Skip to main content

Oracle to Snowflake: Incremental Sync via Iceberg

Oracle to Snowflake usually means GoldenGate, a Kafka pipeline, or a nightly export. 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 a catalog Snowflake reads, and keeps them fresh on a cursor column.

With Snowflake's own Horizon catalog, the default on this page, the files sit on Snowflake-managed storage. Nothing else is in the path: no GoldenGate, no Kafka, no Spark. With AWS Glue, the files sit in your S3 bucket and Snowflake reads them through a catalog integration.

By Shubham Satish BaldavaUpdated October 29, 2026

Key takeaways

  • OLake Go replicates Oracle into Snowflake as Iceberg tables: a full historical snapshot first, then incremental syncs on a cursor column such as updated_at, with no GoldenGate or Kafka in between. The Oracle connector has no log-based change data capture (CDC), so the cursor is what carries changes.
  • Through the Horizon catalog the tables land as Snowflake-managed Iceberg tables. You query them in Snowsight right away, with no CREATE ICEBERG TABLE, no external volume and no refresh.
  • The same tables, written once by OLake Go, are also queryable from DuckDB and Spark through the same catalog. In the run on this page all three engines returned identical rows.

The short answer. To replicate Oracle to Snowflake without redo-log access, run OLake Go with a read-only Oracle user as the source and the Snowflake Horizon catalog as the Iceberg destination. The first sync copies each table in full (the historical snapshot); every later sync fetches the rows whose cursor column moved and upserts them. Snowflake, DuckDB and Spark then query the same Iceberg tables, with no second copy.

How Oracle data reaches Snowflake 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 Horizon. OLake Go asks Horizon for short-lived storage credentials, uploads the data and delete files, then posts the new snapshot to the catalog. Snowflake sees it the moment the commit lands; DuckDB and Spark see it on their next catalog load.

One thing to keep an eye on: the cursor is the whole contract. A row that changes without its updated_at moving is never picked up.

A deleted row leaves no cursor value behind, so it stays in the Iceberg table. Set the column from a trigger or in every write path, and soft-delete if deletes matter.

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.
  • A Snowflake account where you can run the one-time setup as ACCOUNTADMIN. Snowflake-managed storage means no cloud bucket or IAM role on your side.
  • OLake UI from the Docker Compose install, with the OLake Go Oracle source on v0.11 or later. The run on this page used v1.1.0.
  • Docker, and about 30 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 catalog in Snowflake​

Snowflake reads Iceberg tables from two kinds of catalog. Horizon is Snowflake's own: OLake Go writes straight into it, the tables are Snowflake-managed, and they appear in Snowsight the moment a sync commits.

AWS Glue is a catalog outside Snowflake, so the files stay in your S3 bucket. Snowflake reaches them through a catalog integration: a read-only link to that external catalog, refreshed after each sync.

Horizon is the default on this page. Glue is the right pick when Athena, Redshift or Spark on Amazon Elastic MapReduce (EMR) will read the same tables. It keeps the files in your own bucket.

CatalogOLake Go writesSnowflake readsVerified on this page
AWS GlueYesYes: catalog integration (read-only)Documented, not yet run by us
Snowflake HorizonYesYes: native: tables are Snowflake-managed Iceberg tablesYes, end to end

Snowflake Horizon is the catalog built into every Snowflake account. OLake Go talks to it through the Iceberg REST endpoint at https://<account_identifier>.snowflakecomputing.com/polaris/api/catalog, and the tables it creates are Snowflake-managed Iceberg tables. Writing through this endpoint needs OLake Go 0.11.0 or later.

Run the following once in a Snowsight worksheet as ACCOUNTADMIN. It creates a database for the tables, a role OLake Go writes as, a service user that holds the token, and the authentication policy the token needs.

