Data Engineering Guide

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Data Pipelines 🔄 (From Raw Data to Insights) ​

A data pipeline is a system that moves and transforms data from source systems to target systems in a reliable, automated way.

🧠 If processing is the engine, pipelines are the assembly line of data engineering.

🎯 Why Data Pipelines Matter ​

In real companies, data does not sit still.

It continuously flows through systems:

  • User actions
  • Transactions
  • Logs
  • Events
  • External APIs

Pipelines ensure this data becomes usable.

🧭 What is a Data Pipeline? ​

A data pipeline is a sequence of steps:

  1. Extract data from source
  2. Transform data
  3. Load into destination

This is called ETL (Extract → Transform → Load)

⚙️ Data Pipeline Architecture ​

Sources → Ingestion → Processing → Storage → Consumption

Breakdown: ​

  • Sources → Apps, DBs, APIs
  • Ingestion → Kafka, CDC, Batch pulls
  • Processing → Spark, Flink, SQL
  • Storage → Data Lake / Warehouse
  • Consumption → BI dashboards, ML models

🔄 Types of Data Pipelines ​

1. Batch Pipelines ​

Data is processed in chunks at intervals.

Example: ​

  • Daily sales report
  • Hourly log processing

✔ Simple
✔ Scalable
❌ Not real-time

2. Streaming Pipelines ​

Data is processed continuously.

Example: ​

  • Fraud detection
  • Real-time dashboards

✔ Low latency
✔ Real-time insights
❌ Complex

⚙️ ETL vs ELT ​

ETL (Extract → Transform → Load) ​

  • Data is transformed before storing
  • Used in traditional systems

✔ Clean data at destination
❌ Less flexible

ELT (Extract → Load → Transform) ​

  • Data is loaded first, transformed later
  • Used in modern cloud systems

✔ Flexible
✔ Scalable
❌ Requires strong storage layer

🧠 Key Pipeline Concepts ​

1. Orchestration ​

Pipelines are managed using tools like:

  • Airflow
  • Dagster
  • Prefect

They handle:

  • Scheduling
  • Dependencies
  • Retry logic
  • Monitoring

2. Idempotency ​

A pipeline should be safe to re-run:

Same input → same output (no duplicates)

3. Data Quality ​

Important checks:

  • Schema validation
  • Null checks
  • Deduplication
  • Freshness checks

4. Late Arriving Data ​

Data may arrive late due to:

  • Network delays
  • System failures
  • Event backlogs

Pipelines must handle this gracefully.

5. Backfilling ​

Reprocessing historical data when:

  • Bugs are fixed
  • Logic changes
  • Data gaps are found

🚨 Common Pipeline Failures ​

  • Job failures
  • Partial writes
  • Duplicate ingestion
  • Schema drift
  • Delayed upstream data

🔗 How This Connects ​

  • Data Modeling → defines structure
  • Storage → holds data
  • Processing → transforms data
  • Pipelines → move + orchestrate data
  • System Design → builds full architecture

🎯 Goal of Data Pipelines ​

You should be able to:

  • Design ETL/ELT workflows
  • Handle batch + streaming systems
  • Explain orchestration tools
  • Solve real production issues
  • Build scalable data workflows

“A pipeline is not just data movement — it is the reliability layer of modern data systems.”