Skip to main content

Postgres to Redshift: CDC via Apache Iceberg

Postgres to Redshift usually means a Database Migration Service (DMS) task, an Aurora zero-ETL (extract, transform, load) integration, or a nightly COPY, and tables that only Redshift can read. This guide takes a different route.

OLake Go reads the Postgres write-ahead log (WAL) and commits the changes as Apache Iceberg tables into the AWS Glue Data Catalog. The files stay in your own S3 bucket. Redshift maps that Glue database onto an external schema and queries the tables in place.

Nothing else is in the path: no DMS, no Kafka, no load job. Athena and DuckDB read the same tables through the same catalog.

By Shubham Satish BaldavaUpdated October 30, 2026

Key takeaways

  • OLake Go replicates Postgres into Iceberg tables in the Glue Data Catalog: a full historical snapshot first, then change data capture (CDC) from the WAL, with no DMS or Kafka in between.
  • Redshift reads the Glue database through one CREATE EXTERNAL SCHEMA statement, with no refresh. Every change a sync commits is visible on the next query, on Serverless and RA3 alike.
  • The same tables, written once by OLake Go, are also queryable from Athena and DuckDB. In the run on this page all three engines returned identical rows.

The short answer. To replicate Postgres to Redshift with change data capture (CDC), run OLake Go with a Postgres replication slot as the source. Point it at the AWS Glue Data Catalog as the Iceberg destination.

Set the upsert type to Positional Deletes, the format Redshift reads. The first sync copies each table in full (the historical snapshot); every later sync replays inserts, updates and deletes from the WAL. Redshift, Athena and DuckDB then query the same Iceberg tables, with no second copy.

How Postgres CDC reaches Redshift here​

Two things happen in order, and OLake Go does both on its own. The first sync records the current WAL position through the replication slot. Then it reads each table in parallel chunks and writes Parquet data files: that is the historical load.

Every later sync opens the slot at the saved position and replays the inserts, updates and deletes since. Nothing written during the snapshot is lost, and there is no separate backfill job.

Each sync ends with one commit to Glue. OLake Go uploads the data and delete files to the bucket, then updates the table's metadata pointer in the Glue database. Redshift, Athena and DuckDB see the new snapshot on their next query; none of them keeps a copy.

One thing to keep an eye on: the replication slot pins WAL on the Postgres server until OLake Go has read it. A job that is paused for days makes the WAL grow by everything written since. Keep the job running on a schedule, and alert on pg_replication_slots lag if disk is tight.

What you need​

  • Postgres 12 or later with wal_level = logical and a user that can read the WAL. The tables on this page ran on Postgres 16. Hosted Postgres on Amazon Relational Database Service (RDS), Aurora, Google Cloud SQL, Azure, Neon or Supabase works the same way; step 1 links the setup guides.
  • An AWS account with an S3 bucket for the files and an IAM user or role for OLake Go with Glue and S3 permissions. The run on this page used a bucket and a scoped IAM user in ap-southeast-1.
  • Amazon Redshift, Serverless or an RA3 cluster, with an IAM role attached that can read Glue and the bucket. The run on this page used a Serverless workgroup at 8 RPU.
  • OLake UI from the Docker Compose install. The run on this page used OLake Go v1.1.0.
  • Docker, and about 40 minutes.

Step 1: Prepare Postgres for CDC​

Most readers point OLake Go at tables they already have. If you want to follow along with the exact data from this page, create these two tables first:

The two sample tables used on this page
Sample schema
CREATE TABLE customers (
  id SERIAL PRIMARY KEY,
  name VARCHAR(100), email VARCHAR(200), city VARCHAR(100), tier VARCHAR(20),
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW());

CREATE TABLE orders (
  id SERIAL PRIMARY KEY,
  customer_id INT REFERENCES customers(id), product VARCHAR(100), amount NUMERIC(10,2), status VARCHAR(20),
  created_at TIMESTAMPTZ DEFAULT NOW(),
  updated_at TIMESTAMPTZ DEFAULT NOW());

-- Then insert whatever you like. This page started with 8 customers and 40 orders.

OLake Go reads changes through logical replication with the built-in pgoutput plugin (Postgres 10 and later). Four things must be in place on the database before the first sync.

