What Is a Data Pipeline & ETL? Data Engineering Guide

Need to combine data from different sources? Reporting system too slow? Data pipelines automatically collect, transform, and prepare data for analysis.

What Is ETL?

ETL (Extract, Transform, Load) is the process of extracting data from sources, transforming it, and loading it into a target system.

ETL vs ELT

| Approach | Transform | When | |----------|-----------|------| | ETL | Before loading | Legacy systems | | ELT | After loading | Modern data warehouses |

Batch vs Streaming

| Feature | Batch | Streaming | |---------|-------|-----------| | Latency | Minutes-hours | Milliseconds | | Complexity | Low | High | | Use case | Reporting, ML | Real-time dashboards |

Simple ETL (Python)

import pandas as pd

# Extract
orders = pd.read_sql("SELECT * FROM orders WHERE date = CURRENT_DATE", db)

# Transform
orders['revenue'] = orders['quantity'] * orders['price']
orders = orders.dropna(subset=['customer_id'])

# Load
orders.to_sql('daily_revenue', warehouse, if_exists='append')

Airflow Orchestration

extract >> transform >> load  # DAG dependency chain

Tools

| Tool | Type | Highlights | |------|------|-----------| | Airflow | Orchestration | DAG-based, Python | | Kafka | Streaming | High throughput | | Spark | Processing | Batch + streaming | | dbt | Transform | SQL-based, modern |

Best Practices

1. Idempotent pipelines — Re-running produces the same result
2. Incremental loads — Only load changed data
3. Data quality checks — Null, type, range, uniqueness
4. Error handling — Retry, dead letter queues
5. Data lineage — Track data origins
6. Monitoring — Pipeline duration, errors, volume

Conclusion

Data pipelines are the infrastructure for data-driven decisions. Use batch for daily reports, streaming for real-time analytics. Build reliable, scalable data flows with the right tools.

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