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Oracle to Redshift: Replicate via Apache Iceberg

Oracle to Redshift usually means GoldenGate, a Database Migration Service (DMS) task, or a nightly export and COPY. This guide takes a fourth route.

OLake Go reads the Oracle tables over a plain Java Database Connectivity (JDBC) connection, writes them once as Apache Iceberg tables into the AWS Glue Data Catalog, and keeps them fresh on a cursor column. The files stay in your own S3 bucket.

Redshift maps the Glue database onto an external schema and queries the tables in place. Nothing else is in the path: no GoldenGate, no DMS, 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 Oracle into Iceberg tables in the Glue Data Catalog: a full historical snapshot first, then incremental syncs on a cursor column such as UPDATED_AT, with no GoldenGate or DMS in between. The Oracle connector has no log-based change data capture (CDC), so the cursor is what carries changes.
  • 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 Oracle to Redshift without redo-log access, run OLake Go with a read-only Oracle user as the source and 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 fetches the rows whose cursor column moved and upserts them. Redshift, Athena and DuckDB then query the same Iceberg tables, with no second copy.

How Oracle changes reach Redshift here​

Two things happen in order, and OLake Go does both on its own. The first sync reads each table in parallel chunks and writes Parquet data files, then records the highest cursor value it saw: that is the historical load. Every later sync selects rows above that value and writes them as new data files.

A delete file retires the old version of each updated row. 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 cursor is the only thing that carries changes. A row whose UPDATED_AT is not bumped on update is invisible to the next sync, and a deleted row is never seen. Set the column in every write path, or add a trigger, and soft-delete rows you need to remove downstream.

What you need​

  • Oracle Database 11g or later reachable over JDBC, with a read-only user and a cursor column on each table. The tables on this page ran on Oracle Database 23ai Free in Docker.
  • 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 Oracle for incremental sync​

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, in the SHOP schema of pluggable database FREEPDB1
CREATE TABLE shop.customers (
  id NUMBER(10) GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,
  name VARCHAR2(100), email VARCHAR2(200), city VARCHAR2(100), tier VARCHAR2(20),
  created_at TIMESTAMP DEFAULT SYSTIMESTAMP,
  updated_at TIMESTAMP DEFAULT SYSTIMESTAMP);

CREATE TABLE shop.orders (
  id NUMBER(10) GENERATED BY DEFAULT AS IDENTITY PRIMARY KEY,
  customer_id NUMBER(10) REFERENCES shop.customers(id),
  product VARCHAR2(100), amount NUMBER(10,2), status VARCHAR2(20),
  created_at TIMESTAMP DEFAULT SYSTIMESTAMP,
  updated_at TIMESTAMP DEFAULT SYSTIMESTAMP);

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

OLake Go reads Oracle tables over a plain Java Database Connectivity (JDBC) connection: a parallel full snapshot first, then incremental syncs that fetch only rows whose cursor column moved past the last value seen. There is no log-based change data capture (CDC) for Oracle in OLake Go, so three things must be in place before the first sync.

SettingValueWhy it matters
A read-only userCREATE SESSION plus SELECT on each tableThe snapshot and every incremental sync read the tables directly.
A cursor columna column that grows with every change, such as updated_atIncremental syncs select WHERE cursor > last value. Rows updated without touching the cursor are not picked up.
A primary keyon every tableOLake Go uses it to upsert changed rows into the Iceberg table. Without one, changed rows are appended.
On the Oracle database, as a DBA
-- 1. A user for OLake Go
CREATE USER olake IDENTIFIED BY "<password>";
GRANT CREATE SESSION TO olake;

-- 2. Read access to the tables you sync
GRANT SELECT ON shop.customers TO olake;
GRANT SELECT ON shop.orders TO olake;
GRANT SELECT ANY DICTIONARY TO olake;   -- lets discovery read the catalog views

-- 3. A cursor column that your application always sets, for example
--    updated_at TIMESTAMP DEFAULT SYSTIMESTAMP, updated by a trigger or by the application on every change

Check the result before moving on:

sql
SELECT username FROM all_users WHERE username = 'OLAKE';
SELECT table_name FROM all_tables WHERE owner = 'SHOP';

The full list of privileges, with the reason for each, is on the generic Oracle setup guide.

Deletes do not replicate in incremental mode

A deleted row has no cursor value to read, so it stays in the Iceberg table. If deletes matter, soft-delete in Oracle (a deleted_at column that also bumps updated_at) or schedule an occasional full refresh.

