Data Engineering Fundamentals

What You Need to Learn

Data Engineering is the foundation of modern data-driven organizations. Here's what you need to master:

1. Data Pipeline Concepts: ETL (Extract, Transform, Load) vs ELT (Extract, Load, Transform)
2. Distributed Computing: Apache Spark, Hadoop ecosystem
3. Data Storage: Data Lakes vs Data Warehouses
4. Orchestration: Airflow, Prefect for workflow management
5. Data Quality: Testing and validation frameworks

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ELI5: What is Data Engineering?

Imagine you're running a massive library:

• Raw Data = Books arriving in different languages, formats, and conditions
• Data Engineer = The librarian who organizes everything
• ETL Pipeline = The process of:
  • Extract: Collecting books from different sources
  • Transform: Translating, cataloging, and organizing them
  • Load: Placing them on shelves where people can find them

Why it matters: Without organization, you have a pile of books. With data engineering, you have a searchable, useful library!

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System Design: Modern Data Pipeline Architecture

┌─────────────────┐
│  Data Sources   │
│  (APIs, DBs,    │
│   Streaming)    │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   Ingestion     │
│  (Kafka, Firehose)│
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│   Raw Layer     │
│  (S3, ADLS)     │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Processing     │
│  (Spark, DBT)   │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│ Curated Layer   │
│ (Delta Lake)    │
└────────┬────────┘
         │
         ▼
┌─────────────────┐
│  Consumption    │
│ (BI Tools, ML)  │
└─────────────────┘

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Medallion Architecture (Bronze, Silver, Gold)

Bronze Layer (Raw)

• Store data exactly as received
• No transformations
• Full history preserved

Silver Layer (Cleaned)

• Data quality checks applied
• Deduplicated and normalized
• Business logic applied

Gold Layer (Business-Ready)

• Aggregated for specific use cases
• Optimized for query performance
• Ready for analytics and ML

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Best Practices

1. Idempotency: Pipelines should produce same result when run multiple times
2. Incremental Processing: Process only new/changed data
3. Data Quality Checks: Validate at every stage
4. Monitoring: Track pipeline health and data freshness
5. Documentation: Maintain data lineage and catalog

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Real-World Example

At Nike, I built pipelines processing 40+ data sources:

• Kafka microservices for real-time ingestion
• Delta Lake for storage with time travel
• Z-ordering and liquid clustering for query optimization
• Great Expectations for automated data quality

Result: Near real-time sustainability analytics powering carbon reduction strategies.