Snowflake, once per account
USE ROLE ACCOUNTADMIN;

-- A small warehouse for your own queries, and the database OLake Go writes into.
CREATE WAREHOUSE IF NOT EXISTS OLAKE_WH WAREHOUSE_SIZE = XSMALL AUTO_SUSPEND = 60 AUTO_RESUME = TRUE;
CREATE DATABASE IF NOT EXISTS OLAKE_DEMO;
ALTER DATABASE OLAKE_DEMO SET CATALOG = 'SNOWFLAKE';
ALTER DATABASE OLAKE_DEMO SET EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED';

-- The role OLake Go uses. It needs to create schemas; tables are created inside them.
CREATE ROLE IF NOT EXISTS OLAKE_ROLE;
GRANT USAGE, CREATE SCHEMA ON DATABASE OLAKE_DEMO TO ROLE OLAKE_ROLE;
GRANT USAGE ON WAREHOUSE OLAKE_WH TO ROLE OLAKE_ROLE;
GRANT ROLE OLAKE_ROLE TO ROLE SYSADMIN;

-- A service user for OLake Go. It holds the programmatic access token.
CREATE USER IF NOT EXISTS OLAKE_SVC TYPE = SERVICE DEFAULT_ROLE = OLAKE_ROLE;
GRANT ROLE OLAKE_ROLE TO USER OLAKE_SVC;

-- Snowflake issues a token to a service user only under a network policy, unless an
-- authentication policy waives that. This one waives it. Any schema can hold the policy.
USE SCHEMA OLAKE_DEMO.PUBLIC;
CREATE AUTHENTICATION POLICY IF NOT EXISTS olake_pat_policy
  PAT_POLICY = (NETWORK_POLICY_EVALUATION = ENFORCED_NOT_REQUIRED);
ALTER USER OLAKE_SVC SET AUTHENTICATION POLICY olake_pat_policy;

-- The two values OLake Go asks for.
SELECT CURRENT_ORGANIZATION_NAME() || '-' || CURRENT_ACCOUNT_NAME() AS account_identifier,
       CURRENT_REGION() AS region;

-- The token. Snowflake shows the secret once; copy it straight into OLake Go.
ALTER USER OLAKE_SVC ADD PROGRAMMATIC ACCESS TOKEN OLAKE_PAT
  ROLE_RESTRICTION = 'OLAKE_ROLE' DAYS_TO_EXPIRY = 30;

What each block is for:

  • CATALOG = 'SNOWFLAKE' and EXTERNAL_VOLUME = 'SNOWFLAKE_MANAGED' on the database make every table OLake Go creates a Snowflake-managed Iceberg table on Snowflake's own storage. No S3 bucket, IAM role or external volume on your side. To keep the files in your own bucket, create an external volume and name it here instead.
  • The authentication policy is the step people miss. Without it, the ADD PROGRAMMATIC ACCESS TOKEN line fails with Network Policy is required when creating a programmatic access token for user OLAKE_SVC. In production, attach a network policy that allows OLake Go's egress IP instead of waiving the check.
  • ROLE_RESTRICTION pins the token to OLAKE_ROLE, so a leaked token cannot act as ACCOUNTADMIN. Tokens expire after DAYS_TO_EXPIRY (15 by default, 365 at most). Rotate it before then or the sync fails with an authentication error.

You can check the token outside OLake Go by exchanging it for a session token, the same call OLake Go makes on every sync:

Token check
curl -s -X POST "https://<account_identifier>.snowflakecomputing.com/polaris/api/catalog/v1/oauth/tokens" \
  -H 'Content-Type: application/x-www-form-urlencoded' \
  --data-urlencode grant_type=client_credentials \
  --data-urlencode scope=session:role:OLAKE_ROLE \
  --data-urlencode client_secret=<your PAT>

A good token returns a JSON object with an access_token. The other authentication methods (key-pair JWT, External OAuth, workload identity federation) are on the Horizon catalog page.

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 Horizon as the catalog type.
  3. Choose Programmatic Access Token (PAT) as the authentication type and fill in the fields below.
  4. Click Create. OLake Go calls the Horizon endpoint, exchanges the token and saves the destination.
OLake UI destination form for the Snowflake Horizon catalog with the PAT authentication fields

