma ORM
- `database-migrations-sql-migrations` - SQL migrations
- `neon-postgres` - Serverless Postgres

#### Actions
1. Set up database connection
2. Configure ORM
3. Create migrations
4. Implement models
5. Set up seed data

#### Copy-Paste Prompts
```
Use @prisma-expert to set up Prisma ORM
```

```
Use @database-migrations-sql-migrations to create migrations
```

### Phase 3: Query Optimization

#### Skills to Invoke
- `database-optimizer` - Database optimization
- `sql-optimization-patterns` - SQL optimization
- `postgres-best-practices` - PostgreSQL optimization

#### Actions
1. Analyze slow queries
2. Review execution plans
3. Optimize indexes
4. Refactor queries
5. Implement caching

#### Copy-Paste Prompts
```
Use @database-optimizer to optimize database performance
```

```
Use @sql-optimization-patterns to optimize SQL queries
```

### Phase 4: Data Migration

#### Skills to Invoke
- `database-migration` - Database migration
- `framework-migration-code-migrate` - Code migration

#### Actions
1. Plan migration strategy
2. Create migration scripts
3. Test migration
4. Execute migration
5. Verify data integrity

#### Copy-Paste Prompts
```
Use @database-migration to plan database migration
```

### Phase 5: Data Pipeline Development

#### Skills to Invoke
- `data-engineer` - Data engineering
- `data-engineering-data-pipeline` - Data pipelines
- `airflow-dag-patterns` - Airflow workflows
- `dbt-transformation-patterns` - dbt transformations

#### Actions
1. Design data pipeline
2. Set up data ingestion
3. Implement transformations
4. Configure scheduling
5. Set up monitoring

#### Copy-Paste Prompts
```
Use @data-engineer to design data pipeline
```

```
Use @airflow-dag-patterns to create Airflow DAGs
```

### Phase 6: Data Quality

#### Skills to Invoke
- `data-quality-frameworks` - Data quality
- `data-engineering-data-driven-feature` - Data-driven features

#### Actions
1. Define quality metrics
2. Implement validation
3. Set up monitoring
4. Create alerts
5. Document standards

#### Copy-Paste Prompts
```
Use @data-quality-frameworks to implement data quality checks
```

### Phase 7: Database Operations

#### Skills to Invoke
- `database-admin` - Database administration
- `backup-automation` - Backup automation

#### Actions
1. Set up backups
2. Configure replication
3. Monitor performance
4. Plan capacity
5. Implement security

#### Copy-Paste Prompts
```
Use @database-admin to manage database operations
```

## Database Technology Workflows

### PostgreSQL
```
Skills: postgresql, postgres-best-practices, neon-postgres, prisma-expert
```

### MongoDB
```
Skills: nosql-expert, azure-cosmos-db-py
```

### Redis
```
Skills: bullmq-specialist, upstash-qstash
```

### Data Warehousing
```
Skills: clickhouse-io, dbt-transformation-patterns
```

## Quality Gates

- [ ] Schema designed and reviewed
- [ ] Migrations tested
- [ ] Performance benchmarks met
- [ ] Backups configured
- [ ] Monitoring in place
- [ ] Documentation complete

## Related Workflow Bundles

- `development` - Application development
- `cloud-devops` - Infrastructure
- `ai-ml` - AI/ML data pipelines
- `testing-qa` - Data testing