# Change Data Capture (CDC)

> Captures and propagates incremental data changes from source systems in real-time.

- **Category**: Data Science
- **Subcategory**: Data Integration
- **Canonical URL**: https://designpattern.fyi/patterns/change-data-capture/

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## Description
**Context**: Traditional batch ETL has high latency and resource costs. CDC captures database changes (inserts, updates, deletes) in real-time for downstream systems.


## Use Cases
Real-time data synchronization, analytics on recent changes, and keeping downstream systems in sync with operational databases.



## Implementation Example

```python
# CDC Pattern (Conceptual) class CDCConsumer: def __init__(self, source_db): self.source = source_db self.offset = 0
def consume_changes(self): # Get changes since last offset changes = self.source.get_changes(since=self.offset)
for change in changes: self.process_change(change) self.offset = change.position
def process_change(self, change): if change.type == "INSERT": self.handle_insert(change) elif change.type == "UPDATE": self.handle_update(change) elif change.type == "DELETE": self.handle_delete(change)
```



## Trade-offs


### Advantages

- - Low latency

- - Reduced load on source

- - Complete change history

- - Real-time synchronization




### Considerations & Drawbacks

- - Infrastructure complexity

- - Schema evolution challenges

- - Operational overhead

- - Initial load requirement







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**Reference**: [Original Source](https://debezium.io/documentation/)