Horizon Configuration Parameters

ParameterSample ValueDescription
Authentication Type
required		Programmatic Access Token (PAT)Authentication method OLake Go uses for Horizon Catalog REST API requests. Set to match your workspace's authentication configuration.
Horizon Iceberg REST Catalog URI
required		https://<account_identifier>.snowflakecomputing.com/polaris/api/catalogSnowflake Horizon Catalog REST API endpoint.
Snowflake Database
required		SNOWThe name of the database in Snowflake.
Session Role Scope
required		session:role:<HORIZON_ROLE>Informs Snowflake which role to use for this Horizon Catalog REST session, which controls privileges for catalog and table operations. Space-separated if providing more than one.
Programmatic Access Token
required		ver:1-hint:...Snowflake programmatic access token (PAT).
Cloud Storage Region
required		AWS_AP_SOUTH_1Snowflake's account region used to route requests to the correct storage endpoint. Run SELECT CURRENT_REGION(); to know the region
Catalog NamehorizonName of the Iceberg catalog OLake Go registers tables under. Defaults to olake_iceberg if left empty.
Enable Arrow WritesfalseWrites data and delete files using Apache Arrow based writer and registers them in Iceberg.
Catalog Name Supported for v0.3.5 and above
For the catalog name, OLake Go only supports lowercase letters and underscores. Spaces and special characters are not supported.

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

  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 Snowflake, plus DuckDB and Apache Spark if they will read the tables too. 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.

Three target query engines were set on this job. That is the only format decision you make: OLake Go works out which Iceberg delete format all three can read and sets it on every stream. Because the Oracle connector has no change data capture (CDC) mode, each stream runs as Full Refresh + Incremental with updated_at as the cursor.

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
Each stream: Full Refresh + Incremental on updated_at, Upsert, and the upsert type OLake Go resolved (Positional Deletes in this run).
OLake UI Job Logs and History for oracle_to_snowflake showing two completed syncs, the first in 22 seconds and the second in 17 seconds
Job history: the 22 s historical snapshot, then the 17 s incremental sync.

On this page the first run copied 8 customers and 40 orders (48 records) and finished in 22 seconds, container start included. OLake Go names the destination schema after the job and the source schema, lower-cased, so oracle_to_snowflake on schema SHOP produced oracle_to_snowflake_shop. The Iceberg DB field at the top of the Streams page changes it before the first sync.

Step 6: Query the tables in Snowflake​

There is no registration step. The schema and tables OLake Go committed through Horizon are already Snowflake-managed Iceberg tables in the database you configured. One thing to know before the first query: OLake Go creates them with quoted, lowercase names, so Snowflake needs the double quotes.

Snowsight
-- What OLake Go created (schema = <job name>_<source database>[_<source schema>])
SHOW ICEBERG TABLES IN SCHEMA OLAKE_DEMO."oracle_to_snowflake_shop";

-- Quoted identifiers: the names are lowercase, as they were in the source
SELECT "id", "name", "city", "tier"
FROM OLAKE_DEMO."oracle_to_snowflake_shop"."customers"
ORDER BY "id";

SHOW ICEBERG TABLES reports catalog_name = SNOWFLAKE, iceberg_table_type = MANAGED and external_volume_name = SNOWFLAKE_MANAGED: Snowflake treats them as its own managed Iceberg tables, not as external tables that need a refresh.

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.

Snowsight SQL file running SHOW ICEBERG TABLES on the oracle_to_snowflake_shop schema and listing customers and orders with owner OLAKE_ROLE, external volume SNOWFLAKE_MANAGED, catalog SNOWFLAKE and table type MANAGED
SHOW ICEBERG TABLES in Snowsight: both tables are Snowflake-managed, created by OLAKE_ROLE through Horizon.
Snowsight query on OLAKE_DEMO.oracle_to_snowflake_shop.customers returning 10 rows with id, name, city and tier
The customers table in Snowsight after the incremental sync: 10 rows on an X-Small warehouse.

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 17 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 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, and orders the same shape with 8 rows and 3 deletes.

OLake UI sync log filtered to the Iceberg commit reports for customers and orders after the incremental sync, showing addedDataFiles 1, addedPositionalDeleteFiles 1, addedRecords 4 and 8, totalEqualityDeletes 0
The incremental sync in the OLake UI log: one data file and one positional delete file per table, zero equality deletes.

Snowflake shows the result with no refresh. 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:

Snowsight query on customers after the incremental sync showing 10 rows with _op_type r for the eight snapshot rows and u for Liam Carter, Zara Ali, Maya Patel and Lucas Silva
Snowsight after the incremental sync: the four rows the cursor picked up carry u, the untouched snapshot rows r.

Schema changes. When a column is added in Oracle, OLake Go adds it to the Iceberg table on the next sync, and Snowflake shows it. 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 DuckDB and Spark​

This is the point of writing Iceberg instead of loading Snowflake tables. Nothing is exported, copied or refreshed: both engines read the files OLake Go wrote, through the same Horizon endpoint, with credentials vended by Snowflake.

