Postgres ETL tools compared: the best ETL tools for PostgreSQL data integration and replication
Eleven ways to move data into or out of PostgreSQL, lined up by how each one runs, what it connects to, how it bills and who it is wrong for. Postgres is unusual in this category because it ships serious replication of its own, so the first question is not which vendor to buy but whether you need one at all. Below: the six native paths, the logical replication restrictions straight from the PostgreSQL documentation, then the tools that wrap them.
Field mapping auto-plugged · tap a port to rewire
Plug a source port into
Transform on this cable
JSON in
JSON out
5 sample records ready
PostgreSQL 18 documentation read 16 August 2026 · Last updated August 2026
Which Postgres ETL tool should you use?
Start with the direction. If you are copying Postgres to another Postgres, use built-in logical replication and buy nothing. If you are loading Postgres into Snowflake or BigQuery and want no pipeline maintenance, Fivetran is the default and you accept a metered bill. If you want the same job open source and self-hosted, Airbyte has the catalog and Debezium has the change stream. If both ends are in AWS, DMS or Aurora zero-ETL will be cheaper than anything you bolt on. And if the requirement is a few dependable scheduled syncs between Postgres and the SaaS tools your business runs on, with field mapping an analyst can edit, Adapters is flat at $49 to $399 a month.
The mistake worth avoiding early: treating "Postgres ETL" as one category. Loading Postgres from Stripe, replicating Postgres into a warehouse, and syncing two Postgres databases are three different problems with three different shortlists, and a tool that is excellent at one is frequently useless at another. For the broader market outside Postgres specifically, start with best data integration tools.
PostgreSQL ETL tools compared
Billing units rather than price tags, because almost everything in this category is volume-metered and quote-driven. The one flat published figure below is ours. Verify current numbers with each vendor before you sign anything.
| Tool | Where it runs | Approach | Connects to | Pricing model | Best for |
|---|---|---|---|---|---|
| PostgreSQL logical replication | Inside Postgres itself, publisher and subscriber | Native row-level streaming replication, table by table | Postgres to Postgres only, across major versions | Free, part of PostgreSQL since version 10 | Keeping a second Postgres current, or a version upgrade with minimal downtime |
| Debezium | Self-hosted on Kafka Connect, or embedded | Log-based change data capture from the WAL | Postgres, MySQL, SQL Server, Oracle, MongoDB into Kafka | Open source, you pay for Kafka and the people running it | Streaming architectures that already have Kafka and engineers to staff it |
| Fivetran | Managed cloud | Automated ELT with Postgres CDC and schema drift handling | The largest managed connector catalog, Postgres as source or destination | Monthly active rows for connections, model runs for transformations | Teams that want zero pipeline maintenance and will pay a metered bill for it |
| Airbyte | Open source self-hosted, or Airbyte Cloud | ELT with a Postgres CDC source built on logical decoding | Very large catalog, community and certified connectors | Free self-hosted, or Cloud billed by credits tied to rows and volume | Engineering teams that want catalog breadth and control over where it runs |
| AWS DMS | Replication instances inside AWS | Full load plus ongoing change replication | Postgres, and most commercial databases, to AWS targets and others | Per replication instance hour plus storage | Migrations and replication where both ends already live in AWS |
| Aurora zero-ETL | Fully managed by AWS, no pipeline to operate | Near real-time managed replication, no ETL job at all | Aurora PostgreSQL to Redshift or a SageMaker AI lakehouse | Aurora and Redshift consumption, no separate pipeline charge | Aurora shops whose analytics target is already Redshift |
| pgloader | A command line binary you run yourself | One-shot migration and load, with type casting rules | MySQL, SQL Server, SQLite, CSV and fixed-width files into Postgres | Open source, free | A single migration into Postgres, not an ongoing scheduled pipeline |
| Estuary | Managed cloud or private deployment | Streaming CDC with real-time and batch destinations | Databases and SaaS, streaming-first, Postgres CDC included | Usage based, by connector and data volume | Sub-minute freshness out of Postgres without running Kafka yourself |
| Hevo Data | Managed cloud | No-code ELT with in-flight transformations | SaaS applications and databases, Postgres both directions | Tiered by events loaded per month | Analytics teams without a data engineer on staff |
| Talend / Stitch | Managed cloud, now part of Qlik | Simple ELT built on the Singer standard | A steady mid-sized catalog with Postgres on both ends | Tiered by rows replicated per month | Straightforward replication where simplicity beats features |
| Adapters | Managed cloud, scheduled syncs | Visual field mapping, no-code transforms, retries and per-record logs | Postgres with Stripe, Shopify, Salesforce, HubSpot, NetSuite, QuickBooks, Xero and the warehouses | Flat monthly, $49 to $399, no row meter | A handful of dependable syncs on a bill that does not move with volume |
Notice that the first two rows are free. Postgres is one of the few sources where the native option is genuinely competitive, and a fair number of teams buy a platform to do something CREATE PUBLICATION already does. Buy a vendor for the SaaS connectors, the mapping UI and the error handling, not for moving rows between two databases that can already talk to each other.
