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Data Retention & Archiving Planner

data-retention-archiving-planner

Plans and implements data retention policies with archival strategies, compliance requirements, automated cleanup jobs, and cold storage migration. Use for "data retention", "data archival", "GDPR compliance", or "storage optimization".

SKILL.md

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Data Retention & Archiving Planner

Manage data lifecycle with automated retention and archiving.

Retention Policy Document

# Data Retention Policy

## Retention Periods

| Data Type             | Hot Storage | Cold Storage | Total Retention | Reason            |
| --------------------- | ----------- | ------------ | --------------- | ----------------- |
| User accounts         | Active      | N/​A          | Indefinite      | Business need     |
| Order history         | 2 years     | 5 years      | 7 years         | Tax compliance    |
| Logs                  | 30 days     | 90 days      | 120 days        | Operational       |
| Analytics events      | 90 days     | 1 year       | 15 months       | Business insights |
| Audit trails          | 1 year      | 6 years      | 7 years         | Legal compliance  |
| User sessions         | 30 days     | None         | 30 days         | Security          |
| Failed login attempts | 90 days     | None         | 90 days         | Security          |

## Compliance Requirements

### GDPR (EU)

- Right to erasure (right to be forgotten)
- Data minimization
- Storage limitation

### HIPAA (Healthcare)

- Minimum 6 years retention
- Secure archival required

### SOX (Financial)

- 7 years retention for financial records
- Immutable audit trails

### PCI DSS (Payments)

- 1 year minimum for audit logs
- 3 months minimum for transaction logs

Archive Schema Design

-- Hot database: Current active data
CREATE TABLE orders (
  id BIGSERIAL PRIMARY KEY,
  user_id BIGINT NOT NULL,
  total DECIMAL(10,2) NOT NULL,
  status TEXT NOT NULL,
  created_at TIMESTAMP NOT NULL DEFAULT NOW(),
  updated_at TIMESTAMP NOT NULL DEFAULT NOW()
);

-- Cold database: Archived historical data
CREATE TABLE orders_archive (
  id BIGINT PRIMARY KEY,
  user_id BIGINT NOT NULL,
  total DECIMAL(10,2) NOT NULL,
  status TEXT NOT NULL,
  created_at TIMESTAMP NOT NULL,
  updated_at TIMESTAMP NOT NULL,
  archived_at TIMESTAMP NOT NULL DEFAULT NOW()
);

-- Create partition for time-based archival
CREATE TABLE orders_2024_q1 PARTITION OF orders
  FOR VALUES FROM ('2024-01-01') TO ('2024-04-01');

CREATE TABLE orders_2024_q2 PARTITION OF orders
  FOR VALUES FROM ('2024-04-01') TO ('2024-07-01');

Archival Job Implementation

// jobs/​archive-orders.ts
import { PrismaClient } from "@prisma/​client";

const prisma = new PrismaClient();
const archivePrisma = new PrismaClient({
  datasources: {
    db: {
      url: process.env.ARCHIVE_DATABASE_URL,
    },
  },
});

interface ArchivalJob {
  table: string;
  retentionDays: number;
  batchSize: number;
}

async function archiveOrders() {
  const cutoffDate = new Date();
  cutoffDate.setDate(cutoffDate.getDate() - 730); // 2 years

  console.log(`📦 Archiving orders older than ${cutoffDate.toISOString()}`);

  let archived = 0;
  let hasMore = true;

  while (hasMore) {
    await prisma.$transaction(async (tx) => {
      // Find orders to archive
      const ordersToArchive = await tx.order.findMany({
        where: {
          created_at: { lt: cutoffDate },
          status: { in: ["delivered", "cancelled"] },
        },
        take: 1000,
      });

      if (ordersToArchive.length === 0) {
        hasMore = false;
        return;
      }

      // Copy to archive database
      await archivePrisma.order.createMany({
        data: ordersToArchive.map((order) => ({
          ...order,
          archived_at: new Date(),
        })),
        skipDuplicates: true,
      });

      // Delete from hot database
      await tx.order.deleteMany({
        where: {
          id: { in: ordersToArchive.map((o) => o.id) },
        },
      });

      archived += ordersToArchive.length;
      console.log(`  Archived ${archived} orders...`);
    });

    // Rate limiting
    await new Promise((resolve) => setTimeout(resolve, 100));
  }

  console.log(`✅ Archived ${archived} orders total`);
}

// Schedule: Run nightly
archiveOrders();

Automated Cleanup Jobs

// jobs/​cleanup-old-data.ts
interface CleanupJob {
  table: string;
  column: string;
  retentionDays: number;
}

const CLEANUP_JOBS: CleanupJob[] = [
  {
    table: "sessions",
    column: "created_at",
    retentionDays: 30,
  },
  {
    table: "password_reset_tokens",
    column: "created_at",
    retentionDays: 1,
  },
  {
    table: "failed_login_attempts",
    column: "attempted_at",
    retentionDays: 90,
  },
  {
    table: "analytics_events",
    column: "created_at",
    retentionDays: 90,
  },
];

async function runCleanupJobs() {
  console.log("🗑️  Running cleanup jobs...\n");

  for (const job of CLEANUP_JOBS) {
    const cutoffDate = new Date();
    cutoffDate.setDate(cutoffDate.getDate() - job.retentionDays);

    const result = await prisma.$executeRawUnsafe(
      `
      DELETE FROM "${job.table}"
      WHERE "${job.column}" < $1
    `,
      cutoffDate
    );

    console.log(
      `✅ ${job.table}: Deleted ${result} rows older than ${job.retentionDays} days`
    );
  }

  console.log("\n✅ Cleanup complete!");
}

Soft Delete Pattern

// Soft delete for GDPR compliance
model User {
  id        Int       @id @default(autoincrement())
  email     String    @unique
  name      String
  deletedAt DateTime? // NULL = active, NOT NULL = deleted
  createdAt DateTime  @default(now())
  updatedAt DateTime  @updatedAt

  @@index([deletedAt])
}

// Middleware to filter soft-deleted records
prisma.$use(async (params, next) => {
  if (params.action === 'findMany' || params.action === 'findFirst') {
    params.args.where = {
      ...params.args.where,
      deletedAt: null, // Only show non-deleted
    };
  }
  return next(params);
});

// Hard delete after retention period
async function purgeDeletedUsers() {
  const cutoffDate = new Date();
  cutoffDate.setDate(cutoffDate.getDate() - 90); // 90 days retention

  const result = await prisma.user.deleteMany({
    where: {
      deletedAt: { lt: cutoffDate },
    },
  });

  console.log(`🗑️  Purged ${result.count} deleted users`);
}

Cold Storage Migration

#!/​bin/​bash
# scripts/​migrate-to-s3.sh

# Dump old orders to S3 for cold storage
CUTOFF_DATE="2022-01-01"

echo "📦 Migrating orders to S3..."

# 1. Export to CSV
psql $DATABASE_URL -c "\COPY (
  SELECT * FROM orders WHERE created_at < '$CUTOFF_DATE'
) TO STDOUT WITH CSV HEADER" | gzip > orders_archive.csv.gz

# 2. Upload to S3
aws s3 cp orders_archive.csv.gz s3://my-cold-storage/​orders/

# 3. Verify upload
if aws s3 ls s3://my-cold-storage/​orders/​orders_archive.csv.gz; then
  echo "✅ Uploaded to S3"

  # 4. Delete from database
  psql $DATABASE_URL -c "DELETE FROM orders WHERE created_at < '$CUTOFF_DATE'"

  echo "✅ Deleted from database"
else
  echo "❌ S3 upload failed, skipping deletion"
  exit 1
fi

Compliance Automation

// Right to be forgotten (GDPR)
async function deleteUserData(userId: number) {
  console.log(`🗑️  Deleting user data for user ${userId}...`);

  await prisma.$transaction(async (tx) => {
    // 1. Anonymize orders (keep for business records)
    await tx.order.updateMany({
      where: { userId },
      data: {
        userId: null,
        shippingAddress: "[DELETED]",
        billingAddress: "[DELETED]",
      },
    });

    // 2. Delete personal data
    await tx.userProfile.delete({ where: { userId } });
    await tx.paymentMethod.deleteMany({ where: { userId } });
    await tx.address.deleteMany({ where: { userId } });

    // 3. Soft delete user account
    await tx.user.update({
      where: { id: userId },
      data: {
        email: `deleted-${userId}@example.com`,
        name: "[DELETED]",
        deletedAt: new Date(),
      },
    });
  });

  console.log(`✅ User data deleted`);
}

Monitoring & Alerting

// Monitor archive job health
async function checkArchivalHealth() {
  // Check oldest active order
  const oldestOrder = await prisma.order.findFirst({
    orderBy: { created_at: "asc" },
  });

  const age = Date.now() - oldestOrder.created_at.getTime();
  const ageDays = age / (1000 * 60 * 60 * 24);

  if (ageDays > 750) {
    // > 2 years + buffer
    console.error("⚠️  Orders older than retention period found!");
    await sendAlert({
      title: "Archive job failing",
      message: `Oldest order is ${ageDays.toFixed(0)} days old`,
    });
  }

  // Check archive database size
  const archiveCount = await archivePrisma.order.count();
  console.log(`📊 Archive database: ${archiveCount} orders`);

  // Check hot database size
  const hotCount = await prisma.order.count();
  console.log(`📊 Hot database: ${hotCount} orders`);
}

Restore from Archive

// Restore archived order (e.g., for audit)
async function restoreArchivedOrder(orderId: number) {
  // Find in archive
  const archivedOrder = await archivePrisma.order.findUnique({
    where: { id: orderId },
  });

  if (!archivedOrder) {
    throw new Error("Order not found in archive");
  }

  // Copy to hot database
  await prisma.order.create({
    data: {
      ...archivedOrder,
      archived_at: undefined,
    },
  });

  console.log(`✅ Restored order ${orderId} from archive`);
}

Schedule Configuration

# cron schedule for archival jobs
jobs:
  archive-orders:
    schedule: "0 2 * * *" # 2 AM daily
    command: "npm run job:archive-orders"

  cleanup-sessions:
    schedule: "0 3 * * *" # 3 AM daily
    command: "npm run job:cleanup-sessions"

  purge-deleted-users:
    schedule: "0 4 * * 0" # 4 AM Sunday
    command: "npm run job:purge-deleted"

  health-check:
    schedule: "0 */​6 * * *" # Every 6 hours
    command: "npm run job:check-archival-health"

Best Practices

  1. Define clear policies: Document retention periods
  2. Automate everything: Manual cleanup is unreliable
  3. Test restore: Regularly test archive restoration
  4. Monitor job health: Alert on failures
  5. Compliance first: Meet legal requirements
  6. Soft delete: Before hard delete
  7. Batch operations: Avoid locking tables

Output Checklist

  • Retention policy documented
  • Archive schema designed
  • Archival jobs implemented
  • Cleanup jobs automated
  • Soft delete pattern (if applicable)
  • Cold storage migration
  • GDPR compliance (right to be forgotten)
  • Job scheduling configured
  • Monitoring and alerting
  • Restore procedure tested

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