What is Incremental Data Processing? A Data Engineer’s Guide

As a data engineer, your goal is to build pipelines that are not just accurate, but also efficient, scalable, and cost-effective. One of the biggest challenges in achieving this is handling ever-growing datasets. If your pipeline re-processes the entire dataset every time it runs, your costs and run times will inevitably spiral out of control.

This is where incremental data processing becomes a critical strategy. Instead of running a full refresh of your data every time, incremental processing allows your pipeline to only process the data that is new or has changed since the last run.

This guide will break down what incremental data processing is, why it’s so important, and the common techniques used to implement it in modern data pipelines.

Why Do You Need Incremental Data Processing?

Imagine you have a table with billions of rows of historical sales data. Each day, a few million new sales are added.

• Without Incremental Processing: Your daily ETL job would have to read all billion+ rows, filter for yesterday’s sales, and then process them. This is incredibly inefficient.
• With Incremental Processing: Your pipeline would intelligently ask for “only the sales that have occurred since my last run,” processing just the new few million rows.

The benefits are clear:

• Reduced Costs: You use significantly less compute, which directly lowers your cloud bill.
• Faster Pipelines: Your jobs finish in minutes instead of hours.
• Increased Scalability: Your pipelines can handle massive data growth without a corresponding explosion in processing time.

Common Techniques for Incremental Data Processing

There are two primary techniques for implementing incremental data processing, depending on your data source.

1. High-Watermark Incremental Loads

This is the most common technique for sources that have a reliable, incrementing key or a timestamp that indicates when a record was last updated.

• How it Works:
  1. Your pipeline tracks the highest value (the “high watermark”) of a specific column (e.g., last_updated_timestamp or order_id) from its last successful run.
  2. On the next run, the pipeline queries the source system for all records where the watermark column is greater than the value it has stored.
  3. After successfully processing the new data, it updates the stored high-watermark value to the new maximum.

Example SQL Logic:

SQL

-- Let's say the last successful run processed data up to '2025-09-28 10:00:00'
-- This would be the logic for the next run:

SELECT *
FROM raw_orders
WHERE last_updated_timestamp > '2025-09-28 10:00:00';

• Best For: Sources like transactional databases, where you have a created_at or updated_at timestamp.

2. Change Data Capture (CDC)

What if your source data doesn’t have a reliable update timestamp? What if you also need to capture DELETE events? This is where Change Data Capture (CDC) comes in.

• How it Works: CDC is a more advanced technique that directly taps into the transaction log of a source database (like a PostgreSQL or MySQL binlog). It streams every single row-level change (INSERT, UPDATE, DELETE) as an event.
• Tools: Platforms like Debezium (often used with Kafka) are the gold standard for CDC. They capture these change events and stream them to your data lake or data warehouse.

Why CDC is so Powerful:

• Captures Deletes: Unlike high-watermark loading, CDC can capture records that have been deleted from the source.
• Near Real-Time: It provides a stream of changes as they happen, enabling near real-time data pipelines.
• Low Impact on Source: It doesn’t require running heavy SELECT queries on your production database.

Conclusion: Build Smarter, Not Harder

Incremental data processing is a fundamental concept in modern data engineering. By moving away from inefficient full-refresh pipelines and adopting techniques like high-watermark loading and Change Data Capture, you can build data systems that are not only faster and more cost-effective but also capable of scaling to handle the massive data volumes of the future. The next time you build a pipeline, always ask the question: “Can I process this incrementally?”