DuckDB attaches Horizon as an Iceberg REST catalog. It wants a bearer token rather than the raw PAT, so exchange the PAT first (the curl call from the catalog setup), then attach:

DuckDB 1.5.5
INSTALL iceberg; LOAD iceberg;
INSTALL httpfs;  LOAD httpfs;

CREATE SECRET horizon_secret (TYPE iceberg, TOKEN '<access_token from /v1/oauth/tokens>');

ATTACH 'OLAKE_DEMO' AS horizon (
TYPE iceberg,
ENDPOINT 'https://<account_identifier>.snowflakecomputing.com/polaris/api/catalog',
SECRET horizon_secret,
ACCESS_DELEGATION_MODE 'vended_credentials'
);

SHOW ALL TABLES;
SELECT id, name, city, tier FROM horizon.oracle_to_snowflake_shop.customers ORDER BY id;

The session token lives about an hour; re-run the exchange and CREATE OR REPLACE SECRET when a query fails with an authorization error. DuckDB reads deletion vectors from duckdb-iceberg 1.5.3 onward, and only attaches REST catalogs backed by S3, S3 Tables or Google Cloud Storage, so pick an AWS- or GCP-hosted Snowflake account for this path.

DuckDB 1.5.5 shell querying horizon.oracle_to_snowflake_shop.customers and returning the same 10 rows as Snowflake
DuckDB 1.5.5 reading the Snowflake-managed table through Horizon: the same 10 rows.

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

Data types: Oracle to Iceberg to Snowflake​

OLake Go maps Oracle types to Iceberg types, and Snowflake maps Iceberg types to its own. Numbers are the group to watch.

OracleIceberg (written by OLake Go)Snowflake showsNotes
NUMBER(n,0), n up to 9intNUMBER(10,0)
NUMBER(n,0), n above 9longNUMBER(19,0)
NUMBER(n,m) with a scale, FLOAT, BINARY_DOUBLEdoubleFLOATOLake 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_FLOATfloatFLOAT
DATE, TIMESTAMP, TIMESTAMP WITH TIME ZONE, TIMESTAMP WITH LOCAL TIME ZONEtimestamptz (UTC)TIMESTAMP_LTZ(6)Oracle DATE carries a time; it arrives as a UTC timestamp.
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 Snowflake side from Snowflake's Iceberg data types page (checked 2026-10-14).

Cost and maintenance​

  • Storage is Snowflake's with SNOWFLAKE_MANAGED: you pay Snowflake storage rates for the Parquet and delete files. Point the database at your own external volume to keep the files in your bucket instead.
  • Compute. OLake Go runs wherever you run Docker; it does not use a Snowflake warehouse to write. Snowflake compute is spent only when you query.
  • Oracle load. Each incremental sync is one indexed range scan per table on the cursor column; index updated_at so the scan stays cheap as the table grows.
  • Maintenance. Snowflake expires old snapshots and compacts manifests on Snowflake-managed tables at no cost. Automatic data-file compaction is bundled only when Snowflake alone writes the table (Snowflake-managed Iceberg table maintenance). A table that gets a new small file every run will want compaction from the writer side; OLake Fusion does this for the catalogs OLake Go writes to, and Spark's rewrite_data_files works through the same Horizon endpoint.
  • Token rotation. The programmatic access token expires (30 days in the setup above, 365 at most). Put the renewal on a calendar; an expired token fails the next sync at the catalog handshake, before any data moves.

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. The run on this page hit this on its first attempt with olake in lower case.

3. Discovery sees no tables​

The OLake Go user can log in but has no SELECT on the tables, or the tables live in another schema. Grant SELECT per table (step 1) and check all_tables as that user.

4. 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.

5. Snowflake cannot find the table​

SQL compilation error:
Object 'OLAKE_DEMO.ORACLE_TO_SNOWFLAKE_SHOP.CUSTOMERS' does not exist or not authorized.

Snowflake upper-cased an unquoted identifier. OLake Go creates the schema and tables with lowercase names, so quote them: OLAKE_DEMO."oracle_to_snowflake_shop"."customers".

