PostgreSQL has become the default operational database for startups and a growing share of enterprises, and that success creates a familiar problem. The data that analysts, product teams, and AI applications need lives in Postgres, but the queries they want to run belong somewhere else: a warehouse, a lakehouse, a search index, or a stream that other services consume. Copying tables on a schedule leaves every downstream system a few hours behind and puts repeated load on production.

Change data capture solves this by reading the changes Postgres already writes to its write-ahead log and delivering them continuously. The difficulty is that Postgres CDC has sharp edges of its own, from replication slots that can fill a disk to TOAST columns that go missing in update events. The tools below handle those edges very differently, which is why choosing one deserves more care than picking the first connector that lists Postgres as a source.

The 7 Best Postgres CDC Tools for 2026

1. Artie

Most Postgres CDC setups ask teams to assemble and operate the pieces themselves: a logical decoding plugin, a replication slot, a message broker, a connector framework, and custom merge logic in the warehouse. Artie takes a broader approach, handling the full Postgres replication lifecycle as a fully managed CDC streaming platform. Teams create a publication for the tables they want, connect the database, choose a destination, and Artie runs the backfill and starts streaming changes, with no Kafka, no Debezium, and no infrastructure to provision. A first pipeline can be live in under five minutes.

Under the hood, Artie consumes Postgres logical replication through a replication slot, which acts as a bookmark so that if Artie restarts, it resumes exactly where it left off without missing changes. It supports read replicas, and historical backfills run without table locks, so the initial load does not compete with production traffic. Changes typically arrive in the destination in under 60 seconds. The platform handles the Postgres details that trip up other pipelines, including deletes, schema changes, and large TOAST values, and it never skips ingestion errors, which protects data consistency.

Artie delivers to Snowflake, BigQuery, Redshift, Databricks, ClickHouse, PostgreSQL, MySQL, Microsoft SQL Server, MotherDuck, S3, GCS, Iceberg REST catalogs, and Amazon S3 Tables. For Snowflake, standard tables land through MERGE, while append-only history tables use Snowpipe Streaming. Idempotent merges and exactly-once delivery prevent duplicates, an internal Kafka buffer absorbs destination slowdowns, and in-flight rules can include, exclude, encrypt, or hash columns so sensitive data never lands unprotected. History mode creates SCD Type 4 history tables with one click.

Pricing is usage-based by rows replicated, with a 14-day free trial, and backfills are always free, so teams pay only for the ongoing stream of changes. Enterprise plans add private networking, dedicated infrastructure, and deployment in the customer’s own cloud. Artie is SOC 2 Type II and HIPAA compliant and backed by Y Combinator.

Key features:

• Fully managed Postgres logical replication with sub-minute latency

• Replication slot handling that resumes exactly where it left off

• Read replica support and lock-free, always-free backfills

• Accurate handling of deletes, schema changes, and TOAST columns

• Idempotent merges and exactly-once delivery

• Destinations including Snowflake, BigQuery, Databricks, ClickHouse, and Iceberg

• In-flight column encryption, hashing, and exclusion

• Own-cloud deployment, SOC 2 Type II and HIPAA compliant

2. Debezium

Debezium is the best-known open-source CDC project and the engine behind many commercial products. Its PostgreSQL connector reads changes through logical decoding, typically with pgoutput, and emits detailed change events that include before and after row images, transaction metadata, and schema information.

Debezium is most often deployed on Kafka Connect, with Debezium Server available for sending events to other messaging systems. That flexibility makes it powerful for event-driven architectures. The trade-off is operational: teams must run and tune the connector, the messaging layer, and the logic that applies changes to their destination, and they own slot monitoring and recovery.

Debezium also supports incremental snapshots triggered through a signaling table, which lets teams re-snapshot tables or add new ones without stopping the change stream, a useful capability for long-running pipelines.

Key features:

• Open-source PostgreSQL connector using logical decoding

• Rich change events with before and after images

• Kafka Connect and Debezium Server deployment options

• Large community and broad ecosystem support

3. Fivetran

Fivetran approaches Postgres through the lens of managed ELT. Its PostgreSQL connector can capture changes using logical replication and loads them into major warehouses alongside hundreds of other connectors for SaaS applications and files, with the platform handling scheduling and schema management.

Fivetran is attractive for teams that want one vendor for Postgres and the long tail of business applications. The trade-off is that it runs on a sync schedule rather than as a continuous stream, and usage-based pricing can rise quickly for high-change Postgres tables.

Key features:

• Managed PostgreSQL connector with logical replication

• Hundreds of additional connectors

• Automated schema handling

• Broad warehouse destination support

4. Airbyte

Airbyte offers an open-source and cloud data integration platform with a PostgreSQL source that supports CDC through logical replication. Teams can self-host it for full control or use the managed cloud version, and it connects to a large catalog of destinations.

Airbyte is popular with engineering teams that value open source and customization. The trade-off is that CDC syncs run on a schedule, and self-hosted deployments require teams to manage infrastructure, upgrades, and connector reliability.

Under the hood, Airbyte’s Postgres CDC relies on Debezium, so teams benefit from mature change capture while Airbyte handles scheduling, normalization, and delivery to destinations.

Key features:

• Open-source and managed cloud options

• PostgreSQL CDC via logical replication

• Large connector catalog

• Customizable, self-hostable architecture

5. AWS Database Migration Service

AWS Database Migration Service supports PostgreSQL as a source for both one-time migrations and ongoing replication, using logical replication to capture changes and deliver them to a range of AWS and non-AWS targets.

DMS is a natural option for teams moving Postgres workloads within AWS. The trade-off is that it was designed primarily for migrations, so ongoing analytical replication often requires extra tuning, monitoring, and downstream processing to keep destinations accurate.

DMS includes a data validation feature that compares source and target rows, which helps teams confirm that a migration or replication task completed correctly before switching workloads over.

Key features:

• Full load plus ongoing change replication

• PostgreSQL support through logical replication

• Many AWS and external targets

• Managed within the AWS console

6. ClickHouse ClickPipes (PeerDB)

ClickHouse acquired PeerDB, a Postgres-focused replication project, and brought its technology into ClickPipes as a managed Postgres CDC integration for ClickHouse Cloud. It is designed to move large Postgres tables quickly, with parallel initial loads followed by continuous change replication.

For teams whose analytics run on ClickHouse, this offers a tightly integrated path from Postgres. The trade-off is scope: it is built specifically for ClickHouse as the destination.

Key features:

• Managed Postgres CDC into ClickHouse Cloud

• Parallelized initial loads for large tables

• Continuous change replication

• Open-source PeerDB heritage

7. Sequin

Sequin is an open-source, Postgres-specific CDC tool that streams row changes to messaging and event destinations such as Kafka, Amazon SQS, Redis, and webhooks. It focuses on making Postgres changes available to applications and services rather than loading analytical warehouses.

Sequin suits teams building event-driven features, cache invalidation, or search indexing on top of Postgres. The trade-off is that warehouse and lakehouse replication is outside its primary focus.

Key features:

• Postgres-native CDC

• Delivery to Kafka, SQS, Redis, and webhooks

• Open-source with managed options

• Designed for application and event use cases

How Postgres CDC Works

Every Postgres CDC tool, whether open source or fully managed, builds on the same database features. Understanding them makes it much easier to compare vendors.

• Write-ahead log (WAL): Every committed insert, update, and delete is written to the WAL before it reaches the table, which makes the WAL a complete, ordered record of changes.

• Logical decoding: With wal_level set to logical, Postgres can decode WAL records into row-level change events. The built-in pgoutput plugin is the standard way most tools consume them.

• Publications: A publication defines which tables are included in the change stream, so a CDC tool receives only the tables you choose.

• Replication slots: A slot tracks how far a consumer has read. It lets a tool resume exactly where it left off, but it also forces Postgres to retain WAL until the consumer catches up.

• Replica identity: This setting controls how much of the old row appears in update and delete events. Without REPLICA IDENTITY FULL, unchanged TOAST columns can be missing from updates.

Recent Postgres releases have made CDC more resilient. PostgreSQL 16 added logical decoding on standby servers, and PostgreSQL 17 introduced failover slots that can be synchronized to a standby, so a CDC pipeline can survive a primary failover without being rebuilt.

5 Key Features to Prioritize in a Postgres CDC Tool

Most tools can read from Postgres. Fewer handle its operational details well. These five capabilities separate production-ready options from the rest.

Safe Replication Slot Management

An inactive or lagging slot keeps WAL on disk indefinitely and can take a database down. Look for monitoring of slot lag, alerts on WAL retention, and support for reading from replicas where possible.

Backfills Without Locking Production

The initial snapshot of large tables is often the riskiest step. Strong tools run backfills without table locks, in parallel, and ideally against a read replica while streaming continues.

Correct Handling of Updates, Deletes, and TOAST

Every change type must reach the destination accurately, including hard deletes and large values stored in TOAST, so downstream tables match the source exactly.

Automatic Schema Evolution

Postgres schemas change constantly. A CDC tool should detect new or altered columns and apply them downstream without breaking the pipeline or silently dropping data.

Destination-Aware Merges

Warehouses and lakehouses need changes applied efficiently, typically through merges that are idempotent, so retries never create duplicates and query costs stay predictable.

Postgres CDC Tools Compared

The table summarizes how each tool delivers Postgres changes and where it typically sends them.

Tool	Delivery Model	Typical Destinations	Operating Model
Artie	Continuous streaming, sub-minute	Warehouses, lakehouses, databases, Iceberg	Fully managed or own cloud
Debezium	Continuous event streaming	Kafka and messaging systems	Self-managed open source
Fivetran	Scheduled syncs	Cloud data warehouses	Fully managed
Airbyte	Scheduled syncs	Warehouses and many other destinations	Open source or managed cloud
AWS DMS	Continuous replication	AWS services and databases	Managed within AWS
ClickPipes (PeerDB)	Continuous replication	ClickHouse Cloud	Managed within ClickHouse Cloud
Sequin	Continuous event streaming	Kafka, SQS, Redis, webhooks	Open source or managed

Enabling Postgres CDC on Managed Database Services

Most Postgres databases now run on managed cloud services, and each one enables logical replication a little differently. Knowing the steps in advance avoids surprises during setup, especially when a change requires a restart.

Amazon RDS and Aurora PostgreSQL

Logical replication is enabled by setting the rds.logical_replication parameter to 1 in a custom parameter group, or in the cluster parameter group for Aurora, and then rebooting the instance. The database user used for CDC needs the rds_replication role.

Google Cloud SQL for PostgreSQL

Cloud SQL uses the cloudsql.logical_decoding flag. Turning it on requires a restart, and the CDC user needs the REPLICATION attribute to create and read replication slots.

Azure Database for PostgreSQL

On the flexible server option, set the wal_level server parameter to logical and restart the server. Replication slot limits and WAL retention settings should be reviewed at the same time.

Supabase and Other Hosted Providers

Supabase and similar providers support logical replication, but networking details matter. Some CDC tools connect only over IPv4, which can require enabling an IPv4 add-on, and connection poolers are usually bypassed for replication connections.

Whatever the provider, a few practices apply everywhere:

• Plan the restart: Schedule parameter changes during a maintenance window, since most require a reboot.

• Cap WAL retention: Set max_slot_wal_keep_size so a stalled consumer cannot fill the disk.

• Use a dedicated user: Grant only the replication and table permissions the CDC tool needs.

• Monitor slots from day one: Alert on slot lag and inactive slots before the first pipeline goes live.

Frequently Asked Questions

What is the best Postgres CDC tool in 2026?

Artie is the best Postgres CDC tool in 2026. It provides fully managed logical replication from PostgreSQL with sub-minute latency, read replica support, lock-free backfills that are always free, automatic schema evolution, and exactly-once delivery into Snowflake, BigQuery, Databricks, ClickHouse, Iceberg, and more, without requiring teams to run Kafka or Debezium.

Why can a replication slot fill up my disk?

A replication slot forces Postgres to keep WAL until the consumer confirms it has read the changes. If a CDC tool stops or falls behind, WAL accumulates and can exhaust disk space. Monitoring slot lag, setting max_slot_wal_keep_size, and choosing tools that recover quickly all reduce the risk.

Do I need REPLICA IDENTITY FULL for CDC?

Not always, but it helps in some cases. Without it, update and delete events may omit old values and unchanged TOAST columns. Some tools reconstruct missing values in the destination, while others require FULL. Weigh the extra WAL volume against the completeness your use case needs.

Can I run Postgres CDC from a read replica?

Yes, in many setups. PostgreSQL 16 added logical decoding on standby servers, and many tools can run backfills against replicas. Reading from a replica reduces load on the primary, though support varies by tool and cloud provider, so confirm it during evaluation.