--- title: "Bi-Directional Sync vs CDC Duplicates: Reliability Guide" description: "Discover why CDC pipelines generate duplicates, their costly impacts, and how Stacksync's bi-directional sync ensures reliable, duplicate-free data flow." canonical: https://www.stacksync.com/blog/bi-directional-sync-vs-cdc-duplicates-reliability-guide last_modified: 2026-09-18 --- # Bi-Directional Sync vs CDC Duplicates: Reliability Guide Discover why CDC pipelines generate duplicates, their costly impacts, and how Stacksync's bi-directional sync ensures reliable, duplicate-free data flow. Author Ruben Burdin · Founder & CEO Published September 5, 2025 Updated September 18, 2026 Read time 7 min read DATA ENGINEERING Change Data Capture (CDC) pipelines face an inevitable challenge: duplicate messages. While exactly-once delivery is theoretically impossible, network partitions and crashes make it impossible to ensure a downstream system saw an event precisely once. [\[1\]](https://blog.sequinstream.com/why-duplicates-plague-cdc-pipelines-and-how-to-root-them-out) Traditional CDC tools accept these duplicates as an unavoidable consequence, but organizations requiring mission-critical data reliability need better solutions. This technical guide examines why CDC systems generate duplicates, their operational impact, and how purpose-built bi-directional synchronization platforms eliminate these issues through advanced architectural approaches. ## Understanding the CDC Duplicate Problem ### How WAL-Based CDC Creates Duplicates Postgres' logical replication is driven by Postgres' write-ahead log (WAL). Subscribers create a replication slot on a Postgres database. They then receive an ordered stream of changes that occurred in the database: every create, update, and delete. [\[1\]](https://blog.sequinstream.com/why-duplicates-plague-cdc-pipelines-and-how-to-root-them-out) The fundamental issue stems from the commit architecture: - 01 **Batch Processing**: CDC subscribers pull batches of change messages from the WAL - 02 **Sink Delivery**: Messages are shipped to downstream systems (Kafka, SQS, databases) - 03 **Offset Advancement**: Log Sequence Number (LSN) positions are periodically advanced in the database At any given time, a change data capture pipeline is in a partial commit state: it has pulled in many messages, some of them have been written to the sink, but the LSN/offset has not yet been advanced. If the connector crashes while in that partial commit state, Postgres will replay every message after the restart LSN on reconnect. [\[1\]](https://blog.sequinstream.com/why-duplicates-plague-cdc-pipelines-and-how-to-root-them-out) ### Traditional CDC Limitations Debezium follows this pattern. It relies on a restart LSN to track which messages have been processed, both in Postgres and its own internal store. When Debezium pulls a batch of changes from the WAL, it doesn't mark the LSN as processed until after it has successfully written those changes to its configured sink (like Kafka). [\[1\]](https://blog.sequinstream.com/why-duplicates-plague-cdc-pipelines-and-how-to-root-them-out) This means effectively every time you restart Debezium or Debezium's connection to Postgres is cycled, you'll get some number of duplicate messages. For high-throughput databases, these events can easily cause tens of thousands of duplicate deliveries. [\[1\]](https://blog.sequinstream.com/why-duplicates-plague-cdc-pipelines-and-how-to-root-them-out) ## The Operational Cost of CDC Duplicates ### Database Replication Issues Even with primary key-based upserts, replays create operational problems: - **Flapping**: Source row changes from A→B→C, but restarts cause A→B replays, temporarily reverting destination data to older states - **Eventual Consistency Problems**: Temporary data inconsistencies during replay windows - **Performance Overhead**: Unnecessary processing of duplicate change events ### Audit and Compliance Violations While CDC systems aim for idempotent processing, ensuring that duplicate changes do not result in unintended side effects or data inconsistencies [\[2\]](https://geeksforgeeks.org/system-design/change-data-capture-cdc), audit systems require precision: - **Compliance Gaps**: Multiple "user promoted to admin" records corrupt compliance timelines - **Financial Reconciliation Issues**: Double-logged transactions complicate regulatory reporting - **Data Integrity Violations**: Audit trails lose their single source of truth properties ### Side Effect Amplification Duplicate CDC messages trigger unintended operational consequences: - **Double Billing**: Payment processors receive multiple charge events - **Communication Spam**: Multiple password reset emails or notifications - **Workflow Corruption**: Business process automation triggers multiple times ## Stacksync's Bi-Directional Architecture Solution ### Advanced Idempotency Framework Unlike traditional CDC systems that accept duplicates, Stacksync implements comprehensive idempotency tracking at the message level: **Real-Time Change Messages:** - Each WAL transaction receives a unique LSN identifier - Messages within transactions get `commit_idx` sequence numbers - Generated `idempotency_key` combines `commit_lsn:commit_idx` for guaranteed uniqueness **Backfill Operations:** Stacksync uses a combination of the backfill's ID and the source row's primary keys to produce its idempotency_key for a message. That produces a stable key that ensures consumers only process a given read message for a row once per backfill. [\[1\]](https://blog.sequinstream.com/why-duplicates-plague-cdc-pipelines-and-how-to-root-them-out) ### Leaf-Level Filtering Architecture Stacksync uses its idempotency keys to filter "at the leaf", right before delivering to the destination. Whenever Stacksync delivers a batch of messages to a sink, it writes the idempotency keys for each message in that batch to a sorted set in Redis. Therefore, before it delivers a batch of messages to a sink, it can filter out any messages that were already delivered against that sorted set. [\[1\]](https://blog.sequinstream.com/why-duplicates-plague-cdc-pipelines-and-how-to-root-them-out) This approach provides: - **Pre-Delivery Deduplication**: Messages are filtered before reaching destination systems - **Atomic Tracking**: Redis sorted sets maintain delivery state with high availability - **Minimal Replay Windows**: Only edge cases between Redis availability and message delivery can cause replays ### True Bi-Directional Synchronization Stacksync eliminates CDC duplicate issues through architectural superiority: **Unified Sync Engine:** - Single, centralized mechanism manages data flow in both directions - Built-in conflict resolution prevents update wars and infinite loops - Transactional integrity across bi-directional operations **Field-Level Change Detection:** - Non-invasive CDC captures granular field modifications - Event-driven architecture processes changes in real-time - Intelligent state management prevents synchronization loops html ### Comparison: Traditional CDC vs Stacksync Bi-Directional | Aspect | Traditional CDC | Stacksync Bi-Directional | | --- | --- | --- | | Duplicate Handling | Accepts as inevitable | Active prevention through idempotency | | Restart Behavior | Replay from LSN position | Redis-backed filtering prevents replays | | Data Consistency | Eventual consistency | Real-time consistency across systems | | Error Recovery | Manual intervention required | Automated retry with exponential backoff | | Operational Overhead | High maintenance burden | Managed service with monitoring | ## Implementation Recommendations ### For CDC Duplicate Mitigation Organizations currently using traditional CDC tools should implement: - 01 **Consumer-Level Idempotency**: Include idempotency keys in message metadata. Some destinations allow deduplication keys on write – making delivery models exactly-once. Otherwise, the field is available as a last layer of idempotency protection. [\[1\]](https://blog.sequinstream.com/why-duplicates-plague-cdc-pipelines-and-how-to-root-them-out) - 02 **Monitoring and Alerting**: Track replay frequency and duplicate volumes to quantify operational impact - 03 **Downstream Deduplication**: Implement application-level duplicate detection where possible ### For Mission-Critical Operations Organizations with audit, compliance, or financial requirements should consider purpose-built solutions: - 01 **True Bi-Directional Platforms**: Eliminate root causes of duplicates rather than managing symptoms - 02 **Field-Level Synchronization**: Avoid batch-based approaches that create replay windows - 03 **Enterprise Security**: SOC 2, GDPR, HIPAA compliance for regulated environments ## Apply this in automotive components: original customer order and buyer change request Automotive EDI provides a concrete duplicate-demand failure case: a replayed purchase order or revision must retain its business identity. Apply the reliability principles here to partner order and revision references before another accepted demand record is created. | Record to reconcile | Responsible owner | Rule to preserve | | --- | --- | --- | | Original customer order | Customer operations | Preserve partner, plant, PO and line identities before creating ERP references. | | Buyer change request | Order management | Keep the requested differences and applicable version separate from the accepted order state. | | Engineering applicability | Engineering | Resolve the requested part or revision under the approved customer-program rules. | An exception to plan for: **The change arrives before the original is available**. Hold it with its references and investigate missing intake or sequence. Do not invent an original order to satisfy a processing dependency. Acceptance check for the pilot: the original order and every accepted change remain traceable. The [automotive components hub](https://www.stacksync.com/industry/manufacturing/automotive-components) connects the broader operating context. These guides develop the specific record mappings and decisions for this application: - [Automotive component EDI 850 and 860: preserve firm orders and approved revisions](https://www.stacksync.com/blog/automotive-edi-850-860-firm-orders-revisions) - [EDI order and revision automation for automotive component suppliers](https://www.stacksync.com/use-case/automotive-edi-order-revision-automation) ![Automotive component EDI 850 and 860: preserve firm orders and approved revisions: record ownership](https://www.stacksync.com/images/blog-diagrams/icp-automotive-edi-850-860-firm-orders-revisions.webp) ![Automotive component EDI 850 and 860: preserve firm orders and approved revisions: operating sequence](https://www.stacksync.com/images/blog-diagrams-mermaid/icp-automotive-edi-850-860-firm-orders-revisions.webp) ## Conclusion The difference between traditional CDC and advanced bi-directional synchronization comes down to how they handle the inevitable reality of duplicates. While traditional CDC tools accept duplicates as a natural consequence of at-least-once delivery, purpose-built solutions actively work to minimize them through idempotency tracking and filtering. For use cases where duplicates are particularly costly (audit logs, financial transactions, or systems that trigger expensive side effects), tracking message delivery at the individual message level provides significant value. [\[1\]](https://blog.sequinstream.com/why-duplicates-plague-cdc-pipelines-and-how-to-root-them-out) Organizations operating mission-critical systems requiring guaranteed data consistency should evaluate bi-directional synchronization platforms like Stacksync. These solutions eliminate the architectural limitations that cause CDC duplicates while providing real-time, two-way data flow with enterprise-grade reliability and security. Ready to eliminate CDC duplicates from your data architecture? Explore how [**Stacksync's**](https://www.stacksync.com/)purpose-built bi-directional synchronization delivers guaranteed data consistency without the operational overhead of traditional integration maintenance. FAQ ## Frequently asked questions What is two-way data sync? Two-way data sync, also called bidirectional synchronization, is a method of automatically updating data between two connected systems so that both stay consistent. When a record is created, updated, or deleted in either system, the change is reflected in the other within seconds. This differs from one-way sync which only copies data in a single direction. How is two-way sync different from ETL? ETL (Extract, Transform, Load) is a one-way, batch-oriented process that moves data from sources to a data warehouse on scheduled intervals. Two-way sync is real-time and bidirectional, keeping operational systems (CRMs, ERPs, databases) in continuous alignment. ETL is designed for analytics, while two-way sync is designed for operational data consistency. What are the benefits of bidirectional sync? Bidirectional sync eliminates manual data entry between systems, ensures all teams work with current data, prevents conflicting records across departments, and reduces integration maintenance costs. By keeping systems aligned in real time, businesses avoid the data drift, stale information, and reconciliation overhead that plague one-way or batch sync approaches. How does Stacksync handle sync conflicts? Stacksync uses configurable conflict resolution to handle simultaneous updates across systems. Options include timestamp-based resolution (last write wins), system priority (one system always takes precedence), field-level rules (different fields can have different priorities), and manual review queues for ambiguous conflicts. All resolutions are logged for auditability. Which systems support two-way sync with Stacksync? Stacksync supports two-way sync between 1,000+ connectors including Salesforce, HubSpot, NetSuite, PostgreSQL, MySQL, Snowflake, BigQuery, MongoDB, Shopify, Zendesk, and more. Any combination of CRM, ERP, database, and SaaS application can be connected with bidirectional real-time synchronization through the visual no-code interface. About the author Ruben Burdin Founder & CEO Ruben Burdin is the Founder and CEO of Stacksync, the first real-time and two-way sync for enterprise data at scale. Ruben is a Y Combinator alumni with a strong background in software engineering and business. [All posts by Ruben Burdin](https://www.stacksync.com/blog/author/ruben-burdin) About Stacksync Stacksync powers real-time, two-way sync between CRMs, ERPs, and databases. Engineers sync data at scale and automate workflows, not dirty API plumbing. Stacksync ## Explore these integrations and topics - [platform Two-way sync](https://www.stacksync.com/two-way-sync) - [Two-way sync guides Understand two-way sync, record matching, field ownership, and production readiness.](https://www.stacksync.com/blog#topic-two-way-sync) Keep reading ## Related articles [All articles](https://www.stacksync.com/blog) [Data engineering Automotive component suppliers: connect Salesforce program awards and NetSuite demand 4 min read read](https://www.stacksync.com/blog/automotive-components-salesforce-netsuite-program-award-demand) [Data engineering Automotive suppliers: separate firm releases and forecasts in Dynamics and SQL Server 4 min read read](https://www.stacksync.com/blog/automotive-dynamics-sql-server-firm-release-forecast) [Data engineering Automotive components: connect NetSuite shipments to Postgres lot evidence 4 min read read](https://www.stacksync.com/blog/automotive-netsuite-postgres-lot-shipment-traceability) ## You just read how it should work. See it run on your own data. [Book a demo](https://www.stacksync.com/book-a-demo) [Get started](https://app.stacksync.com/)