SettingValueWhy it matters
wal_levellogicalWithout it Postgres writes no row images to the WAL (write-ahead log), so there is nothing to replicate. Changing it needs a restart.
Replication roleLOGIN REPLICATION plus SELECT on the tablesThe first snapshot reads the tables; the change stream opens a replication connection.
Publicationone per OLake Go job, covering the synced tablesTells Postgres which tables' changes to publish.
Replication slotone per OLake Go job, plugin pgoutputKeeps WAL segments until OLake Go has read them.
On the source database
-- 1. Logical replication. wal_level needs a restart (a parameter-group change on managed Postgres).
ALTER SYSTEM SET wal_level = 'logical';
ALTER SYSTEM SET max_replication_slots = 10;
ALTER SYSTEM SET max_wal_senders = 10;
ALTER SYSTEM SET wal_sender_timeout = 0;   -- so a long first snapshot is not cut off

-- 2. A role for OLake Go
CREATE ROLE olake WITH LOGIN REPLICATION PASSWORD '<password>';
GRANT USAGE ON SCHEMA public TO olake;
GRANT SELECT ON ALL TABLES IN SCHEMA public TO olake;
ALTER DEFAULT PRIVILEGES IN SCHEMA public GRANT SELECT ON TABLES TO olake;

-- 3. A publication for the tables you sync, and a pgoutput slot
CREATE PUBLICATION olake_pub FOR TABLE customers, orders;
SELECT pg_create_logical_replication_slot('olake_slot', 'pgoutput');

Tables with a primary key need nothing more. A table without one needs ALTER TABLE <table> REPLICA IDENTITY FULL; so that its updates and deletes carry the old row.

Check the result before moving on:

sql
SHOW wal_level;                                        -- logical
SELECT slot_name, plugin, active FROM pg_replication_slots;
SELECT pubname, puballtables FROM pg_publication;

Managed Postgres sets wal_level through a parameter group or server setting rather than ALTER SYSTEM. Each flavour has its own guide:

Where Postgres runsGuide
Amazon RDSRDS Postgres CDC setup
Amazon AuroraAurora Postgres CDC setup
Azure Database for PostgreSQLAzure Postgres CDC setup
Google Cloud SQLCloud SQL Postgres CDC setup
Your own server or VMGeneric Postgres CDC setup
Docker, for a local testLocal Postgres with Docker Compose
One slot and one publication per job

Two OLake Go jobs sharing a replication slot read each other's changes and both lose data. Create a separate slot and publication for every job.

Step 2: Set up the Glue catalog​

Redshift reads Iceberg tables through a catalog. AWS Glue Data Catalog is the one this page uses: OLake Go creates a Glue database per job and registers each table there. The files sit in your S3 bucket.

Redshift, Athena, Amazon Elastic MapReduce (EMR) and DuckDB all read from the same catalog. Redshift reaches it through an external schema, with the IAM role attached to the workgroup or cluster.

CatalogOLake Go writesAmazon Redshift readsVerified on this page
AWS GlueYesYes: external schema from the Glue Data CatalogYes, end to end

AWS Glue Data Catalog is the default catalog in OLake Go. The tables live as Parquet and metadata files in an S3 bucket you own, and Glue holds the pointer to the current metadata file. Athena, Redshift, EMR, Spark and DuckDB read the same tables through Glue, which is why it is the catalog to pick when more than one AWS engine will read the data.

You need:

  • An S3 bucket for the table files, in the region where your engines run.
  • An IAM user or role for OLake Go with Glue and S3 permissions. The exact policy is on the AWS Glue catalog page.
  • No Glue database in advance: OLake Go creates one per job, named after the source database and schema (for example pg_to_snowflake_shop_public).

Glue has no built-in compaction for tables written by external engines, so plan on OLake Fusion or Spark for maintenance once the change volume grows.

The run on this page gave OLake Go a scoped IAM user with this inline policy. Redshift got a role with AmazonS3ReadOnlyAccess and read access to Glue:

IAM policy for the OLake Go user (replace the bucket, region and account)
{
  "Version": "2012-10-17",
  "Statement": [
    { "Effect": "Allow", "Action": ["s3:ListBucket", "s3:GetBucketLocation"], "Resource": "arn:aws:s3:::<bucket>" },
    { "Effect": "Allow", "Action": ["s3:GetObject", "s3:PutObject", "s3:DeleteObject", "s3:AbortMultipartUpload", "s3:ListMultipartUploadParts"], "Resource": "arn:aws:s3:::<bucket>/*" },
    { "Effect": "Allow",
      "Action": ["glue:GetDatabase", "glue:GetDatabases", "glue:CreateDatabase", "glue:GetTable", "glue:GetTables", "glue:CreateTable", "glue:UpdateTable", "glue:DeleteTable"],
      "Resource": ["arn:aws:glue:<region>:<account>:catalog", "arn:aws:glue:<region>:<account>:database/*", "arn:aws:glue:<region>:<account>:table/*/*"] }
  ]
}

