Example:

Source Database → CDC → Only Changed Records → Data Platform

CDC is especially useful for keeping analytical systems synchronized with operational databases.

⚠️ 9. Challenges in Data Ingestion

A production ingestion pipeline must handle:

Duplicate Data, Missing Data, Schema Changes, Late Data, Network Failures, High Volume

🛡️ 10. Important Data Ingestion Best Practices

A reliable ingestion pipeline should include:

✅ Incremental processing

✅ Retry mechanisms

✅ Error handling

✅ Data validation

✅ Monitoring and alerting

✅ Idempotent processing

✅ Schema validation

✅ Checkpointing for streaming systems

🌍 Real-World Example

Website → Orders Database → CDC → Kafka → Spark → Data Lake → Data Warehouse → Power BI

When a customer places an order, the event can be captured, processed, stored, and eventually used by analysts for reporting.

🎯 Interview Question

❓ What is the difference between data ingestion and data transformation?

Data ingestion focuses on moving data from a source to a destination.

Data transformation focuses on changing, cleaning, enriching, or restructuring that data.

Example:

Database → Ingestion → Move the data → Transformation → Clean & modify the data → Warehouse

💡 Key Takeaway

Remember:

📥 Data Ingestion = Get the data into the platform

📦 Batch = Process periodically

⚡ Streaming = Process continuously

🔄 Incremental = Process only new/changed data

🔍 CDC = Capture source changes

A strong understanding of ingestion is essential before moving into advanced topics like Kafka, Spark, Airflow, and cloud data pipelines.

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