6. The destination test fails at the catalog handshake​

OLake Go's destination test exchanges the token at /v1/oauth/tokens before anything else. Three causes cover nearly every failure: a service user without a network or authentication policy, a scope that does not match the token's ROLE_RESTRICTION, or an expired token. Run the curl exchange from the catalog setup; it returns the same error Snowflake gives OLake Go.

How this compares with other ways to move Oracle data to Snowflake
MethodFreshnessUpdates and deletesWhere the data landsWhat you run
Oracle GoldenGate with the Snowflake handlerSeconds, from the redo logYesNative Snowflake tablesGoldenGate (licensed), its Snowflake handler and staging
Debezium's Oracle connector, Kafka and the Snowflake Kafka connectorSeconds, from LogMinerYes, with the connector's tombstone handlingKafka topics, then Snowflake tablesKafka, Kafka Connect, Debezium and a schema registry
Managed ELT (Fivetran, Estuary, Airbyte Cloud)Minutes, by planYes on plans with log-based Oracle captureSnowflake tables, a second copy you pay to move and storeNothing; priced per row, per GB or per connector
Manual export (Data Pump or SQL*Plus spool to CSV, then COPY INTO)Hours to dailyNo; full reloads onlyFiles you controlA script and a scheduler
OLake Go to Iceberg on Horizon (this guide)Incremental on a schedule down to one minuteInserts and updates that move the cursor; deletes need a soft-delete columnApache Iceberg tables that Snowflake, DuckDB, Spark and Trino all readOne Docker Compose stack, open source; Snowflake storage plus your own compute

GoldenGate is the choice when you need log-based capture with hard deletes and already hold the license. Writing Iceberg once avoids paying to move and store a second copy as soon as another engine 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.
  • Your Snowflake account is on Azure and DuckDB is a reader. DuckDB attaches REST catalogs backed by S3, S3 Tables and Google Cloud Storage only, so pick Spark or Trino as the second engine there.

FAQs​

How do I migrate Oracle to Snowflake without GoldenGate?

Run OLake Go (open source, Apache 2.0) against the Oracle database with a read-only user, and point it at Snowflake's Horizon catalog. 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. The tables appear in Snowflake as Snowflake-managed Iceberg tables. GoldenGate, Kafka and a Snowflake connector are not needed, and no Oracle license option is involved.

Does OLake Go do change data capture (CDC) from Oracle redo logs?

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 incremental syncs start?

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.

What is the best way to move Oracle data to Snowflake?

For a one-time migration a full load through OLake Go lands the tables as Iceberg in minutes and can be re-run. For an ongoing feed without redo-log access, keep the same job in incremental mode on an updated_at column. Snowflake Openflow has no Oracle connector; GoldenGate fits when you need log-based capture and have the license. OLake Go fits when the tables should also be readable from DuckDB, Spark or Trino.

Which Oracle data types change on the way to Snowflake?

NUMBER with a scale, FLOAT and BINARY_DOUBLE become Iceberg double and show up as FLOAT, so round at query time when you need exact decimal arithmetic. NUMBER(n,0) becomes int or long. DATE and every TIMESTAMP variant become timestamptz in UTC. CLOB, RAW, BLOB and the INTERVAL types arrive as VARCHAR.

Can I use AWS Glue instead of the Horizon catalog?

Yes. With Glue as the catalog the files stay in your S3 bucket, Athena, Redshift and EMR read them natively, and Snowflake reads them through a catalog integration as read-only tables you refresh after each sync. Horizon is the better default when Snowflake is the main reader, because the tables are Snowflake-managed and need no refresh.

Oracle to BigQuerySame source, read from BigQuery.Oracle to Amazon RedshiftSame source, read from Amazon Redshift.Oracle to DatabricksSame source, read from Databricks.Oracle to Apache IcebergSame source, read from Apache Iceberg.Postgres to SnowflakeSame engine, replicated from Postgres.MySQL to SnowflakeSame engine, replicated from MySQL.MongoDB to SnowflakeSame engine, replicated from MongoDB.SQL Server to SnowflakeSame engine, replicated from SQL Server.DB2 to SnowflakeSame engine, replicated from DB2.MariaDB to SnowflakeSame engine, replicated from MariaDB.Kafka to SnowflakeSame engine, replicated from Kafka.S3 to SnowflakeSame engine, replicated from S3.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: