---
title: "How Debezium Captures Postgres Changes: 2026 Deep Dive"
description: "Discover how Debezium captures PostgreSQL changes via WAL and why Stacksync offers superior bi-directional real-time sync without Kafka complexity."
canonical: https://www.stacksync.com/blog/debezium-captures-postgres-changes-2025-deep-dive
last_modified: 2026-07-28
---

# How Debezium Captures Postgres Changes: 2026 Deep Dive

Discover how Debezium captures PostgreSQL changes via WAL and why Stacksync offers superior bi-directional real-time sync without Kafka complexity.

Author

Ruben Burdin · Founder & CEO

Published

September 1, 2025

Updated

July 28, 2026

Read time

11 min read

DATA ENGINEERING

Enterprise organizations face a critical challenge in modern data architecture: maintaining consistent, real-time information across operational systems while avoiding the infrastructure complexity and limitations of traditional change data capture solutions. While Debezium established foundational CDC capabilities for PostgreSQL, its architecture introduces significant operational overhead that modern database synchronization platforms have evolved to eliminate.

![Comparison of Debezium plus Kafka versus Stacksync for Postgres CDC](https://www.stacksync.com/images/blog-highlights/debezium-vs-stacksync-summary.webp)

## The Operational Data Synchronization Problem

Organizations implementing change data capture face fundamental constraints with traditional approaches. Batch-based ETL processes introduce significant latency. By the time data is extracted, processed, and loaded into a target system, it's already hours or even days old. This inherent delay creates a gap between when an event happens and when you can act on it [\[1\]](https://striim.com/blog/change-data-capture-cdc-what-it-is-and-how-it-works), making operational systems inconsistent and unreliable.

Modern enterprises require real-time data synchronization across CRMs, ERPs, and databases where inconsistencies directly impact customer experience and business operations. Traditional CDC tools like Debezium address change detection but create new challenges through complex infrastructure dependencies and one-way data flows that fail to meet bi-directional sync requirements.

Key Technical Challenges:

- Complex Kafka infrastructure requirements for basic synchronization
- One-way streaming limiting operational system integration
- Replication slot failure risks affecting database stability
- Performance scaling requiring extensive expertise and tuning

## What is Debezium and Its Postgres Integration

Debezium captures row-level changes in PostgreSQL databases and streams them to Kafka topics through Kafka Connect source connectors. The platform leverages PostgreSQL's write-ahead log (WAL) to detect database modifications using native replication mechanisms.

![Debezium's data path from the Postgres WAL to a Kafka topic](https://www.stacksync.com/images/blog-diagrams/debezium-wal-pipeline.webp)

*The four stages Debezium's connector runs on every change.*

### PostgreSQL WAL-Based Change Detection

Debezium interfaces with PostgreSQL's write-ahead log, a sequential record of all database modifications essential for ACID compliance. When PostgreSQL executes transactions, it follows this process:

- 01 Transaction Processing: PostgreSQL validates operations and opens transactions for modifications
- 02 WAL Record Generation: Creates WAL records with unique Log Sequence Numbers (LSN) before in-memory changes
- 03 Buffer Management: Writes WAL records to memory buffers before row insertions
- 04 Commit Processing: Flushes WAL buffers to persistent storage upon transaction commit
- 05 Replication Slot Access: Debezium connects via replication slots to consume WAL records using `pgoutput` plugin

This architecture provides ordered change processing and ensures only committed transactions are captured. `pgoutput` is the default output plugin, but PostgreSQL supports others with different tradeoffs; see our [logical decoding plugins guide](https://www.stacksync.com/blog/postgresql-logical-decoding-plugins-developers-guide) for a breakdown of `pgoutput` vs `wal2json` vs `decoderbufs`.

### Debezium's Change Capture Implementation

The platform follows a structured approach combining initial snapshots with continuous streaming:

Initial Snapshot Phase:

- Starts SELECT transactions for consistent read views
- Records current WAL LSN positions for transition points
- Scans configured tables generating read events for existing data
- Maintains consistency between historical and streaming data

Continuous Streaming Phase:

- Transitions to real-time streaming from stored LSN positions
- Converts PostgreSQL logical decoding events to standardized formats
- Routes changes to dedicated Kafka topics per table
- Manages offset tracking and WAL record cleanup

While this approach guarantees comprehensive data capture, it introduces significant operational complexity through mandatory Kafka dependencies.

## Critical Limitations of Traditional CDC Architecture

### Infrastructure Complexity and Operational Overhead

Debezium's architecture requires extensive Kafka infrastructure even for basic database synchronization scenarios. Organizations must deploy and maintain:

- Kafka cluster operations with topic management and partitioning strategies
- Kafka Connect worker configuration and monitoring systems
- Consumer offset tracking and distributed error handling
- JVM tuning and resource allocation across multiple components

This infrastructure overhead diverts engineering resources from core business development to integration maintenance, precisely the "dirty plumbing" work that reduces competitive advantage. Teams that want the durability of a log-based queue without standing up Kafka can also build one directly on Postgres itself; see how to [build SQS- and Kafka-like queues in raw SQL](https://www.stacksync.com/blog/postgres-cdc-without-debezium-build-sqskafka-sql).

### Replication Slot Risk and Database Impact

Single message processing failures can lock Debezium operations, causing replication slot accumulation that risks database stability. When synchronization fails:

- WAL files accumulate, potentially exhausting storage capacity
- Checkpoint processing overhead increases database resource consumption
- System outages become possible from storage exhaustion
- Recovery requires manual intervention and expertise

### Performance Scaling Challenges

Achieving production-scale throughput with Debezium requires:

- Multiple replication slot configurations for parallel processing
- Complex Kafka partitioning and consumer group optimization
- Extensive monitoring and alerting infrastructure
- Specialized expertise in both PostgreSQL and Kafka ecosystems

These requirements create significant barriers for organizations seeking reliable database synchronization without dedicated platform engineering teams.

## Stacksync: Purpose-Built Database Synchronization Platform

Stacksync addresses the fundamental limitations of traditional CDC approaches through a purpose-built architecture optimized for operational data synchronization. Unlike Debezium's analytics-focused streaming model, Stacksync delivers true bi-directional sync between operational systems with enterprise-grade reliability and simplified implementation.

### Real-Time Bi-Directional Synchronization

Stacksync is engineered specifically for real-time, bi-directional data synchronization, providing the reliability, performance, and developer-focused features required to solve this problem effectively. By abstracting away the complexity of conflict resolution, error handling, and scalability, Stacksync empowers engineering teams to build robust, automated data flows and focus on delivering business value .

Technical Advantages Over Traditional CDC:

- Sub-second synchronization latency across 1,000+ pre-built connectors
- True bi-directional data flow with automated conflict resolution
- No Kafka infrastructure dependency through managed synchronization architecture
- Database-centric integration enabling familiar SQL-based development workflows

### Automated Data Sync Between Applications

Stacksync eliminates the operational complexity of traditional CDC while delivering superior performance. The platform achieves 6.8x performance improvements over Kafka-based approaches while providing higher availability and enhanced developer tooling.

Operational Benefits:

- No-code connector activation through OAuth and secure connection methods
- Automatic schema mapping with intelligent field transformation
- Built-in error handling with retry mechanisms and rollback capabilities
- Enterprise security compliance including SOC 2, GDPR, HIPAA certifications

### Low Code Integration Platform Architecture

Two-way sync tools solve this fundamental problem by enabling real-time, bi-directional data flow between systems. Rather than building and maintaining complex integration code, these platforms provide automated synchronization that keeps critical information consistent across your technology stack .

Stacksync's architecture addresses the core limitations of Debezium's approach:

| Aspect | Traditional CDC (Debezium) | Modern Sync Platform (Stacksync) |
| --- | --- | --- |
| Sync Direction | One-way streaming designed mainly for analytics pipelines | True bi-directional sync built for real-time operational workflows |
| Infrastructure | Requires Kafka and related infrastructure to manage streams | Fully managed synchronization service with no Kafka overhead |
| Operations | High operational complexity with tuning, monitoring, and maintenance | Automated operations with built-in recovery and reliability |
| Architecture Focus | Optimized for analytics and downstream data consumption | Optimized for keeping operational systems consistent and in sync |

### Key Takeaways

Traditional CDC tools like Debezium excel at streaming changes into analytics stacks but introduce infrastructure and operational complexity.

Modern sync platforms focus on real-time, bi-directional synchronization, making them better suited for operational use cases across CRMs, ERPs, and databases.

The core difference is architectural intent: analytics pipelines versus reliable, always-consistent operational systems.

## Comprehensive ETL Tools Comparison for Modern Requirements

While general-purpose ETL and iPaaS platforms can be adapted for sync tasks, they often introduce unnecessary complexity, cost, and performance limitations. However, for the increasingly common challenge of keeping operational systems like CRMs, ERPs, and databases in real-time alignment, a purpose-built platform is superior. For a full vendor-by-vendor breakdown of Debezium, Confluent, Striim, AWS DMS, and Fivetran on cost and complexity, see our [Postgres CDC tool comparison](https://www.stacksync.com/blog/postgres-change-data-capture-speed-cost-complexity-showdown).

### Database Synchronization vs Traditional ETL

Modern organizations require evaluation criteria that prioritize operational capabilities over analytics-focused features:

Real-Time Data Synchronization Requirements:

- Real-time or near real-time replication: Keeps data synchronized as changes occur, enabling timely analytics, operational dashboards, and fast decision-making [\[2\]](https://matillion.com/learn/blog/change-data-capture-tools)
- Bi-directional conflict resolution for concurrent modifications across systems
- Operational system integration without performance degradation
- Simplified deployment and maintenance for engineering efficiency

Performance and Reliability Standards:

- Reduced load on source systems: Transfers only changed data instead of entire datasets, minimizing resource usage [\[2\]](https://matillion.com/learn/blog/change-data-capture-tools)
- Sub-second latency for mission-critical business processes
- Automated error recovery without manual intervention
- Enterprise security and compliance requirements

### Bi-Directional Sync Tools Evaluation

Organizations evaluating synchronization platforms should prioritize operational focus over analytics capabilities. As data ecosystems grow more complex, investing in robust two-way synchronization isn't optional, it's essential for operational efficiency and data-driven decision making. The right tool eliminates data silos, reduces manual work, and provides the foundation for a unified view of your business .

Critical Evaluation Criteria:

- True bi-directional synchronization (not dual one-way streams)
- No infrastructure dependencies or complex maintenance requirements
- Comprehensive connector ecosystem for CRMs, ERPs, and databases
- Built-in conflict resolution and automated error handling
- Enterprise security compliance and data governance capabilities

## Implementation Strategy for Modern Database Synchronization

Stacksync's approach eliminates traditional CDC complexity while delivering superior operational capabilities. Organizations implementing Stacksync typically achieve:

### Engineering Efficiency and Resource Optimization

- 90% reduction in integration maintenance through managed synchronization infrastructure
- Engineering time reallocation from integration plumbing to competitive advantage development
- Simplified deployment without Kafka expertise or infrastructure management
- Automated monitoring and recovery reducing operational overhead

### Operational Performance Improvements

- Real-time data consistency across CRMs, ERPs, and operational databases
- Sub-second synchronization enabling immediate business process automation
- Conflict resolution maintaining data integrity across concurrent system updates
- Scalable architecture handling millions of records without performance degradation

### Business Impact and Competitive Advantage

Modern database synchronization platforms like Stacksync deliver measurable business outcomes beyond technical improvements:

Operational Efficiency:

- Elimination of manual data reconciliation processes
- Real-time customer information across all systems
- Automated workflow triggers based on data changes
- Consistent reporting and analytics across platforms

Strategic Advantages:

- Engineering resources focused on product development rather than integration maintenance
- Faster time-to-market for new system integrations
- Reduced dependency on specialized integration expertise
- Enhanced customer experience through consistent data

## Debezium PostgreSQL Production Best Practices

Running Debezium against Postgres in production comes down to three priorities: protect the replication slot, pick the right output plugin, and monitor WAL growth before it becomes an outage.

### Capacity: size the slot pool with headroom

Set `wal_level = logical` and provision `max_replication_slots` and `max_wal_senders` for every connector you plan to run, plus spare headroom for a parallel migration or a failed-connector restart that needs a second slot temporarily.

### Output plugin: match it to your Postgres version

Debezium recommends `pgoutput` for PostgreSQL 10+; it ships with Postgres itself, so there is no extra extension to install. On older PostgreSQL versions, `wal2json_streaming` is the safer choice, since the non-streaming variant can throw an `OutOfMemoryError` when a single transaction is large.

### The real production risk: slot bloat

A replication slot that stops being consumed, because the connector crashed, was redeployed, or fell behind, keeps the WAL it hasn't delivered yet. Postgres will not reclaim that WAL until the slot catches up or is dropped, so an idle slot can fill the disk. `max_slot_wal_keep_size` caps how much WAL a lagging slot is allowed to retain, trading a hard disk-exhaustion failure for a slot invalidation you can alert on and recover from instead.

### What to monitor

- Replication slot size (via `pg_replication_slots`) and its growth rate, not just its current value
- WAL disk usage on the primary
- Connector lag against the current WAL position
- Alerting on slot invalidation, since a silently-dropped slot means you are missing changes, not just running late

## Migrating Off Debezium: A Practical Path

Teams that already run Debezium in production do not have to cut over in one step. A working Debezium setup starts with the same PostgreSQL prerequisites regardless of which platform reads the WAL afterward:

![Flowchart for migrating off Debezium to Stacksync without downtime](https://www.stacksync.com/images/blog-diagrams-mermaid/debezium-migration-flow.webp)

*A parallel-run migration keeps the old pipeline as a rollback path until parity is confirmed.*

`-- Enable logical replication and size the WAL sender poolALTER SYSTEM SET wal_level = logical;ALTER SYSTEM SET max_replication_slots = 10;ALTER SYSTEM SET max_wal_senders = 10;-- Dedicated replication role for the CDC connectorCREATE ROLE dbz WITH LOGIN PASSWORD 'dbz' REPLICATION;-- Track full row images so updates/deletes carry old valuesALTER TABLE customers REPLICA IDENTITY FULL;`

Each of those statements requires a restart or a table rewrite, and the replication slot they create keeps accumulating WAL on disk the moment it exists, whether or not a consumer is reading from it. That is the operational tax of Debezium's architecture: correct, but it puts the failure mode (disk exhaustion from an unconsumed slot) on the database itself.

### Parallel-run migration

- 01 Deploy Stacksync alongside the existing Debezium/Kafka Connect pipeline, pointed at the same tables
- 02 Compare row counts and checksums between both pipelines for a full replication cycle before touching production traffic
- 03 Cut consumers over to Stacksync once parity holds, keeping Debezium running as a rollback path
- 04 Drop the Debezium replication slot only after cutover is confirmed, since an orphaned slot silently blocks WAL cleanup

## Next-Generation Automated Data Sync Solutions

The evolution from traditional CDC to purpose-built synchronization platforms represents a fundamental shift in enterprise data architecture. While Debezium established important foundations for change data capture, modern organizations require solutions that eliminate infrastructure complexity while delivering true bi-directional operational synchronization.

Stacksync's architecture preserves the technical benefits of log-based change detection while addressing the operational limitations of Kafka-dependent implementations. This approach delivers enterprise-grade reliability with significantly reduced complexity, enabling organizations to achieve real-time data synchronization without specialized infrastructure expertise.

Transform Your Database Synchronization Strategy

Experience the next generation of database synchronization with Stacksync's bi-directional, real-time platform. Eliminate Kafka complexity, reduce engineering overhead, and achieve true operational data consistency across your enterprise systems.

[Start Your Free Trial](https://www.stacksync.com/) today and discover how purpose-built synchronization technology can accelerate your integration strategy while reducing operational complexity.

FAQ

## Frequently asked questions

Does Debezium support PostgreSQL logical replication out of the box?

Yes. Debezium reads PostgreSQL's write-ahead log through a logical replication slot using the pgoutput plugin, which ships with PostgreSQL by default. It requires wal_level set to logical, a dedicated replication role, and (for full before/after row images) REPLICA IDENTITY FULL on the tracked tables.

What are Debezium best practices for PostgreSQL in 2026?

Size max_replication_slots and max_wal_senders with headroom, use pgoutput on PostgreSQL 10+ (wal2json_streaming on older versions, to avoid an OutOfMemoryError on large transactions), set max_slot_wal_keep_size as a safety valve against slot bloat, and monitor replication slot size and WAL growth, not just connector lag, since a stalled slot silently fills disk before a connector looks unhealthy.

How does Debezium compare to Informatica or Fivetran for Postgres CDC?

Debezium is open-source and self-hosted, requiring a Kafka and Kafka Connect cluster you operate yourself. Informatica and Fivetran are managed but built for analytics pipelines rather than operational sync, and Fivetran's connectors can run up to 30-minute latency. Stacksync targets the middle: managed like Fivetran, but real-time and bi-directional like a custom Debezium pipeline, without the Kafka dependency.

Can I migrate off Debezium without downtime?

Yes, by running the new pipeline in parallel with the existing Debezium connector against the same tables, comparing row counts and checksums for a full replication cycle, then cutting consumers over once parity holds and dropping the old replication slot last.

Is Stacksync secure for enterprise use?

Yes. Stacksync is SOC 2 Type II certified, ISO 27001 certified, and HIPAA compliant. Data is encrypted in transit with TLS 1.2+ and at rest with AES-256. The platform uses zero-persistent-storage architecture, meaning your data is not retained after sync operations. Enterprise security features include SSO, SCIM, IP whitelisting, and full audit logging.

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

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