What is Incremental Processing?

The Core Problem: Imagine you have a data warehouse holding 500 TB of event data. Your business wants hourly refreshed reports. If you recompute all transformations from scratch every hour, you face massive costs and cannot meet latency requirements. With 500 TB at 1 GB per second processing speed, a full scan takes 5.8 days, not one hour. How Incremental Processing Works: Instead of processing everything, you track what has changed. New ride events arrive? Process only those new events. User profile updated in your database? Capture just that change. This requires three components working together. First, you need reliable change detection. For append only logs like event streams, you track consumer offsets indicating which messages you have already processed. For database tables, you use Change Data Capture (CDC) to monitor transaction logs, or rely on timestamp columns like updated_at to identify new or modified rows. Second, your transformations must handle incremental updates safely. They should be idempotent, meaning running the same input twice produces the same output without corruption. They must merge new data with existing tables using business keys and versioning logic. Third, you maintain durable state about progress. This might be Kafka consumer offsets stored in a coordination service, high watermark values recording the latest timestamp processed per source table, or version metadata tracking which data made it into each destination table. Real World Scale: A ridesharing company processes 5 to 10 billion events daily. At peak, that is 200k to 500k events per second. Incremental processing lets them update analytics dashboards within 1 to 5 minutes instead of waiting hours for full batch recomputation. The trade is complexity: you now manage state, handle late arriving data, and ensure correctness across retries.