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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4August 29, 2026 481 2
