Change Data Capture (CDC) has become a foundational pattern in modern data engineering, yet many practitioners struggle with its nuances and implementation challenges. At its essence, a CDC data pipeline continuously identifies and captures changes made to data in source systems, then propagates those changes to target systems with minimal latency. Unlike traditional batch ETL that periodically extracts entire datasets, CDC tracks only what changed—insertions, updates, and deletions—enabling real-time data synchronization while dramatically reducing processing overhead. This guide explores the mechanics, architectures, and practical considerations that data engineers must understand to build effective CDC pipelines.
How CDC Actually Works Under the Hood
CDC pipelines operate by monitoring database transaction logs rather than querying tables directly. Every database maintains a transaction log for crash recovery—a sequential record of every change committed to the database. PostgreSQL calls this the Write-Ahead Log (WAL), MySQL calls it the binary log (binlog), and SQL Server uses the transaction log. These logs contain low-level representations of every insert, update, and delete operation, making them perfect sources for change capture.
When you implement CDC, you’re essentially tapping into this existing infrastructure. A CDC tool connects to the database and reads its transaction log, decoding the binary format into structured change events. Each event contains the operation type, the affected table and row, the before and after values for updates, and metadata like transaction IDs and timestamps. This approach has several critical advantages over alternative methods.
Query-based CDC, where you poll tables looking for changed records, requires timestamp columns or version numbers and misses deletions entirely. Trigger-based CDC, where database triggers fire on changes to write to audit tables, adds overhead to every transaction and can impact application performance. Log-based CDC elegantly sidesteps both issues—the transaction log already exists for the database’s own purposes, so you’re leveraging existing infrastructure rather than adding new components that slow down operational systems.
The technical challenge lies in correctly parsing transaction logs, which are database-specific binary formats. Tools like Debezium, AWS DMS, and Striim encapsulate this complexity, providing connectors that understand the log format for each database type. They handle nuances like handling large transactions that span multiple log segments, dealing with DDL changes, and maintaining exactly-once delivery semantics despite network failures or process crashes.
CDC Methods Comparison
The Architecture of Production CDC Pipelines
A production-grade CDC pipeline consists of several interconnected components, each serving a specific purpose in the data flow. Understanding this architecture helps you make informed decisions about technology choices and deployment patterns.
Source Database Preparation forms the foundation. You must configure your source database to retain transaction logs long enough for CDC tools to process them. This means setting appropriate log retention policies—too short and you risk data loss during outages, too long and you waste storage. You’ll also need database credentials with replication permissions, which are more privileged than standard application accounts but less than full administrative access.
CDC Capture Layer contains the connectors or agents that read transaction logs. These components run separately from your source database, connecting to it over the network. They maintain state about which log positions have been processed, ensuring exactly-once semantics. If a connector crashes and restarts, it resumes from its last checkpoint rather than reprocessing already-handled changes or skipping new ones.
Message Broker or Streaming Platform serves as the intermediary between capture and consumption. Apache Kafka is the most common choice due to its durability, scalability, and rich ecosystem. The capture layer writes change events to Kafka topics, which provides buffering, replay capabilities, and the ability to support multiple downstream consumers. Each source table typically maps to its own topic, though you can customize this based on your access patterns.
Transformation and Processing Layer applies business logic to raw change events. This might involve filtering out sensitive columns, enriching events with reference data, aggregating changes, or reshaping event structure to match target system requirements. Tools like Apache Flink, Kafka Streams, or custom applications consume from Kafka topics, transform the data, and produce to new topics or write directly to target systems.
Target Systems and Sinks represent the final destination for CDC data. These might be data warehouses like Snowflake or Redshift, data lakes in S3, search indexes like Elasticsearch, cache layers like Redis, or downstream microservices. Each target requires its own connector or sink that understands how to apply change events appropriately—using upserts for updates, handling deletes, and maintaining consistency.
Handling the Three Core Operations
Every CDC pipeline must correctly handle three fundamental operations: inserts, updates, and deletes. The naive approach—simply replaying operations in order—works for simple cases but breaks down when you encounter edge cases.