The six native ways to move data in and out of Postgres
Every managed tool in the comparison above is ultimately driving one of these. Knowing which one your vendor uses tells you more about latency, cost and failure modes than any feature list will.
| Method | Shape | Latency | Filtering | Use it for | The catch |
|---|---|---|---|---|---|
| Logical replication | Row-level changes, publisher to subscriber | Continuous, near real-time | Per table, per schema, or per row with a WHERE clause | A second readable Postgres, or a major version upgrade | No DDL, no sequences, no large objects. See the restrictions table below |
| Physical streaming replication | Byte-for-byte WAL shipping of the whole cluster | Continuous, lowest lag | None. It is all or nothing | High availability standbys and read replicas | Replica is read-only and must match the major version exactly |
| Logical decoding plus a plugin | A change stream you consume yourself | Continuous | Whatever your consumer implements | Feeding Kafka, a warehouse, or a search index via Debezium | A paused consumer stops advancing the slot and the WAL grows until the disk fills |
| COPY | Bulk load or unload of a table to a file or stream | Batch, as fast as the disk | Any query you can write | Loading files, or exporting a table for a warehouse | A snapshot in time. It has no idea what changed since the last run |
| pg_dump and pg_restore | A logical backup of schema, data, or both | Batch, one-off | Per table or per schema | Migrations, and seeding the initial schema before logical replication | Not a pipeline. Nobody should be running this on a cron for analytics |
| postgres_fdw | A foreign table you query live | On demand, at query time | Any query, pushed down where the planner can | Joining another Postgres without copying anything | Every query hits the remote database, so it is a load problem not a storage one |
The replication slot row is the one that causes real incidents. A slot that nobody is consuming does not advance, so Postgres retains WAL on the publisher indefinitely and the disk fills. Pausing a connector, or deleting one without dropping its slot, is enough to do it. The step-by-step version of all of this is in our guide to Postgres logical replication, and change data capture tools compares the log-based options against watermark polling.
What Postgres logical replication does not replicate
This is the table that decides whether native replication is enough for you, and it is the section most comparisons of this category leave out entirely. Read from the PostgreSQL 18 documentation on 16 August 2026. None of these are bugs. They are documented restrictions, and every one of them has surprised somebody in production.
| Object or operation | Replicated | What that means for you |
|---|---|---|
| Schema and DDL commands | No | Copy the initial schema by hand with pg_dump --schema-only, then keep later changes in sync yourself. Apply additive changes to the subscriber first and you avoid most of the intermittent errors. |
| Sequence data | No | Values in serial and identity columns arrive as ordinary table data, but the sequence object itself still shows its start value on the subscriber. Harmless for a read replica, a genuine outage if you ever fail over without resetting them. |
| Large objects | No | There is no workaround other than storing the data in normal tables instead. Worth checking before you design around lo_import. |
| Views, materialized views, foreign tables | No | Replication supports tables only, including partitioned tables. Attempting to publish any other kind of relation raises an error rather than silently skipping it. |
| TRUNCATE | Yes, with a catch | The subscriber truncates the same group of tables, including anything collected by CASCADE, minus tables outside the subscription. If a truncated table has a foreign key to a table you did not subscribe, the apply fails. |
| Partitioned tables | Yes, with a catch | By default changes originate from the leaf partitions, so matching partitions must exist on the subscriber. Set publish_via_partition_root on the publication to replicate through the root table instead. |
| UPDATE and DELETE with REPLICA IDENTITY FULL | Conditionally | If the table has columns of a type with no default B-tree or hash operator class, such as point or box, updates and deletes cannot be applied. Give the table a primary key or an explicit replica identity and the problem goes away. |
The sequence row is the one that bites hardest, because nothing looks wrong until the day you promote the subscriber and every insert collides with an existing id. If a switchover is anywhere in your plan, advance the sequences from the publisher as part of the runbook, not as a step somebody remembers.
The Postgres replication numbers worth knowing
Six dated facts, each read from a primary source on 16 August 2026. These are the settings and limits that decide whether a Postgres pipeline is boring or a recurring incident.
logical
The wal_level you need
Logical replication requires wal_level = logical on the publisher, which needs a restart. Everything else in a basic setup works on defaults.
1 per sub
Replication slots to budget
max_replication_slots must cover at least the number of subscriptions, plus reserve for table synchronization. max_wal_senders should match it, plus any physical replicas.
0
Sequences replicated
Sequence data does not replicate. If you plan a switchover to the subscriber, you have to advance them from the publisher first.
PG 10
When native logical replication landed
Built-in publish and subscribe has shipped since PostgreSQL 10. Before that, this job needed an extension such as pglogical or Slony.
Aug 2026
Current supported branches
PostgreSQL 18.6, 17.11, 16.15, 15.19 and 14.24 were released on 13 August 2026. Version 13 and earlier are out of support and no longer get fixes.
Aurora only
The reach of zero-ETL
Amazon's zero-ETL integration needs an Aurora PostgreSQL source and targets Redshift or a SageMaker AI lakehouse. Plain RDS Postgres to Snowflake is not in scope.
How a Postgres pipeline should be built, step by step
Five decisions that decide whether the pipeline runs quietly for two years or turns into somebody's Tuesday. The tool you pick affects how much of this it does for you, but none of them do all five.
Step 01 / Direction
Decide which way the data is actually moving
Postgres as a source feeding a warehouse is a different purchase from Postgres as a destination collecting SaaS data, and both are different from Postgres to Postgres. Teams buy the wrong tool because the category name hides three jobs. Write the direction down before you look at a single vendor page.
Step 02 / Incremental
Pick the incremental strategy before the tool
Full reload, watermark on updated_at, or log-based capture from the WAL. This one decision drives your cost, your freshness and whether deletes are ever noticed. A watermark query cannot see a deleted row, so if hard deletes matter you need change data capture or a periodic reconciliation pass.
Step 03 / Types
Settle the type mapping while it is still cheap
Postgres numeric has no natural home in every target. Money landing as a float is the classic silent corruption. timestamptz must keep its zone, and jsonb should land as native JSON rather than a string you re-parse forever. Ask any vendor to show you the CREATE TABLE it generates before you sign.
Step 04 / Identity
Give every replicated table a real primary key
Without a primary key or an explicit replica identity, logical replication cannot apply updates and deletes, and REPLICA IDENTITY FULL fails outright on columns of types like point or box. Warehouse loaders hit the same wall from the other direction, because they need a key to merge on.
Step 05 / Failure
Decide what happens when forty rows fail
Partial failure is the normal case. You want a per-record error log with the source values attached, a retry policy you did not have to write, and an alert that reaches a human. A pipeline that is silent when it half-works is worse than one that stops.
Step three is where most of the silent damage happens, and the data mapping best practices guide covers the type and naming decisions in detail. If you have not settled the load order, ETL versus ELT explains why landing raw and transforming in the warehouse became the default.
What teams actually move with Postgres
Postgres into the warehouse
Application tables landing in Snowflake or BigQuery so analysts can join them to finance and marketing data without querying production.
SaaS data into Postgres
Stripe charges, Shopify orders or Salesforce opportunities replicated into your own database so your product can query them with a join instead of an API call.
Postgres to Postgres
A reporting replica, a regional copy, or a major version upgrade run through logical replication so the cutover is measured in seconds rather than a maintenance window.
Migrating onto Postgres
Moving off MySQL or SQL Server, where the work is type conversion and identifier case rather than throughput. This is a project with an end date, not a pipeline.
Feeding a search index or cache
Change capture pushing updated rows into Elasticsearch or a read model, so search results stop lagging the database by an hour.
Warehouse results back to Postgres
Modeled tables and scores pushed from Snowflake back into the application database that actually serves them to users.
That last one, pushing modeled results out of the warehouse and back into an application database or a SaaS tool, is a separate purchase with separate failure modes. It is covered on reverse ETL tools.
When another Postgres ETL tool is the right answer
We would rather you buy the correct thing than churn in month three. Five situations where Adapters is not what you want.
You are copying Postgres to Postgres
Buy nothing. Native logical replication does this well, for free, across major versions, and it is already installed. Read the restrictions table above, confirm none of them apply to you, and write two SQL statements.
You need hundreds of connectors
If the requirement list runs to forty SaaS sources including several obscure ones, buy catalog breadth. Fivetran and Airbyte have spent years on that surface area and we have not.
Both ends already live in AWS
Aurora Postgres into Redshift is what zero-ETL was built for, and DMS covers the messier migrations. Neither needs a third-party bill attached, and both will beat us on price inside that boundary.
You need sub-second change streams
We run on a schedule you choose. If rows have to reach a search index or an event bus within seconds, that is Debezium on Kafka, or a streaming vendor such as Estuary.
Your volume genuinely is enormous
Flat pricing is a bet that most teams move a predictable amount of data. If you are replicating billions of rows a month, negotiate a volume contract with a metered vendor and you will likely beat our list price. Our advantage is the middle, where a metered bill is unpredictable and a hand-built loader is a permanent staffing cost nobody put on the budget.
Four questions to ask on every Postgres ETL demo
Question 01
Does it use a replication slot, and who drops it?
Slots are the number one cause of a full Postgres disk. Ask what happens to the slot when you pause the connector, and whether deleting the connector removes it.
Question 02
How does it handle a schema change?
A new column upstream should not break the load. Ask whether it adds the column, ignores it, or fails the run, and whether anyone gets told either way.
Question 03
What types does it choose on the other side?
Ask to see the generated CREATE TABLE. numeric collapsing to a float and timestamptz losing its zone are the two that get discovered months later, in a finance report.
Question 04
What does a full historical resync cost?
On a monthly-active-rows meter, one accidental resync of a large table can cost more than the rest of the year. Get the answer in writing before you sign.
Postgres connectors, source by source
Each page below covers one lane into or out of PostgreSQL: the field mappings, the type conversions that bite, the incremental strategy and the questions people ask about that specific pair.
Questions buyers ask about Postgres ETL tools
- What are Postgres ETL tools?
- Postgres ETL tools move data into or out of a PostgreSQL database and keep it current on a schedule. The category covers three different jobs that get confused with each other: loading Postgres from SaaS APIs, replicating Postgres into a warehouse for analytics, and keeping two Postgres databases in sync. A tool that is excellent at one of those is often mediocre at the others.
- What is the best ETL tool for PostgreSQL?
- There is no single best one, only a best fit for the direction you are moving data. Fivetran and Airbyte win on managed connector breadth into a warehouse. Debezium wins on open source log-based change capture. AWS DMS wins inside AWS. pgloader wins on one-off migrations into Postgres. Adapters wins when you need a handful of dependable scheduled syncs on a flat monthly bill.
- Does PostgreSQL have built-in ETL?
- Not ETL, but it has strong built-in replication. Logical replication, available since PostgreSQL 10, streams table changes from a publisher to a subscriber using CREATE PUBLICATION and CREATE SUBSCRIPTION. It moves rows, not schemas, and it has documented restrictions covered below. COPY and postgres_fdw handle bulk loading and cross-database queries. None of them transform data or talk to a SaaS API.
- What are the limitations of Postgres logical replication?
- PostgreSQL documents them explicitly. Schema and DDL commands are not replicated. Sequence data is not replicated, so identity columns show the start value on the subscriber. Large objects are not replicated at all, with no workaround. Only tables replicate, so views, materialized views and foreign tables raise an error. The full list is in the table below.
- What is the difference between logical and physical replication in Postgres?
- Physical replication copies the write-ahead log byte for byte, producing an exact binary replica of the whole cluster that you cannot write to and cannot filter. Logical replication decodes the WAL into row-level changes, so you can pick individual tables, replicate between different major versions, and keep the subscriber writable. Analytics pipelines almost always need logical.
- How do I set up logical replication in Postgres?
- Set wal_level = logical in postgresql.conf and restart, then allow the replication user in pg_hba.conf. On the source run CREATE PUBLICATION mypub FOR TABLE users, departments. On the target run CREATE SUBSCRIPTION mysub CONNECTION 'dbname=foo host=bar user=repuser' PUBLICATION mypub. That copies the initial table contents, then streams incremental changes.
- How do I move data from Postgres to a data warehouse?
- Three strategies, in rising order of complexity. Full reload works while tables are small and is the one people abandon too late. Watermark polling on an updated_at column is the sensible middle and covers most reporting needs. Log-based change data capture reads the WAL and catches deletes, which a watermark query cannot see at all.
- What is zero-ETL for Postgres?
- It is Amazon's managed replication from an Aurora PostgreSQL cluster into Amazon Redshift or a SageMaker AI lakehouse, with no pipeline for you to run. It is genuinely near real-time and fully managed, but it is one vendor path: the source must be Aurora and the target must be an AWS analytics service. It does nothing for Snowflake, BigQuery or a SaaS application.
- How much do PostgreSQL ETL tools cost?
- Most of this category bills by volume rather than by seat. Fivetran meters monthly active rows, Airbyte Cloud meters credits tied to rows and gigabytes, and AWS DMS bills replication instance hours. That makes the bill hard to forecast before you run it, and a full historical resync is where teams get surprised. Adapters is flat at $49 to $399 a month.
For the categories around this one, see Snowflake ETL tools and BigQuery ETL tools if Postgres is the source and a warehouse is the destination, iPaaS platforms when the job is keeping business applications in agreement rather than filling a database, and ETL software for how our own pipeline works end to end.
Sync Postgres with the tools your business runs on, at a flat monthly price
Map the columns once, pick a schedule, and let it run with retries, alerts and per-record logs. From $49 a month, with no row meter and no credits to forecast.
No credit card required.