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Data Management

data-management

Design and manage data storage effectively. Use when working with databases, schemas, or data migrations. Covers schema design, migrations, and data integrity.

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SKILL.md

Full skill instructions

Data Management

Workflows

  • Schema Design: Define tables, relationships, constraints
  • Migrations: Version control schema changes
  • Indexing: Add indexes for query performance
  • Backup: Ensure data recovery capability

Schema Design Principles

Normalization

  • 1NF: Atomic values, no repeating groups
  • 2NF: No partial dependencies
  • 3NF: No transitive dependencies

When to Denormalize

  • Read-heavy workloads
  • Reporting/​analytics
  • Caching layers

Migration Best Practices

Forward-Only Migrations

Each migration should be a single forward step.

-- migrations/​001_create_users.sql
CREATE TABLE users (
  id UUID PRIMARY KEY DEFAULT gen_random_uuid(),
  email VARCHAR(255) NOT NULL UNIQUE,
  created_at TIMESTAMPTZ NOT NULL DEFAULT NOW()
);

CREATE INDEX idx_users_email ON users(email);

Safe Migrations

  • Add columns as nullable first
  • Create indexes concurrently
  • Never drop columns in the same deploy

Indexing Strategy

-- B-tree (default): Equality and range queries
CREATE INDEX idx_users_email ON users(email);

-- Partial index: When you query a subset
CREATE INDEX idx_active_users ON users(id) WHERE active = true;

-- Composite index: Multiple columns
CREATE INDEX idx_orders_user_date ON orders(user_id, created_at DESC);

Connection Management

// Use connection pooling
const pool = new Pool({
  max: 20,                    // Max connections
  idleTimeoutMillis: 30000,   // Close idle connections
  connectionTimeoutMillis: 2000
});

Data Integrity

  • Use foreign key constraints
  • Add NOT NULL where appropriate
  • Use CHECK constraints for validation
  • Consider using ENUM types for fixed values

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