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Engineering teams implementing PostgreSQL face a persistent technical challenge: maintaining data consistency across operational systems without degrading database performance. Traditional batch-oriented integration creates operational lag, data silos, and complex maintenance overhead that diverts engineering resources from core product development.
Stacksync solves this challenge with purpose-built PostgreSQL CDC technology, delivering true bi-directional, real-time synchronization across more than 200 popular business applications and databases with no-code configuration and enterprise-grade security compliance.
Change data capture detects every incremental change to a database and delivers those changes to downstream systems. Change Data Capture (CDC) is a powerful technique used in event-driven architectures to capture change streams from a source data system, often a database, and propagate them to other downstream consumers and data stores such as data lakes, data warehouses, or real-time data platforms. [1]
When implemented correctly, the downstream system processes these changes to perfectly replicate the original dataset. Every insert, update, and delete operation must be captured and delivered in order without missing any alteration.
PostgreSQL 10+ includes logical replication built-in, recording every change in the Write Ahead Log (WAL). PostgreSQL CDC is driven by its Write-Ahead Logging (WAL), which also supports its replication process. The WAL maintains a record of all database updates, including when changes are made to database tables. Anytime data is inserted, updated, or deleted, it is logged in the WAL. [1]
In the context of PostgreSQL, CDC provides a method to share the change events from Postgres tables without affecting the performance of the Postgres instance itself. [1] The WAL represents the most reliable interface for PostgreSQL CDC, though it requires sophisticated tooling for effective implementation.
Almost every data-intensive application includes CDC components. Organizations with 10+ engineers typically implement CDC solutions as systems grow beyond single PostgreSQL instances toward specialized tools.
Common CDC use cases emerge as teams distribute work across multiple systems:
Database replication: Reliably mirror data across databases with greater flexibility than traditional PostgreSQL read-replicas. CDC enables primary database independence while providing powerful data transformation capabilities during transmission.
Real-time analytics: Stream changes directly to OLAP databases and data warehouses, enabling immediate insights and analytics without impacting production database performance.
Cache management: Ensure application caches and search indexes remain perfectly synchronized with source data, eliminating staleness issues while reducing unnecessary refresh operations.
Microservice consistency: Preserve data integrity across distributed systems by propagating changes to specialized services while maintaining strong transactional guarantees.
Event-driven automation: Trigger workflows and jobs with immediate, transaction-guaranteed execution, enabling responsive systems that act on data changes as they occur.
Audit logging: Capture every database change for compliance requirements or to power user-facing features like detailed history views and precision rollbacks.
Beyond specific use cases, CDC reduces technical debt while providing a foundation for scale. Rather than having systems constantly poll tables or use triggers for change detection, CDC provides a consistent pattern for detecting changes independently without additional database burden.
A reliable CDC system provides guarantees around reliability, performance, and data integrity while abstracting edge cases and bugs for confident development.
Delivery guarantees: Every database change must be delivered reliably. Many CDC implementations risk dropped messages (at-most-once delivery) or duplicates (at-least-once delivery). Optimal CDC systems provide exactly-once processing guarantees through sophisticated deduplication and acknowledgment methods. For PostgreSQL CDC use cases requiring consistency (replication, cache invalidation), at-least-once delivery represents the minimum requirement, though exactly-once processing proves ideal.
Throughput: Volume of change events handled within given timeframes, measured as bandwidth (MB/sec) with operations per second as proxy metrics. CDC solutions must handle peak database IOPS with capacity to spare. Insufficient CDC throughput creates lag during peak load, often resulting in permanent backlog if recovery never occurs.
Latency: Time delay between database changes and target system appearance. Some use cases (analytics, reporting) tolerate hours or days of latency. Customer-facing features (search index updates) require millisecond latency for accurate results. Measuring p95 or p99 latency contextualizes system tolerances and helps evaluate how CDC systems handle backlog situations.
Retries and error handling: System response to transient errors. Poison pill messages can crash CDC pipelines immediately, which proves beneficial for replication scenarios. Alternative implementations queue errant messages into dead letter queues (DLQ), log alerts, and attempt redelivery with exponential retries.
Ordering: Maintaining correct change event sequence prevents data corruption or inconsistencies from interdependent operations. Enforcing strict ordering often trades throughput and latency as parallel processing becomes more complex. Quality CDC solutions allow ordering configuration for specific change types, balancing speed with data structure requirements.
Schema evolution: Handling PostgreSQL database schema modifications including adding, altering, or removing columns. Schema changes can break downstream services, requiring either graceful propagation downstream or proactive system halting to prevent corruption.
Snapshots: Initial state capture of PostgreSQL tables and ability to handle subset targeting using SQL. Snapshotting enables CDC initialization and post-incident recovery. Quality CDC systems must handle massive table snapshots while simultaneously capturing new changes.
Database load: CPU and storage consumption on the database. Systems using polling or triggers can significantly slow database performance. WAL-based systems add virtually no overhead during normal operation, though disconnected replication slots can rapidly consume available storage.
Monitoring and observability: CDC often becomes critical application infrastructure requiring measurement and observation of all performance characteristics. Quality CDC solutions integrate with existing monitoring and observability tooling, providing clear traceable errors versus cryptic, convoluted logs.
Developers implementing PostgreSQL CDC directly have several methods available, each with distinct advantages and disadvantages.
Using triggers with audit tables involves creating database triggers that activate upon data modification events (INSERT, UPDATE, DELETE) on target tables. Triggered events record change details in separate audit log tables. Changes are captured instantly, enabling the real-time processing of change events. Triggers can capture all event types: INSERTs, UPDATEs, and DELETEs. By default, the PostgreSQL Trigger function used here adds helpful metadata to the events, e.g., the statement that caused the change, the transaction ID, or the session user name. [2]
However, triggers increase the execution time of the original statement and thus hurt the performance of PostgreSQL. Triggers require changes to the PostgreSQL database. [2]
Polling for changes involves adding dedicated timestamp columns to tables and periodically querying for records modified since the last check based on timestamps. This approach offers simple implementation for basic scenarios without requiring complex database configurations. However, it necessitates timestamp column presence, can be resource-intensive with frequent polling, and cannot reliably capture DELETE events unless employing soft deletion patterns.
Listen/Notify implements PostgreSQL's built-in publish-subscribe pattern where database sessions listen on channels for real-time data change notifications. Setting up triggers to fire on INSERT, UPDATE, or DELETE operations broadcasts JSON payloads containing change details to listening applications. While simple to implement with immediate notification capabilities, Listen/Notify provides only "at-most-once" delivery semantics requiring active connections when notifications occur, limits payloads to 8000 bytes, and offers no persistence for missed messages. These constraints make it suitable primarily for lightweight change detection scenarios rather than mission-critical systems requiring guaranteed delivery.
Logical replication (via the WAL) represents the most robust and efficient method. When comparing the three approaches to implementing change data capture with PostgreSQL, using logical replication is the clear winner. It is not only highly efficient, capturing all event types in real-time without harming the performance of the PostgreSQL database, but is also widely available, whether you're using a self-managed or a managed PostgreSQL installation, and applicable without introducing changes to the database schema. [2]
Consuming events via logical replication boils down to directly accessing the file system, which does not impact the performance of the PostgreSQL database. [2]
While each approach captures database changes, delivering those changes to other systems with sufficient performance guarantees adds significant complexity and work, explaining why most teams choose off-the-shelf solutions.
Implementing PostgreSQL CDC without building solutions from scratch requires evaluating robust tools and platforms offering unique features and capabilities.
Stacksync leads purpose-built CDC platforms designed specifically for PostgreSQL operational synchronization. Use Stacksync to build real-time, bi-directional syncs, orchestrate workflows, and observe every data pipeline with exactly-once processing guarantees across diverse destinations. Configure and sync data within minutes without code. Whether you sync 50k or 100M+ records, Stacksync handles all the dirty plumbing of infrastructure, queues and code so you don't have to . Available for self-hosting via Docker with Prometheus endpoints, web console, CLI, API, and developer tools, plus fully hosted Stacksync Cloud deployment.
Debezium provides widely adopted distributed CDC platform built on Apache Kafka and Kafka Connect. Captures changes in real-time using logical replication, streaming to Kafka topics with exactly-once processing. Sequin beats Debezium in a head-to-head comparison on messages per second by 6.8x and on average latency by 4.7x. The highest throughput we were able to consistently achieve with Debezium deployed on AWS MSK Connect was 6k ops/sec. At 10k ops/sec, Debezium's latency grew unbounded. [3] Requires Kafka setup and management, introducing operational complexity with configuration, maintenance, and debugging difficulties.
Stacksync Cloud provides fully hosted, highly available Stacksync deployment configured immediately for global scaling. Stacksync offers transparent, pay-as-you-go pricing based on the number of records synced, providing predictable costs and scalability, unlike Heroku Connect's opaque, contract-based model tied to Salesforce and infrastructure costs. Overall, Stacksync's flexible and clear pricing makes it more cost-effective and suitable for high-volume, predictable data synchronization needs. Enhanced features include tracing, alerting, team management, and disaster recovery with pay-per-processed-data pricing abstracting infrastructure.
Decodable offers hosted Debezium plus Apache Flink stack, ideal for organizations invested in these technologies wanting managed deployment. Captures database changes with strong deliverability guarantees, monitoring, and schema management tools. Requires learning pipeline configuration and applying SQL transforms in dashboard with Flink pipelines presenting moderate learning curves. Moderately expensive with pipeline "tasks" potentially costing hundreds of dollars monthly.
Confluent provides two options: Confluent Debezium and Direct JDBC PostgreSQL connector. Debezium version offers fully hosted implementation with enterprise security, compliance, monitoring, and scaling tools. Complex Debezium and Kafka topic configuration remains customer responsibility. Direct JDBC connector uses polling rather than logical replication, capturing only inserts and updates with delays. Both options carry enterprise pricing, trading engineering time for expensive infrastructure products.
Estuary provides fully managed CDC solution simplifying real-time data pipelines with pre-built connector selection enabling quick source-destination connections. Loads data into intermediary data sources enabling fast subsequent backfills with minimal database overhead.
Traditional ETL providers like Fivetran and Airbyte offer CDC capabilities but typically provide batch CDC rather than real-time solutions. Changes may take minutes to appear in streams or queues without maintaining atomic change records. These solutions target non-operational, analytics use cases rather than real-time operational requirements. They offer scheduled batch delivery to various destinations with simple setup but limited CDC configuration options. The enterprise data integration market is rapidly expanding, projected to grow from $15.22 billion in 2025 to over $30.17 billion by 2033. This surge is driven by digital transformation and the increasing need to connect diverse systems—CRMs, ERPs, databases, and SaaS applications—across modern organizations.
Major cloud infrastructure providers offer CDC products working within their ecosystems. AWS DMS (Database Migration Service), GCP Datastream, and Azure Data Factory can be configured to stream PostgreSQL changes to other infrastructure within respective platforms. These solutions prove effective for organizations committed to specific cloud providers comfortable with their tooling. They support logical replication with real-time insert, update, and delete capture, though delivery guarantees vary based on configuration and provider. Setup requires navigating web consoles, permissions systems, tooling, and logging to establish pipelines requiring familiarity with provider settings and configurations.
Change Data Capture has evolved from bespoke data integration approaches to specialized infrastructure with defined performance characteristics and features. For real-time operational integrity, a bi-directional sync platform is the only architecture that can eliminate data latency and guarantee consistency between your most critical business systems. Focusing on operational integrity is paramount for engineering teams building a reliable and scalable data ecosystem. By solving the core problem of data consistency between CRMs, ERPs, and databases, platforms like Stacksync provide the stable foundation for all data-driven initiatives—from analytics to automation.
PostgreSQL CDC enables real-time integration across increasingly specialized and distributed systems, helping organizations reduce technical debt while establishing sturdy foundations for scale. Rather than having systems constantly poll tables or use triggers for change detection, CDC provides consistent patterns for detecting changes independently without additional database burden.
Stacksync represents the definitive solution for PostgreSQL CDC, delivering true bi-directional synchronization with enterprise-grade security, no-code configuration, and proven performance across 200+ connectors. Unlike traditional CDC tools focusing primarily on analytics pipelines, Stacksync addresses operational data consistency requirements where real-time accuracy directly impacts business operations, enabling organizations to eliminate data silos, reduce manual reconciliation, and focus engineering resources on competitive differentiation rather than integration maintenance.
