Change data capture (CDC) tracks database changes such as inserts, updates, and deletes, so other systems can act on them in near real time. This is essential for teams working with modern analytics environments. However, many source databases don’t support CDC natively, including older relational databases and even some SaaS applications.
In cases where transaction logs are not accessible, alternative strategies such as query-driven CDC, logging via triggers, or comparison snapshots must be used. This blog covers how to choose the best CDC approach and create reliable data pipelines, either with complete custom coding or using an automated solution such as CData Sync.
Choosing the right capture mechanism
Log-based CDC reads database transaction logs and publishes changes to streaming systems, offering sub-second latency and strong delivery semantics. It is the preferred method because it introduces minimal load on the source system and captures every change type, including deletes. When transaction log access is unavailable, teams must evaluate three practical alternatives.
Query-based CDC, also called incremental query, polls source tables using a timestamp or an auto-incrementing ID column. It is simple and universally compatible, but it cannot reliably capture deleted rows and is not truly real-time. The key is selecting a well-indexed watermark column to avoid full table scans on every polling cycle.
Trigger-based CDC uses database triggers to write change records to an audit table, capturing all change types, including deletes, but it increases write overhead and creates tight schema coupling.
Snapshot-based CDC compares sequential full or partial table snapshots and works without any schema changes, though it carries a high resource cost and is best reserved for low-frequency archival use cases.
Method | Best for | Key limitation |
Query-based CDC | Periodic analytics sync | Misses deletes; near real-time |
Trigger-based CDC | Operational event pipelines | Increases write overhead |
Snapshot-based CDC | Low-frequency archival reporting | High resource cost at scale |
Planning initial snapshots and identity management
Start by taking an initial complete snapshot of the source table prior to implementing the change capture process. This ensures the downstream system starts with a complete dataset. Ensure synchronize the batches and use table locking to avoid data inconsistencies during the snapshot.
The primary keys or unique keys provide the basis for all de-duplication or merge logic performed downstream; it is necessary to ensure their stability before performing any snapshot process. The key logic used for composite keys must also be documented beforehand, and one must prepare for situations where the schema changes occur.
Buffering and distributing change events
Buffering uses an enterprise-grade persistent and scalable message queue or publish-and-subscribe system that allows us to store, propagate, and replay change events. Otherwise, if there is no buffer, then we would just pass the change events collected using some form of polling or triggers into our downstream system without any way of replaying in case of a consumer failure, because of tight coupling.
Kafka is a common choice as a buffering solution for the enterprise world. But depending on your existing infrastructure, you may use cloud-native equivalents such as Amazon Kinesis, Google Pub/Sub or Azure Event Hubs. This pattern is simple: a change is observed at the source system; we store the change along with a unique identifier of this change into our buffer; and it is consumed by downstream systems, which get replayed from the last consumed event position in case of failure.
Implementing a CDC gateway for centralized processing
The concept of CDC gateway ensures that all issues of filtering, transformation of format, and consumption-specific routing are done centrally, thereby avoiding redundant code and easing compliance management. Instead of consumers implementing their own logic for masking and normalization, the gateway handles the process one time for all consumers who receive change streams that have been formatted to suit them as required for various applications such as analytics platforms and data warehouses. The CDC gateway also implements role-based policies for all consumers, ensuring easy compliance audits.
Handling schema evolution and versioning
Changes in the schema are bound to happen in production systems. There will be cases where there will be a name change for a column, or addition of a column, or even the datatype for the column would have changed. If not handled correctly, it would cause failures in the pipeline. Use a register for all the schemas in a registry with explicit version numbers and automate the compatibility check before any such change is made. This, along with a rollback mechanism, where the schema is reverted to its previous version if a new change breaks a consumer, will help a great deal in case such a change breaks compatibility.
Adding observability and automated recovery
Silent failures happen with pipelines that do not have observability built in. In particular, poll or trigger-based CDC pipelines are prone to this problem. Monitoring metrics for lag between events and number of change events processed per second, error rate, duplicate events, and end-to-end freshness for each source table are the basic metrics necessary to identify any issue prior to it impacting the business user.
It is critical to bake automated recovery into your pipeline. Redelivery of events is handled through idempotency checks done by consumers. Reconciliation jobs periodically compare the state between the source and destination and alert the teams if there is missing data. Backoff logic on transient failures in extraction protects against failure caused by a temporary API glitch.
Piloting and iterating CDC implementations
Going small at first mitigates risks and identifies practical challenges regarding latency, data quality, and loads early on, prior to a more extensive implementation phase. A table feeding one consumer is enough for beginning pilots. What matters most at this stage is ensuring that data provenance works correctly based on a small sample set of data and validating key metrics with adequate loads before scaling out by domain. Adjustments that were not needed when going small become crucial when it comes to handling real production loads.
Operational best practices for non-native CDC
Discipline is necessary in running non-native CDC pipelines. Every change event needs buffering to separate the producers and consumers and to facilitate replaying of the change events. The gateway itself needs to take responsibility for transformation, masking, and compliance rather than leaving it to individual consumers. Scheduling of validation and reconciliation needs to be automated, while the polling interval needs to be re-evaluated periodically using source database load metrics to minimise burden.
There are certain common failure scenarios seen in non-native CDC implementations:
Missed deletes: Query polling cannot detect deleted rows. Using soft delete flags or trigger-based audit tables fills that gap.
Duplicate events: The absence of idempotency causes the same event to be processed more than once. Applying idempotent consumer logic with unique event IDs resolves this.
Performance overhead: Polling on unindexed columns slows the database down. Indexing watermark columns and optimising polling frequency addresses this.
Compliance gaps: Decentralised masking leaves sensitive data exposed. Introducing a CDC gateway with role-based access control (RBAC) and audit logging closes that risk.
A non-native CDC pipeline stays stable in production only when monitoring, automation, and cross-team communication work together.
How CData Sync simplifies CDC without native support
Building a non-native CDC pipeline from scratch means stitching together capture mechanisms, buffering layers, and monitoring tools, each requiring its own setup. CData Sync removes much of that complexity through its built-in CDC and History Mode features.
In a job, the CDC replication type automatically tracks inserts, updates, and deletes from supported sources and replicates only the changed records to the destination, eliminating the need for custom polling or trigger logic. A full list of CDC sources supported by CData Sync is available in the given documentation. For sources that do not support native CDC, CData Sync automatically falls back to incremental replication using a timestamp or integer-based check column, ensuring continuous data movement regardless of the source's native capabilities. For teams evaluating performance, this benchmarking article shows how enhanced CDC compares against traditional CDC in real-world tests.
The History Mode feature goes a step further by preserving every version of a row with timestamps, giving teams a complete audit trail for compliance, trend analysis, and debugging without re-querying the source. Details on the supported History Mode destinations in CData Sync are available here.
Both modes work across CData Sync's broad connector library, whether the source is a legacy on-premises database or a modern SaaS application, making it a straightforward path to production-ready incremental pipelines.
Frequently asked questions
What is the best way to implement CDC if my database doesn't support it natively?
When a database lacks native CDC, use methods like incremental queries, snapshot comparisons, or database triggers to detect changes, then stream those changes to downstream systems using a buffer or message queue.
Can CDC work reliably without access to transaction logs?
Yes, alternative CDC approaches like query-based polling and trigger-based logging can enable reliable change tracking, though they may increase latency or risk missing certain changes under heavy write loads.
How do I capture deletes without native CDC functionality?
To capture deletes, combine audit tables or triggers with soft delete flags, or periodically compare full or partial snapshots to detect removed records.
What are the biggest risks with query-based CDC?
The main risks include missing deletes, processing duplicate changes, handling schema drift, and increased database load due to polling.
How can I ensure my CDC pipeline handles schema evolution?
Use explicit schema versioning, implement a schema registry, and automate compatibility checks to ensure that downstream systems remain consistent as the schema changes.
Implement CDC pipelines faster with CData Sync
CData Sync provides more than 350 connectors with native CDC support, automated scheduling, and secure on-premises agent deployment for teams working with legacy or SaaS sources that lack native change tracking.
Start a free 30-day trial today or start a conversation with the team to learn more!
Replicate faster. Integrate smarter.
Whether you're syncing to a data warehouse, a cloud app, or a local database, CData Sync keeps your data flowing in real time — with the reliability your business depends on.