Step 3: Connect Postgres to OLake Go​

  1. In the OLake UI, open Sources and click Create Source. Pick Postgres as the connector, give the source a name and keep the OLake Go version on the latest release.
  2. Under Change data capture (CDC), leave Enable CDC on and enter the Replication Slot and Publication you created in step 1. The default Initial Wait Time is fine.
  3. Under Connection details, fill in the host, port, database name, username and password. Leave Schemas empty to discover every non-system schema, or list the ones you want.
  4. Click Set up source. OLake Go tests the connection, then checks the four CDC prerequisites (wal_level, the replication privilege, the slot and the publication) and shows the current and recommended value for anything that fails.

Here is the source form from the run on this page. Postgres ran in another container, so the host is host.docker.internal; the user, slot and publication are the ones from step 1.

OLake UI Edit source form for Postgres on OLake Go v1.1.0 with Enable CDC on, replication slot olake_slot, publication olake_pub, host host.docker.internal, database shop, user olake and SSL disabled
The Postgres source in OLake UI: Enable CDC on, with the slot, publication and replication user from step 1.

Set up source runs the connection test and the CDC checks (wal_level, the slot, the publication) before anything is saved. When one fails, the dialog names the setting and its recommended value.

The full field reference, including SSL modes and SSH tunnels, is on the Postgres connector page.

Step 4: Configure the Iceberg destination​

  1. Open Destinations and click Create Destination.
  2. Pick Apache Iceberg as the connector and AWS Glue as the catalog type.
  3. Enter the S3 path, the AWS region and the access key pair (or leave the keys empty to use the instance role).
  4. Click Create.
ParameterSample ValueDescription
S3 Path
required		s3://<BUCKET_NAME>/S3 bucket path where Iceberg table data and metadata files will be stored.
AWS Region
required		ap-south-1AWS Region where your S3 bucket and Glue Data Catalog exist. Required so OLake Go calls the correct regional S3 and Glue endpoints.
AWS Access KeyXXXAWS access key ID for authentication. Optional if using IAM roles or instance profiles.

If using IAM:
Docker Compose: add the required IAM environment variables under x-envs in your compose file. See Service Environment Variables for setup details.
Kubernetes: set up pod IAM in Cloud Integration.
AWS Secret KeyXXXAWS secret access key for authentication. Optional if using IAM roles or instance profiles.

If using IAM:
Docker Compose: add the required IAM environment variables under x-envs in your compose file. See Service Environment Variables for setup details.
Kubernetes: set up pod IAM in Cloud Integration.
S3 Endpointhttps://glue.ap-south-1.amazonaws.comS3 API endpoint for writing Iceberg data files. Optional for AWS S3 but required for S3-compatible storage like MinIO.
Custom Glue Endpoint Configurationtrue/falseEnable custom Glue catalog configuration when Glue catalog settings differ from S3 (different credentials, region, or endpoint). Shows additional Glue configuration fields. Leave off when Glue and S3 use the same AWS setup.
Enable Arrow Writestrue/falseWrites data and delete files using Apache Arrow based writer and registers them in Iceberg.

The following fields appear in the UI after Custom Glue Endpoint Configuration is turned on. Configure Glue catalog connection settings here separately from the S3 fields above.

ParameterSample ValueDescription
Glue Catalog ID123456789012AWS account ID used as the Glue Data Catalog identifier.
Glue Access KeyXXXAccess key for authenticating Glue catalog requests. Required when Glue credentials differ from S3 credentials.
Glue Secret KeyXXXSecret key for authenticating Glue catalog requests. Required when Glue credentials differ from S3 credentials.
Glue Endpointhttps://glue.ap-south-1.amazonaws.comCustom endpoint URL for AWS Glue or a Glue-compatible catalog service.
Glue Regionap-south-1Region for the Glue catalog, if different from the S3 region. Falls back to AWS Region if not set.

Here is the destination form from the run on this page. It holds the bucket path OLake Go writes under, the region of the bucket and the Glue catalog, and the access key pair of the IAM user from step 2.

OLake UI Edit destination form for Apache Iceberg with catalog type AWS Glue, S3 path s3://olake-x2y-lab-672919669757-apse1/lakehouse/, region ap-southeast-1 and an access key pair
The Glue destination in OLake UI: the S3 path, the region and the keys of the scoped IAM user.

Step 5: Create the job and run the first sync​

Redshift is not in OLake Go's target engine picker yet. This job therefore lists Athena and DuckDB as target engines and sets the upsert type to Positional Deletes by hand. That is the format Redshift reads, and both listed engines read it too.

  1. Open Jobs and click Create Job. Give it a name and a schedule. Every Day at a fixed hour, or a cron expression, both work; the schedule is how often the change stream is drained, not how often a full copy runs.
  2. Select the source and the destination you created.
  3. Under Advanced Settings, set Target Query Engines to AWS Athena and DuckDB (Redshift is not in the list yet; it reads what Athena reads). OLake Go then resolves an Iceberg delete format every one of those engines can read, so you never pick one by hand.
  4. Click Next. OLake Go discovers the tables and opens the Streams page.
  5. Tick the tables to replicate. For each one, set the sync mode to Full Refresh + CDC and the ingestion mode to Upsert. Leave the Upsert Type as OLake Go resolved it (Positional Deletes in the run on this page).
  6. Click Create Job, then open the job's menu and choose Sync Now for the first run.

OLake Go offers every delete format Athena and DuckDB both read. The run on this page picked Positional Deletes from that list because Redshift reads Iceberg v2 tables with positional deletes and nothing else.

OLake UI Streams Selection page with the public schema, customers and orders selected, sync mode Full Refresh + CDC, ingestion mode Upsert and upsert type Positional Deletes on Iceberg format version V2
Each stream: Full Refresh + CDC, Upsert, and Positional Deletes as the upsert type. The Iceberg DB name at the top is editable.
OLake UI Job Logs and History for postgres_to_redshift showing two completed syncs, the first in 19 seconds and the second in 18 seconds
Job history: the 19 s historical snapshot, then the 18 s change-only sync.

On this page the first run copied 8 customers and 40 orders (48 records, 2.23 KB) and finished in 19 seconds, container start included. OLake Go names the Glue database after the job, the source database and the schema, so postgres_to_redshift on database shop produced postgres_to_redshift_shop_public. The Iceberg DB field at the top of the Streams page changes it before the first sync.

Step 6: Query the tables in Redshift​

Redshift reads the Glue Data Catalog through an external schema: one statement maps a Glue database onto a schema name in your Redshift database, and every Iceberg table OLake Go commits there is queryable at once, with no refresh. Redshift Serverless and RA3 clusters both do this; the IAM role attached to the namespace or cluster needs read access to Glue and to the bucket.

Redshift query editor v2
-- Once: map the Glue database OLake Go created (schema = <job name>_<source database>_<source schema>)
CREATE EXTERNAL SCHEMA lakehouse
FROM DATA CATALOG
DATABASE 'postgres_to_redshift_shop_public'
IAM_ROLE default;

-- Then query the Iceberg tables like any other schema
SELECT id, name, city, tier FROM lakehouse.customers ORDER BY id;
SELECT status, count(*) AS n, round(sum(amount), 2) AS total
FROM lakehouse.orders GROUP BY status ORDER BY status;

IAM_ROLE default uses the default role of the namespace (Serverless) or cluster; name a role ARN instead when you keep several. Redshift reads Iceberg v2 tables with positional deletes, which is why the job on this page uses that upsert type. The external schema is read-only: OLake Go is the writer, and INSERT or DELETE on these tables fails.

OLake Go adds four columns to every table: _olake_id (a hash of the primary key), _op_type, _olake_timestamp and _cdc_timestamp. _op_type is r for a row from the first snapshot, c for an insert, u for an update and d for a delete. Filter them out of dashboards, or use them to see when a row last changed.

Redshift query editor v2 in the AWS console running the customers query on the Serverless workgroup and returning 10 rows with id, name, city and tier
Redshift Serverless reading the customers table through the external schema after the change sync in query editor v2: 10 rows, one per key.

Step 7: Change rows in Postgres and sync again​

To see the change path work, the run on this page made 12 changes in Postgres after the first sync:

Changes made in Postgres between the two syncs
INSERT INTO customers (name, email, city, tier) VALUES
  ('Maya Patel', 'maya@example.com', 'Pune', 'silver'),
  ('Lucas Silva', 'lucas@example.com', 'São Paulo', 'bronze');
INSERT INTO orders (customer_id, product, amount, status) VALUES
  (9, 'Laptop', 1299.00, 'placed'), (9, 'Mouse', 25.50, 'placed'),
  (10, 'Monitor', 349.99, 'placed'), (3, 'Keyboard', 89.00, 'placed'), (7, 'Webcam', 79.90, 'placed');
UPDATE customers SET tier = 'gold', updated_at = NOW()      WHERE name = 'Liam Carter';
UPDATE customers SET city = 'Abu Dhabi', updated_at = NOW() WHERE name = 'Zara Ali';
UPDATE orders    SET status = 'delivered', updated_at = NOW() WHERE id IN (1, 4, 7);

The second sync read 12 records (594 bytes) from the slot and finished in 18 seconds end to end. For customers that meant one new data file with 4 rows: 2 inserts plus the new versions of 2 updated rows.

One small positional delete file retires the 2 old versions. OLake Go's commit report in the sync log shows it directly: addedDataFiles=1, addedRecords=4, addedPositionalDeleteFiles=1, addedPositionalDeletes=2. The orders table has the same shape with 8 rows and 3 deletes.

OLake UI sync log filtered to the Iceberg commit report, showing addedDataFiles 1, addedPositionalDeleteFiles 1, addedRecords and addedPositionalDeletes for the change sync
The change-only sync in the OLake UI log: one data file and one positional delete file per table.

Redshift shows the result on the next query, with no refresh and no ALTER TABLE. Liam Carter is gold, Zara Ali moved to Abu Dhabi, and the two new customers are there.

The two retired versions are gone, because Redshift applies the positional deletes while reading. The _op_type column shows how each row arrived: r for the snapshot, c for the inserts and u for the updates.

Schema changes. When a column is added in Postgres, OLake Go adds it to the Iceberg table on the next sync. The Glue table definition is updated in the same commit. Redshift shows it on the next query of the external schema.

Widening changes (int to bigint, real to double precision) are applied. Narrowing or incompatible type changes fail the sync rather than corrupt the table. Full rules are on the schema evolution page.

Query the same tables from Athena and DuckDB​

This is the point of writing Iceberg instead of loading Redshift tables. Nothing is exported, copied or refreshed: both engines read the files OLake Go wrote, through the same Glue database.

Athena reads the Glue Data Catalog natively: the Glue database OLake Go created shows up in the query editor's database list, and the Iceberg tables in it need no registration and no refresh. Pick a workgroup with a query result location in S3, select the database, and query.

Athena query editor (database: the one OLake Go created)
SELECT id, name, city, tier FROM postgres_to_redshift_shop_public.customers ORDER BY id;
SELECT status, count(*) AS n, round(sum(amount), 2) AS total
FROM postgres_to_redshift_shop_public.orders GROUP BY status ORDER BY status;

Athena engine v3 reads Iceberg v2 tables with positional and equality deletes, and cannot read v3 tables at all. Keep the job on positional or equality deletes, never deletion vectors, while Athena is a reader. From the command line, aws athena start-query-execution --work-group <workgroup> --query-execution-context Database=${props.schema ?? 'postgres_to_redshift_shop_public'} --query-string "..." runs the same queries; aws athena get-query-results returns the rows.

Athena query editor in the AWS console running the customers query on the postgres_to_redshift_shop_public database and returning 10 rows
Athena reading the same table through Glue: the same 10 rows.

An orders roll-up matched Postgres to the cent in all three engines. It gave 16 delivered orders for 8659.68, 18 placed for 10522.26, 11 shipped for 6397.25.

Data types: Postgres to Iceberg to Redshift​

OLake Go maps Postgres types to Iceberg types, and Redshift maps Iceberg types to its own through the external schema. Most columns pass through unchanged; three groups are worth knowing about.