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 Oracle to OLake Go​

  1. In the OLake UI, open Sources and click Create Source. Pick Oracle as the connector, give the source a name and keep the OLake Go version on the latest release.
  2. Fill in the host and port, choose Service Name (or SID) as the connection type and enter it, then the username and password from step 1. Type the username the way Oracle stores it, which is upper case unless you created it in double quotes: OLake Go quotes the name when it connects, so olake and OLAKE are different users.
  3. Click Set up source. OLake Go tests the connection and saves the source.

Here is the source form from the run on this page. Oracle ran in another container, so the host is host.docker.internal. The connection type is Service Name (FREEPDB1) rather than a system identifier (SID), and the user is the one from step 1.

OLake UI Edit source form for Oracle on OLake Go v1.1.0 with host host.docker.internal, port 1521, connection type Service Name, service name FREEPDB1 and user olake
The Oracle source in OLake UI: service name FREEPDB1 and the read-only user from step 1.

The full field reference, including SSL and SSH tunnels, is on the Oracle 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, an S3 path under the lab bucket, 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. Because the Oracle connector has no change data capture (CDC) mode, each stream runs as Full Refresh + Incremental with UPDATED_AT as the cursor.

  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 + Incremental with UPDATED_AT as the cursor column 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 SHOP schema, CUSTOMERS and ORDERS selected, sync mode Full Refresh + Incremental, cursor field UPDATED_AT, ingestion mode Upsert and upsert type Positional Deletes on Iceberg format version V2
Each stream: Full Refresh + Incremental on UPDATED_AT, Upsert, and Positional Deletes as the upsert type. The Iceberg DB name at the top is editable.
OLake UI Job Logs and History for oracle_to_redshift showing two completed syncs, the first in 19 seconds and the second in 11 seconds
Job history: the 19 s historical snapshot, then the 11 s incremental sync.

On this page the first run copied 8 customers and 40 orders (48 records, 2.24 KB) and finished in 19 seconds, container start included. OLake Go names the Glue database after the job and the source schema, lower-cased, so oracle_to_redshift on schema SHOP produced oracle_to_redshift_shop. 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 'oracle_to_redshift_shop'
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 incremental sync in query editor v2: 10 rows, one per key.

Step 7: Change rows in Oracle and sync again​

To see the incremental path work, the run on this page made 12 changes in Oracle after the first sync. Every update also sets updated_at, which is what the cursor sees:

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

The second sync fetched 12 rows (598 bytes) above the saved cursor and finished in 11 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 incremental sync
The incremental 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. In incremental mode OLake Go cannot tell an insert from an update, so every row the second sync brought carries _op_type = u; the snapshot rows keep r.

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

Widening changes 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 oracle_to_redshift_shop.customers ORDER BY id;
SELECT status, count(*) AS n, round(sum(amount), 2) AS total
FROM oracle_to_redshift_shop.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 oracle_to_redshift_shop database and returning 10 rows
Athena reading the same table through Glue: the same 10 rows.

An orders roll-up matched Oracle 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: Oracle to Iceberg to Redshift​

OLake Go maps Oracle types to Iceberg types, and Redshift maps Iceberg types to its own through the external schema. Numbers are the group to watch.

OracleIceberg (written by OLake Go)Redshift showsNotes
NUMBER(n,0)longBIGINTThe NUMBER(10) identity keys on this page arrived as BIGINT.
NUMBER(n,m) with a scale, FLOAT, BINARY_DOUBLEdoubleDOUBLE PRECISIONOLake Go stores decimals as doubles. The orders.amount sums on this page matched Oracle to the cent in every engine; round at query time when you need exact decimal arithmetic.
BINARY_FLOATfloatREAL
DATE, TIMESTAMP, TIMESTAMP WITH TIME ZONE, TIMESTAMP WITH LOCAL TIME ZONEtimestamptz (UTC)TIMESTAMPOracle DATE carries a time; it arrives as a UTC instant, shown without a zone.
VARCHAR2, NVARCHAR2, CHAR, CLOB, NCLOB, LONGstringVARCHAR
RAW, LONG RAW, BLOB, BFILE, INTERVAL typesstringVARCHARBinary values arrive as text; keep large binaries out of the sync.

Oracle types in the table come from the Oracle 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.

