Change Data Capture at Billion-Record Scale: Architecture Patterns for Incremental ETL in High-Volume Healthcare Claims Environments

Abstract

Healthcare claims adjudication systems accumulate transactional histories that reach billions of records, and moving that data into an analytical environment without disrupting the operational system that produces it is a harder engineering problem than general data warehouse literature usually acknowledges. This article examines change data capture (CDC), the practice of identifying and extracting only the records that changed since the previous processing cycle, rather than re-extracting an entire dataset, as the architectural answer to that problem at a billion-record scale. A tiered maturity framework organizes the discussion around three approaches: query-time delta detection using high-watermark timestamps, database-level transaction log mining, and event-driven real-time capture built on message-queue decoupling. Each tier is evaluated against five dimensions drawn from practitioner engagements in pharmacy benefits management and claims adjudication environments: extraction impact on the operational source system, change detection completeness, change detection accuracy, pipeline latency, and implementation complexity. The article gives particular attention to a constraint that moderate-volume Extract, Transform, Load (ETL) literature rarely has to confront directly: adjudication databases are latency-sensitive operational systems bound by regulatory data-handling obligations, which means an extraction architecture that would be a reasonable default at a smaller scale can become an operational risk once record counts cross into the billions. Data quality validation for incremental delta record sets receives separate treatment, since a change data capture cycle that correctly identifies every changed record can still load a delta set that is duplicated, out of order, or incomplete. The framework's engineering conclusions extend beyond healthcare to any high-volume transactional environment, financial services, telecommunications billing, retail transaction history, and government administrative systems that face the same underlying tension between operational latency sensitivity and analytical data currency.