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Data Engineering

Giriş

Data Engineering, modern software systems'de data pipeline design, data warehousing ve data processing için kritik öneme sahiptir. Senior-level developers için data engineering konularını anlamak, data pipelines tasarlamak, data warehouses kurmak ve real-time processing implement etmek için gereklidir. Bu bölüm, data pipeline design, data warehousing, real-time processing, data governance ve data quality konularını kapsar.

Kapsanan Konular

1. Data Pipeline Design

ETL/ELT processes, data flow design, ve pipeline orchestration.

Öğrenilecekler: - ETL/ELT processes - Data flow design - Pipeline orchestration - Data transformation - Pipeline monitoring

2. Data Warehousing

Data warehouse design, data modeling, ve data storage strategies.

Öğrenilecekler: - Data warehouse design - Data modeling - Data storage strategies - Data partitioning - Data indexing

3. Real-Time Processing

Stream processing, real-time analytics, ve event processing.

Öğrenilecekler: - Stream processing - Real-time analytics - Event processing - Real-time pipelines - Performance optimization

4. Data Governance

Data quality, data lineage, ve data security.

Öğrenilecekler: - Data quality - Data lineage - Data security - Data catalog - Data policies

5. Data Quality

Data validation, data cleansing, ve data monitoring.

Öğrenilecekler: - Data validation - Data cleansing - Data monitoring - Quality metrics - Quality improvement

Neden Önemli?

1. Business Intelligence

  • Data-driven decisions
  • Business insights
  • Performance analytics
  • Trend analysis
  • Competitive advantage

2. System Integration

  • Data consistency
  • System interoperability
  • Data synchronization
  • Real-time updates
  • Operational efficiency

3. Compliance & Security

  • Data governance
  • Regulatory compliance
  • Data security
  • Privacy protection
  • Risk management

4. Technical Excellence

  • Scalable architecture
  • Performance optimization
  • Data reliability
  • System maintainability
  • Continuous improvement

Mülakat Soruları

Temel Sorular

  1. Data engineering nedir?

  2. Cevap: Data pipeline design, data processing, data warehousing, data governance.

  3. ETL nedir?

  4. Cevap: Extract, Transform, Load - data integration process.

  5. Data warehouse nedir?

  6. Cevap: Centralized data storage, optimized for analytics and reporting.

  7. Real-time processing nedir?

  8. Cevap: Stream processing, real-time analytics, immediate data processing.

  9. Data governance nedir?

  10. Cevap: Data quality, data lineage, data security, data policies.

Teknik Sorular

  1. Data pipeline nasıl tasarlanır?

  2. Cevap: Source identification, transformation logic, target design, monitoring setup.

  3. Data warehouse nasıl optimize edilir?

  4. Cevap: Data modeling, partitioning, indexing, query optimization.

  5. Real-time processing nasıl implement edilir?

  6. Cevap: Stream processing framework, event handling, performance optimization.

  7. Data quality nasıl sağlanır?

  8. Cevap: Validation rules, cleansing processes, monitoring, quality metrics.

  9. Data governance nasıl kurulur?

  10. Cevap: Policies definition, data catalog, lineage tracking, security measures.

Best Practices

1. Data Pipeline Design

  • Design for scalability
  • Implement error handling
  • Monitor pipeline health
  • Plan for failure recovery
  • Document data flow

2. Data Warehousing

  • Use proper data modeling
  • Implement partitioning
  • Optimize queries
  • Monitor performance
  • Plan for growth

3. Real-Time Processing

  • Design for performance
  • Handle backpressure
  • Implement error handling
  • Monitor latency
  • Plan for scaling

4. Data Governance

  • Define clear policies
  • Implement data catalog
  • Track data lineage
  • Ensure security
  • Monitor compliance

5. Data Quality

  • Define quality metrics
  • Implement validation
  • Monitor quality
  • Clean data regularly
  • Plan for improvement

Kaynaklar