What is Backfill & Reprocessing?

Definition
Backfilling means loading or correcting historical data that was never processed correctly. Reprocessing means running existing data through new or fixed logic to repair incorrect results.
The Core Problem:
Imagine you ship a bug in your analytics pipeline. For 90 days, your "active users" metric counted bots as real users. Now your dashboard shows inflated numbers, your ML model trained on wrong labels, and your CFO made decisions based on bad data. The raw event logs are still there, untouched in storage. But every derived table, aggregate, and report is now polluted.

This is what backfill and reprocessing solve: repairing your data history when logic changes or bugs are discovered.

Backfill Versus Reprocessing:
Think of backfill as filling gaps. You add a new data source but it has 2 years of history you never processed. Or a daily job failed for a week, leaving missing partitions. Backfill loads that historical data from scratch, often from raw logs or database snapshots.

Reprocessing is different. The data was already processed, but the transformation logic changed. Maybe you fixed a bug, redefined a business metric, or upgraded a feature calculation. You take the same raw input and run it through new logic to overwrite old incorrect outputs.

Why This Matters at Scale:
At companies like Netflix or Uber, pipelines process hundreds of thousands of events per second into petabytes of derived data. When logic changes, you might need to recompute 10 terabytes per day across 90 days. That is 900 terabytes of data movement. Without a systematic strategy, you either leave data inconsistent (old logic for 2023, new logic for 2024) or overwhelm your infrastructure trying to fix everything at once.

✓ In Practice: Production systems treat backfill and reprocessing as first class operations, not one-off scripts. They are orchestrated workflows with throttling, validation, and rollback mechanisms built in.

💡 Key Takeaways

✓Backfill loads historical data that was never processed, filling gaps from missed jobs or new data sources

✓Reprocessing reruns existing data through updated logic to fix bugs or apply new business rules

✓At scale, backfilling 90 days at 10 TB per day means moving 900 TB, requiring careful resource management

✓Without systematic strategies, data becomes inconsistent across time periods, polluting dashboards and models

📌 Examples

1Backfill example: A new Kafka topic starts collecting payment events. You need to load 18 months of payment history from database archives to make reporting complete.

2Reprocessing example: Your revenue calculation had a tax bug for 6 months. Raw events are correct, but all daily revenue aggregates need recomputation with fixed logic.

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