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.
  • Oracle load. Each incremental sync runs one range query per table on the cursor column. Index UPDATED_AT on large tables so the query does not scan them.
  • Maintenance. Glue has no built-in compaction for tables written by external engines. A 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.
  • 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 source test fails to connect​

ORA-12514: Cannot connect to database. Service FREEPDB1 is not registered with the listener

The service name is wrong or the listener has not finished registering it. Run lsnrctl status on the server, or connect with the SID instead. On a fresh container the service appears a minute or two after startup.

2. The source test fails with ORA-01017 although sqlplus logs in​

failed to ping database: ORA-01017: invalid credential or not authorized; logon denied

OLake Go quotes the username when it connects, so Oracle compares it case-sensitively. A user created as CREATE USER olake is stored as OLAKE; enter it in upper case.

3. Updated rows are missing after an incremental sync​

The update did not move the cursor column. OLake Go only fetches rows whose cursor value is above the last one it saw, so an UPDATE that leaves UPDATED_AT untouched is invisible. Set the column in every write path, or add a trigger that sets it on UPDATE.

4. 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 table from Glue, or run OLake Go against a region whose Glue catalog does not have it.

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

ERROR: Database 'oracle_to_redshift_shop' 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.

How this compares with other ways to move Oracle data to Redshift
MethodFreshnessUpdates and deletesWhere the data landsWhat you run
AWS DMS to RedshiftCDC through LogMiner or Binary ReaderYesNative Redshift tablesA DMS replication instance and task, plus an S3 staging bucket; needs supplemental logging on Oracle
Oracle GoldenGate to RedshiftSecondsYesNative Redshift tablesGoldenGate Extract and Replicat, a licensed Oracle option
Export (Data Pump or SELECT to CSV), then COPY into RedshiftHours to dailyNo; full reloads onlyNative 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)Incremental on a schedule down to one minuteInserts and updates through the cursor; no deletesApache Iceberg tables that Redshift, Athena, EMR, Spark and DuckDB all readOne Docker Compose stack, open source; your bucket plus your own compute

DMS is the simpler choice when you need deletes replicated and are already on AWS. 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​

  • You need hard deletes replicated. Incremental sync cannot see a deleted row. Soft-delete in Oracle, or use a log-based tool.
  • Tables have no cursor column and cannot get one. Without a monotonically increasing column, only Full Refresh works.
  • 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.

FAQs​

How do I replicate Oracle to Redshift without GoldenGate or DMS?

Run OLake Go (open source, Apache 2.0) against the Oracle database with a read-only user, and point it at the AWS Glue Data Catalog with an S3 bucket. The first sync copies each table in parallel chunks; every later sync fetches only rows whose cursor column (for example UPDATED_AT) moved, and upserts them. Redshift reads the result through an external schema over the Glue database. GoldenGate, DMS, Kafka and a Redshift load job are not needed, and no Oracle license option is involved.

Is this change data capture from the Oracle redo log?

No. The Oracle connector runs Full Refresh and Full Refresh + Incremental; it does not read redo logs or LogMiner. Incremental syncs select rows where a cursor column is greater than the last value seen, which captures inserts and updates that bump the cursor but not deletes. For log-based capture you need GoldenGate or Debezium's Oracle connector.

Does OLake Go copy the existing Oracle data before it starts the incremental syncs?

Yes, automatically. A job in Full Refresh + Incremental mode first copies every selected table in parallel chunks, records the highest cursor value, and from the next run fetches only rows above it. There is no separate backfill job.

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.

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 Oracle data types change on the way to Redshift?

NUMBER columns with a scale become Iceberg double and show up in Redshift as DOUBLE PRECISION, so round at query time when you need exact decimal arithmetic. Whole-number NUMBER columns become BIGINT. DATE and every TIMESTAMP variant become TIMESTAMP in UTC. VARCHAR2, CLOB and the binary types arrive as VARCHAR.

Can OLake Go read Oracle on RDS, Autonomous Database or on premises?

Yes. The connector connects over JDBC with a service name or SID, a read-only user and SELECT on the tables, so RDS for Oracle, Autonomous Database and self-managed instances all work. The run on this page used Oracle Database 23ai Free in Docker.

Oracle to SnowflakeSame source, read from Snowflake.Oracle to BigQuerySame source, read from BigQuery.Oracle to DatabricksSame source, read from Databricks.Oracle to Apache IcebergSame source, read from Apache Iceberg.Postgres to Amazon RedshiftSame engine, replicated from Postgres.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.Oracle 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: