Change Data Capture (CDC) Incremental Processing

How do we query incremental database CDC feeds using watermark timestamps to avoid full-table re-scans?

Production⏱ 2 Minutes⭐ 1300 XP

🤔 THE QUESTION

How do we query incremental database CDC feeds using watermark timestamps to avoid full-table re-scans?

💡 WHAT IS IT?

Filtering parquet CDC logs for records where ingestion_timestamp exceeds the last high watermark.

🎯 WHAT IS IT USED FOR?

High-frequency incremental ETL pipelines processing only new inserts and updates from operational DBs.

💻 EXAMPLE

from pyspark.sql.functions import col

incremental_df = spark.read.parquet("raw/cdc_feed") \
    .filter(col("ingestion_timestamp") >= last_watermark)

🎯 Mission Objectives

Practice typing production-grade PySpark code for Change Data Capture (CDC) Incremental Processing.

✓ Read raw CDC log feed
✓ Filter on high watermark timestamp
✓ Process incremental database deltas

🚀 Start Mission

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