Inserts are the most straightforward. When a CDC event indicates a new row, you insert it into your target system. The complexity arises when you receive duplicate insert events due to retries or replay scenarios. Your target system must handle these idempotently, typically using “insert if not exists” semantics or upsert operations that update when a row already exists.
Updates require more sophisticated handling. A CDC update event contains both the before and after states of the row. You must identify the target row using its primary key or unique identifier, then apply the new values. The challenge is handling out-of-order updates—if an older update arrives after a newer one has already been applied, you must not overwrite the newer data. This requires comparing timestamps or version numbers to determine which change is authoritative.
Deletes present unique challenges because they remove data rather than changing it. In the target system, you can implement hard deletes that physically remove rows, or soft deletes that mark rows as deleted using a flag or deletion timestamp. Soft deletes preserve history and allow for potential recovery, but complicate queries that should exclude deleted records. Hard deletes save storage but lose historical context.
Consider this example: an order is created (insert), updated to change the shipping address (update), then cancelled and removed from the system (delete). Your CDC pipeline must process these operations in sequence, handling the possibility that they might arrive out of order due to network delays or processing parallelism. Maintaining transaction boundaries—ensuring changes from the same source transaction are applied together—is crucial for maintaining consistency.
Managing Schema Evolution in CDC Pipelines
Schema changes represent one of the most challenging aspects of operating CDC pipelines in production. Source databases inevitably evolve—developers add columns for new features, change data types to accommodate larger values, rename tables for clarity, or split tables during refactoring. Your CDC pipeline must handle these changes gracefully without data loss or downtime.
Additive Changes like adding new columns are the easiest to handle. Most CDC tools detect new columns in transaction logs and automatically include them in change events. Your target systems must accommodate these new fields, either by dynamically adjusting their schema or by accepting and storing the additional data even if it’s not immediately used. Schema registries like Confluent Schema Registry help by versioning your event schemas and enforcing compatibility rules.
Destructive Changes like dropping columns require more care. If a column is removed from the source, your CDC pipeline will stop receiving values for it. Existing data in target systems might retain old values unless you explicitly remove them. You need to decide whether to preserve historical data for that column or remove it to match the new source schema.
Type Changes can break CDC pipelines entirely. If a source column changes from integer to string, existing consumers expecting integers will fail when they receive string values. The solution typically involves versioning your event schemas, supporting both old and new formats during a transition period, and coordinating updates across all pipeline components.
A robust approach to schema evolution involves:
- Schema Registry: Centrally manage and version all event schemas, enforcing compatibility rules that prevent breaking changes
- Compatibility Modes: Configure forward, backward, or full compatibility based on your requirements
- Schema Validation: Validate incoming events against expected schemas, quarantining invalid events for manual review
- Monitoring and Alerting: Detect schema changes immediately and alert relevant teams
- Rollback Procedures: Maintain the ability to revert schema changes if they cause downstream issues
Some organizations adopt a pessimistic approach where schema changes require manual approval and coordination across teams. Others embrace optimistic evolution, allowing changes automatically and dealing with issues reactively. The right choice depends on your organization’s risk tolerance and operational maturity.
Common CDC Pipeline Patterns
Monitoring and Operational Considerations
Operating CDC pipelines in production requires comprehensive monitoring and alerting. Unlike batch ETL jobs that run on schedules with clear success or failure states, CDC pipelines run continuously, making issues harder to detect until they accumulate into significant problems.
Replication Lag measures the time between when a change occurs in the source database and when it appears in the target system. This is your primary health metric. Small amounts of lag (seconds to minutes) are normal, but growing lag indicates your pipeline can’t keep up with the change rate. Monitor lag at each stage—capture lag, processing lag, and sink lag—to identify bottlenecks.
Throughput Metrics track how many change events your pipeline processes per second. Compare this to your source database’s transaction rate. If your throughput is consistently lower than the change rate, lag will grow over time. Throughput should remain relatively stable under normal conditions, with spikes corresponding to known high-activity periods.
Error Rates and Dead Letter Queues capture events that couldn’t be processed successfully. Schema mismatches, constraint violations, and transformation errors can cause events to fail. Rather than blocking the entire pipeline, route failed events to dead letter queues for manual investigation. High error rates indicate systematic problems that need immediate attention.
Resource Utilization across all pipeline components affects performance and cost. Monitor CPU, memory, network bandwidth, and storage for CDC connectors, message brokers, transformation workers, and target systems. Resource exhaustion can cause lag to grow or components to crash.
Common operational issues include:
- Log retention too short: Source database purges transaction logs before CDC processes them, causing data loss
- Network partitions: Connectivity issues between components cause buffering and eventual overflow
- Target system slowness: Slow writes to target systems back up the entire pipeline
- Schema incompatibilities: Unhandled schema changes break event processing
- Resource exhaustion: Insufficient CPU, memory, or disk causes performance degradation
Implement automated alerts for these conditions, with thresholds based on your SLAs. For example, alert when replication lag exceeds 5 minutes, error rate exceeds 1%, or dead letter queue depth grows beyond 1000 events.
Data Consistency and Exactly-Once Semantics
CDC pipelines must maintain data consistency even in the presence of failures. The gold standard is exactly-once semantics—every source change appears exactly once in the target, never duplicated or lost. Achieving this in distributed systems is notoriously difficult.
At-least-once delivery guarantees that no changes are lost, but allows duplicates. If a CDC component crashes after processing an event but before acknowledging it, the event will be reprocessed after restart. This is the easiest guarantee to implement and sufficient for idempotent operations.
At-most-once delivery ensures no duplicates but allows data loss. Events are acknowledged before processing, so crashes can cause lost changes. This is rarely acceptable for CDC pipelines where accuracy is paramount.
Exactly-once delivery requires transactional coordination between components. Kafka’s transactional APIs, combined with idempotent sinks, can achieve this. The CDC component must write to Kafka and update its checkpoint in a single atomic transaction. The sink must deduplicate events or use idempotent write operations.
In practice, many organizations implement idempotent sinks (operations that produce the same result when applied multiple times) and accept at-least-once delivery. Upsert operations, where you insert if the row doesn’t exist or update if it does, are naturally idempotent. Delete operations are idempotent because deleting an already-deleted row is a no-op.
Transaction boundaries add another layer of complexity. Changes from the same source transaction should be applied atomically to the target—either all or none. If you apply half of a multi-table transaction and then fail, you’ve violated consistency. CDC tools track transaction boundaries in their events, allowing downstream systems to batch related changes together.
Performance Optimization Strategies
As data volumes grow, CDC pipeline performance becomes critical. Several optimization strategies can dramatically improve throughput and reduce latency.
Parallelization distributes processing across multiple workers. Kafka’s partitioning naturally enables this—each partition can be consumed independently. Partition your CDC topics based on keys that allow parallel processing without violating ordering guarantees. For example, partition by entity ID so all changes to the same entity go to the same partition, maintaining order while allowing different entities to be processed in parallel.
Batching groups multiple small operations into larger batches before writing to target systems. Instead of issuing a separate write for each change event, accumulate events for a short period (seconds) and write them as a batch. This dramatically reduces network round trips and improves target system throughput. The tradeoff is slightly increased latency.
Filtering and Projection reduces data volume by excluding unnecessary columns or filtering out uninteresting changes. If downstream consumers only need specific tables or columns, filter them at the CDC source rather than transmitting and processing everything. This reduces network bandwidth, message broker storage, and processing load.
Target System Optimization focuses on how you write to destination systems. Use bulk loading APIs when available, maintain appropriate indexes, and consider partitioning large target tables. For data warehouses, write to staging areas first, then merge in batch rather than performing individual upserts.
Conclusion
CDC data pipelines represent a fundamental shift from periodic batch processing to continuous data integration, enabling real-time analytics and operational insights that drive modern businesses. By capturing changes at the source through transaction logs and propagating them with minimal latency, CDC pipelines provide the foundation for data architectures that are both scalable and responsive to business needs.
The complexity of CDC—handling schema evolution, maintaining consistency, and managing distributed system challenges—requires careful design and operational discipline. However, the benefits of real-time data synchronization, reduced processing overhead, and elimination of batch windows make CDC an essential pattern for data engineers building modern data platforms. Understanding these fundamentals positions you to make informed architectural decisions and operate CDC pipelines successfully in production environments.