PostgresIceberg (written by OLake Go)Redshift showsNotes
integer, serialintINTEGER
bigint, bigseriallongBIGINT
numeric(p,s), real, double precisiondoubleDOUBLE PRECISIONOLake Go stores decimals as doubles. The orders.amount sums on this page matched Postgres to the cent in every engine; round at query time when you need exact decimal arithmetic.
timestamptz, timestamptimestamptz (UTC)TIMESTAMPRedshift shows the UTC instant without a zone; timestamp has no zone in Postgres and is read as UTC.
varchar, text, uuid, json, jsonbstringVARCHARParse JSON with JSON_PARSE in Redshift.
booleanbooleanBOOLEAN
byteabinaryVARBYTE

Postgres types in the table come from the Postgres connector reference. The Redshift side comes from the Redshift Iceberg page (checked 2026-10-30) and from svv_external_columns on the tables of this run. OLake Go adds _cdc_lsn on Postgres tables, the WAL position each change came from.

Cost and maintenance​

  • Storage is your bucket: you pay S3 rates for the Parquet and delete files, and nothing to Glue beyond its per-object pricing for the table entries.
  • Compute. OLake Go runs wherever you run Docker. Redshift Serverless bills RPU-hours only while queries run; a query over the external schema scans the Parquet files in S3 through Redshift Spectrum, so filter on the columns you need. Athena bills per terabyte scanned.
  • Maintenance. Glue has no built-in compaction for tables written by external engines. A CDC table that gets a new small file and delete file per sync wants compaction; OLake Fusion does this for the catalogs OLake Go writes to, and Spark on EMR's rewrite_data_files works through the same Glue catalog.
  • WAL disk. The replication slot keeps WAL until OLake Go reads it. A job paused for a weekend on a busy server can hold tens of gigabytes. Drop the slot if you retire a job.
  • Key rotation. The IAM user's access key is what OLake Go writes with. Rotate it on your schedule, or run OLake Go on an instance with a role and leave the key fields empty.

Troubleshooting​

1. The destination test fails on a table OLake Go did not create​

User: arn:aws:iam::<account>:user/<olake user> is not authorized to perform: s3:GetObject on resource: "arn:aws:s3:::<another bucket>/.../test_olake.db/test_olake/metadata/00000-....metadata.json"

OLake Go's destination test opens a probe table called test_olake.test_olake in the Glue database test_olake. An earlier run in the same account and region may have left that table behind, pointing at a bucket your user cannot read.

The test then fails before it writes anything. Delete the stale test_olake.test_olake table from Glue, or run OLake Go against a region whose Glue catalog does not have it. The run on this page hit this and moved to a clean region.

2. Redshift cannot see the external schema's tables​

ERROR: Database 'postgres_to_redshift_shop_public' does not exist in the Glue Data Catalog

The external schema was created before the first sync, or in a different region from the Glue database. OLake Go creates the Glue database on the first sync; create the external schema after it, in the workgroup's region.

3. Redshift returns no rows or old rows​

The IAM role on the namespace or cluster cannot read the bucket, so Spectrum skips the data files. Check that the role has s3:GetObject on the bucket path and glue:GetTable on the database. Check too that IAM_ROLE default points at it (DEFAULT_IAM_ROLE in the namespace settings).

4. Athena asks for a query result location​

Before you run your first query, you need to set up a query result location in Amazon S3.

Athena writes every result set to S3. Create a workgroup with a result location in your bucket and pick it in the editor, instead of changing the shared primary workgroup. The run on this page used s3://<bucket>/athena-results/.

5. The next sync waits on the slot​

If a sync hangs after the snapshot, another process holds the replication slot, or wal_sender_timeout cut the connection during a quiet period. Check pg_stat_replication for a second consumer of olake_slot, and set wal_sender_timeout = 0 for the OLake Go user as the prerequisites block shows.

How this compares with other ways to move Postgres data to Redshift
MethodFreshnessUpdates and deletesWhere the data landsWhat you run
Aurora PostgreSQL zero-ETL integrationSecondsYesNative Redshift tables; Redshift is the only readerNothing to run; Aurora PostgreSQL as the source
AWS DMS to RedshiftCDC through logical replicationYesNative Redshift tablesA DMS replication instance and task, plus an S3 staging bucket
COPY from S3 exports on a scheduleHours to dailyNo; full reloads or your own mergeNative Redshift tablesA script, a scheduler and a staging bucket
Managed ELT (Fivetran, Estuary, Airbyte Cloud)Minutes, by planVaries by toolRedshift tables, a second copy you pay to move and storeNothing; priced per row, per GB or per connector
OLake Go to Iceberg on Glue (this guide)CDC on a schedule down to one minuteYes, as Iceberg row-level deletesApache Iceberg tables that Redshift, Athena, EMR, Spark and DuckDB all readOne Docker Compose stack, open source; your bucket plus your own compute

The zero-ETL integration is the simpler choice when the source is Aurora and Redshift is the only reader. Writing Iceberg once avoids paying to move and store a second copy. That matters as soon as Athena, EMR or a notebook needs the same rows, or you want to own the files.

When not to use this​

  • Every query must hit native Redshift speed. Spectrum reads Parquet from S3 on every query. Keep the Iceberg tables as the source and load the hot slice into a native table on a schedule.
  • Tables have no primary key. Inserts replicate; updates and deletes cannot be matched to a row and are not applied. Add a primary key or set REPLICA IDENTITY FULL first.
  • You want deletion vectors. Redshift reads them only on Serverless above 4 RPU or RG-type clusters, and Athena cannot read Iceberg v3 at all. Keep the job on positional deletes while either is a reader.

FAQs​

How do I replicate Postgres to Redshift without DMS or zero-ETL?

Run OLake Go (open source, Apache 2.0) against a Postgres database with logical replication on, and point it at the AWS Glue Data Catalog with an S3 bucket. The first sync copies the tables in full, every later sync replays only the changes from the write-ahead log, and Redshift reads the result through an external schema over the Glue database. No DMS task, no Kafka and no Redshift load job sit in the path.

Does Redshift read Iceberg tables with row-level deletes?

Yes. Redshift reads Iceberg v2 tables with positional deletes through Redshift Spectrum, on Serverless and RA3. The run on this page used positional deletes and Redshift returned one row per key after the updates. Equality deletes and Iceberg v3 tables with deletion vectors are not the right pick while Redshift is a reader.

Does OLake Go copy the existing Postgres data before it starts reading the WAL?

Yes, automatically. A job in Full Refresh + CDC mode creates the replication slot, copies every selected table in parallel chunks, then switches to the slot. Nothing written during the copy is lost, and there is no separate backfill job.

Why Iceberg on Glue rather than loading Redshift tables?

A Glue-registered Iceberg table is one copy that Redshift, Athena, EMR, Spark and DuckDB all read, in a bucket you own, with no COPY job and no refresh. Loading native Redshift tables is faster to query but makes Redshift the only reader, and a second engine means a second copy. Keep the Iceberg tables as the shared source and load a hot slice into a native table when a dashboard needs it.

Is Redshift one of OLake Go's target query engines?

Not in the picker yet. On this page the job lists Athena and DuckDB as target engines and sets the upsert type to Positional Deletes by hand, which is the format Redshift reads. Redshift then reads the tables through the external schema like any other Iceberg v2 table.

Which Postgres data types change on the way to Redshift?

numeric columns become Iceberg double and show up in Redshift as DOUBLE PRECISION, so round at query time when you need exact decimal arithmetic. timestamptz becomes TIMESTAMP in UTC. integer stays INTEGER, bigint BIGINT, varchar and text become VARCHAR, and json and jsonb arrive as VARCHAR.

What does OLake Go need on the Postgres side?

wal_level set to logical, a replication slot and a publication for the tables, and a user with the REPLICATION attribute and SELECT on the tables. Hosted Postgres on RDS, Aurora, Cloud SQL, Azure, Neon or Supabase exposes the same settings through a parameter group or dashboard toggle.

Postgres to SnowflakeSame source, read from Snowflake.Postgres to ClickHouseSame source, read from ClickHouse.Postgres to DuckDBSame source, read from DuckDB.Postgres to BigQuerySame source, read from BigQuery.Postgres to DatabricksSame source, read from Databricks.Postgres to Apache IcebergSame source, read from Apache Iceberg.Oracle to Amazon RedshiftSame engine, replicated from Oracle.Kafka to Amazon RedshiftSame engine, replicated from Kafka.MySQL to Amazon RedshiftSame engine, replicated from MySQL.MongoDB to Amazon RedshiftSame engine, replicated from MongoDB.MariaDB to Amazon RedshiftSame engine, replicated from MariaDB.S3 to Amazon RedshiftSame engine, replicated from S3.SQL Server to Amazon RedshiftSame engine, replicated from SQL Server.DB2 to Amazon RedshiftSame engine, replicated from DB2.Postgres connector referenceEvery field, sync mode and troubleshooting note for the source.Query engine compatibilityWhich engines read which catalogs and delete formats.All integrationsEvery source and engine pair with a tested guide.

Deeper reading on this site: