Change Data Capture (CDC) is a data engineering technique that identifies, captures, and processes row-level changes occurring in operational databases and data sources. CDC pipelines enable organizations to maintain synchronized copies of data across multiple systems, keeping downstream analytics tables, data warehouses, and real-time applications current with source data modifications ¹⁾.

CDC serves as a foundational pattern in modern data architectures, addressing the challenge of efficiently propagating data changes from operational systems to analytical and downstream consumers. Rather than performing expensive full-table scans or reloading entire datasets, CDC techniques selectively capture and replicate only the modifications that occur—insertions, updates, and deletions—at the row level ²⁾.

Organizations implement CDC to solve several critical problems: reducing latency in data synchronization, minimizing computational overhead on source systems, enabling near-real-time analytics, and supporting event-driven architectures where downstream systems must react immediately to data changes. This is particularly important in scenarios where source databases experience high transaction volumes and full refresh approaches would be prohibitively expensive.

CDC pipelines can be sourced through two primary mechanisms. Native change data feeds extract changes directly from database transaction logs or built-in CDC functionality. Database systems like PostgreSQL (via WAL/logical decoding), MySQL (via binlog), Oracle (via LogMiner), and SQL Server provide native mechanisms for capturing committed changes at the source. Cloud data platforms increasingly expose these capabilities through managed CDC services ³⁾.

Snapshot-based inference represents an alternative approach where CDC systems infer changes by comparing periodic snapshots of the source data. When native change feeds are unavailable or impractical, organizations periodically extract full table snapshots, compare them against previous snapshots, and compute row-level differences. This approach is simpler to implement but typically introduces latency and may incur higher computational costs, as the comparison process scales with table size rather than change volume ⁴⁾.

Robust CDC implementations must address several operational challenges that arise in production environments. Out-of-order updates occur when changes arrive at the destination in a different sequence than they occurred at the source, potentially due to network delays, parallel processing, or asynchronous replication. CDC systems must preserve causality and apply updates in the correct logical order to avoid corrupting downstream data.

Delete operations present particular complexity, as the system must accurately distinguish between intentional deletions and records that simply failed to be captured. Some CDC approaches use soft deletes (marking records as deleted with a timestamp) rather than hard deletes, preserving history for auditing and enabling easier recovery from errors.

Late-arriving data refers to changes that are discovered or delivered after a significant delay, potentially after downstream systems have already processed subsequent updates. CDC pipelines must implement idempotent processing logic and maintain sufficient history to correctly retroactively apply delayed changes without creating inconsistencies ⁵⁾.

Modern CDC implementations employ several architectural patterns. Event streaming platforms like Apache Kafka, AWS Kinesis, and cloud messaging services serve as the backbone for many CDC pipelines, providing reliable, ordered delivery of change events. Change data capture connectors from platforms like Debezium extract changes from source databases and deliver them to streaming systems, while frameworks like Apache Flink or Spark Streaming process these events and apply them to downstream targets.

Cloud-native data platforms have increasingly integrated CDC capabilities directly into their platforms. Databricks, Snowflake, and similar systems now offer native CDC support, allowing users to capture changes from source systems and efficiently apply them to analytics tables without manual pipeline coding ⁶⁾. AutoCDC automates the detection and application of snapshot-based changes, further reducing the operational burden of implementing CDC for scenarios where periodic snapshots are the primary source of change information ⁷⁾.

CDC enables multiple critical use cases across organizations. Real-time analytics relies on CDC to keep analytical tables synchronized with operational sources, enabling dashboards and reports to reflect current data. Data replication uses CDC to maintain read replicas, backup copies, and distributed copies of operational databases. Event-driven architectures trigger downstream actions based on detected changes, such as notifying microservices when customer records are updated or initiating workflow processes when order status changes ⁸⁾.

CDC also supports compliance and auditing by maintaining detailed change logs that satisfy regulatory requirements for data governance and historical tracking, and enables data warehouse loading where CDC efficiently populates slowly-changing dimensions and fact tables without full reloads.

• Native CDC vs Snapshot-Based CDC
• AutoCDC vs Hand-Coded CDC
• AutoCDC