Belajar Quality Engineer - Backend & Data Quality

#Pendahuluan

Setelah di episode 6 kita mempelajari frontend quality engineering, pada episode ini kita mempelajari backend & data quality — bagaimana menguji backend logic, database, dan data quality. Backend adalah fondasi aplikasi: jika backend bermasalah, frontend tidak akan berfungsi dengan benar.

Mengapa backend & data quality penting? Karena data yang corrupt atau backend yang bermasalah bisa menyebabkan data loss, security breaches, dan application failures. QE harus memastikan backend robust dan data berkualitas.

#Database Testing

#Database Test Types

text

Database Testing:
├── Schema Testing:
│   ├── Table structure
│   ├── Column types
│   ├── Constraints
│   └── Indexes
├── Data Integrity Testing:
│   ├── Referential integrity
│   ├── Unique constraints
│   ├── Not null constraints
│   └── Check constraints
├── Query Testing:
│   ├── Correctness
│   ├── Performance
│   └── Edge cases
└── Migration Testing:
    ├── Schema changes
    ├── Data migration
    └── Rollback capability

#Contoh Database Test

typescript

// database.test.ts
import { PrismaClient } from '@prisma/client';
 
const prisma = new PrismaClient();
 
describe('User Database', () => {
  afterAll(async () => {
    await prisma.$disconnect();
  });
 
  it('should enforce unique email', async () => {
    await prisma.user.create({
      data: { email: 'test@test.com', name: 'Test' },
    });
 
    await expect(
      prisma.user.create({
        data: { email: 'test@test.com', name: 'Test 2' },
      })
    ).rejects.toThrow();
  });
 
  it('should cascade delete orders', async () => {
    const user = await prisma.user.create({
      data: { email: 'cascade@test.com', name: 'Cascade' },
    });
 
    await prisma.order.create({
      data: { userId: user.id, total: 100 },
    });
 
    await prisma.user.delete({ where: { id: user.id } });
 
    const orders = await prisma.order.findMany({
      where: { userId: user.id },
    });
 
    expect(orders).toHaveLength(0);
  });
});

#Data Quality

#Data Quality Dimensions

text

Data Quality Dimensions:
├── Accuracy: Data benar dan valid
├── Completeness: Tidak ada data kosong
├── Consistency: Konsisten di semua system
├── Timeliness: Data up-to-date
├── Validity: Sesuai format & rules
└── Uniqueness: Tidak ada duplicate

#Data Quality Checks

sql

-- Accuracy check
SELECT * FROM users WHERE email NOT LIKE '%@%';
 
-- Completeness check
SELECT COUNT(*) FROM users WHERE name IS NULL OR name = '';
 
-- Consistency check
SELECT user_id, COUNT(DISTINCT email) 
FROM user_accounts 
GROUP BY user_id 
HAVING COUNT(DISTINCT email) > 1;
 
-- Uniqueness check
SELECT email, COUNT(*) 
FROM users 
GROUP BY email 
HAVING COUNT(*) > 1;

Note

Data quality bukan hanya testing tapi juga monitoring. Implement data quality checks di pipeline dan monitor secara持续. Data quality issues yang tidak terdeteksi bisa menyebabkan masalah besar di production.

#Integration Testing

#Backend Integration

text

Backend Integration Testing:
├── API Integration:
│   ├── Request/Response validation
│   ├── Status codes
│   └── Error handling
├── Database Integration:
│   ├── CRUD operations
│   ├── Transactions
│   └── Concurrency
├── Message Queue Integration:
│   ├── Message publishing
│   ├── Message consumption
│   └── Error handling
└── External Service Integration:
    ├── Third-party APIs
    ├── Payment gateways
    └── Email services

#Practical: Backend Quality Checklist

yaml

backend_quality:
  database:
    - schema_testing
    - data_integrity
    - query_performance
    - migration_testing
  data_quality:
    - accuracy_checks
    - completeness_checks
    - consistency_checks
    - uniqueness_checks
  integration:
    - api_testing
    - database_testing
    - message_queue
    - external_services

Tip

Gunakan test database yang terisolasi untuk testing. Jangan test langsung di production database. Gunakan Docker untuk spinning up test database yang reproducible.

#Penutup

Pada episode 7 ini, kalian telah mempelajari backend & data quality.

Inti yang harus dibawa pulang:

Database testing: schema, data integrity, query, migration.
Data quality: accuracy, completeness, consistency, timeliness, validity, uniqueness.
Integration testing: API, database, message queue, external services.
Monitoring data quality secara持续.

Di episode 8 selanjutnya, kita akan membahas shift-left testing — bagaimana mengimplementasikan quality di requirement & design phase. Sampai jumpa di episode 8!