Everyone overcomplicates this. You don't need a massive Spark cluster or a fancy new streaming job.
The cheapest, most reliable way is to use the original raw data. If you don't have it, stop and fix that first.
Assuming you have logs or old DB snapshots:
* Process them directly from cloud storage (S3, GCS).
* Use a simple batch script in a throwaway container or serverless function.
* Transform and push to your new CDP in large, idempotent batches.
Key points:
* **No real-time processing.** It's historical data.
* **Throttle your writes** to avoid cost spikes on the new platform.
* **Idempotency is critical.** Script must be re-runnable.
Example batch script core:
```python
# Pseudocode - run in chunks
for batch in read_raw_events_from_s3(date='2022-01-01', chunk_size=10000):
transformed = simple_transform(batch)
post_to_cdp_api(transformed)
time.sleep(0.1) # throttle
```
Avoid rebuilding your entire pipeline. Just get the data moved. Complexity is the enemy.
Simplicity is the ultimate sophistication