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Oracle to BigQuery: Replicate via Apache Iceberg

Oracle to BigQuery usually means GoldenGate, a Datastream stream, or a nightly export and bq load. 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 BigLake catalog, Google Cloud's Iceberg representational state transfer (REST) catalog, and keeps them fresh on a cursor column. The files stay in your own Cloud Storage bucket.

BigQuery queries the tables through the catalog with a four-part name, with no dataset to create and no external table to declare. Nothing else is in the path: no GoldenGate, no Datastream, no load job. DuckDB and Spark 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 a BigLake catalog: a full historical snapshot first, then incremental syncs on a cursor column such as UPDATED_AT, with no GoldenGate or Datastream in between. The Oracle connector has no log-based change data capture (CDC), so the cursor is what carries changes.
  • BigQuery reads the catalog as a data source. Every change a sync commits is visible on the next SELECT, with no refresh and no load job.
  • The same tables, written once by OLake Go, are also queryable from DuckDB and Spark. In the run on this page all three engines returned identical rows.

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

How Oracle changes reach BigQuery 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 BigLake. OLake Go uploads the data and delete files to the bucket with the service account's own credentials, then updates the table's metadata pointer in the catalog. BigQuery, DuckDB and Spark 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.
  • A Google Cloud project with the BigLake API enabled, a Cloud Storage bucket for the files, and a service account for OLake Go with the BigLake Editor role on the project and Storage Object User on the bucket. The run on this page used a bucket in us-central1.
  • BigQuery in the same project. Queries run in the bucket's region, and the account running them needs the BigQuery Job User role plus read access to the catalog.
  • OLake UI from the Docker Compose install. The run on this page used OLake Go 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 BigLake catalog​

BigQuery reads Iceberg tables through a catalog. BigLake is the one this page uses: it is Google Cloud's Iceberg REST catalog, OLake Go creates a namespace per job in it, and the files sit in your Cloud Storage bucket.

BigQuery, DuckDB and Spark all read from the same catalog. BigQuery treats it as a data source in the project, so a table OLake Go commits is queryable on the next statement.

CatalogOLake Go writesBigQuery readsVerified on this page
BigLake (Google Cloud Lakehouse)YesYes: native: namespaces map to BigQuery datasetsYes, end to end

BigLake is the Iceberg REST catalog built into Google Cloud's Lakehouse. OLake Go talks to it at https://biglake.googleapis.com/iceberg/v1/restcatalog, the table files land in a Cloud Storage bucket you own, and BigQuery reads the tables without any registration step. Writing to BigLake needs OLake Go 0.9.1 or later.

Everything below happens in the Google Cloud console, in the project that will hold the tables.

  1. Enable the API. Open APIs and services, then Enable APIs and services, and enable the BigLake API. BigQuery and Cloud Storage are on by default in most projects.
  2. Create a bucket. In Cloud Storage, create a bucket in the region where BigQuery will run the queries (us-central1 on this page). The bucket name becomes the catalog name in BigQuery, so pick one you are happy to type in SQL.
  3. Create a service account for OLake Go. In IAM and Admin, Service Accounts, create one (for example olake-biglake). Then grant it two roles: BigLake Editor on the project (IAM, Grant access, the service account as principal) and Storage Object User on the bucket (the bucket's Permissions tab).
  4. Download a JSON key. On the service account's Keys tab, Add key, Create new key, JSON. The file downloads once; OLake Go asks for its contents.
Google Cloud console IAM Grant access panel with the OLake service account as principal and the BigLake Editor role selected
The project-level grant: the service account OLake Go runs as, with the BigLake Editor role.

There is no catalog to create. BigLake creates a single-bucket catalog named after the bucket the first time OLake Go calls the endpoint with gs://<bucket> as the catalog path, in the default end-user credential mode, where every client brings its own storage credentials. The catalog shows up under BigLake, Catalogs after the first connection test.

Two things to know before the first sync:

  • The service account's BigLake Editor role is what lets OLake Go create namespaces and commit tables. Storage Object User on the bucket is what lets it write the Parquet and metadata files. Missing either one fails the connection test with a 403 from biglake.googleapis.com or from storage.googleapis.com, which tells you which grant is missing.
  • Leave the catalog in end-user mode. OLake Go 1.1.0 does not send the X-Iceberg-Access-Delegation header, so a catalog switched to vended credentials rejects its commits (see troubleshooting). Readers that want short-lived tokens from the catalog can switch the mode later with gcloud beta biglake iceberg catalogs update <bucket> --credential-mode=vended-credentials, after setting rest_access_delegation in a CLI config.
Google Cloud console Create private key dialog for the OLake service account with the JSON key type selected
The JSON key for the service account. The console downloads it once; its contents go into the OLake Go destination form.

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 BigLake as the catalog type.
  3. Fill in the fields below. The catalog path is the bucket, with no folder after it. Paste the whole service account JSON file into the credentials field.
  4. Click Create. OLake Go calls the BigLake endpoint, creates the catalog if it is the first contact, and writes and drops a test table in the bucket.

Here is the destination form from the run on this page. It holds the BigLake endpoint, the bucket as the catalog path, the service account JSON and the project ID.

OLake UI Edit destination form for Apache Iceberg with catalog type Big Lake, REST catalog URI https://biglake.googleapis.com/iceberg/v1/restcatalog, BigLake catalog path gs://olake-x2y-lab-bq, authentication type GCP and the project ID oauth-361809
The BigLake destination in OLake UI: the endpoint, the bucket as catalog path, GCP authentication and the project ID.
FieldValue on this pageWhat it is
REST Catalog URIhttps://biglake.googleapis.com/iceberg/v1/restcatalogThe BigLake Iceberg REST endpoint. The same for every project and region.
BigLake Catalog Pathgs://<bucket>The bucket that holds the tables. The bucket name is also the catalog name BigQuery uses. Bucket root only; a folder after it is not supported.
Authentication TypeGCPGoogle OAuth through the service account. The only choice for BigLake.
GCP Service Account JSONthe key file contentsUsed for both the catalog calls and the Cloud Storage writes.
Catalog Nameolake_icebergA label OLake Go uses in its own logs. Lowercase letters and underscores only.
GCP Auth Scopeshttps://www.googleapis.com/auth/cloud-platformThe OAuth scope on the token. Leave the default.
GCP Project IDthe project IDRoutes and bills the catalog calls. Required for multi-bucket catalogs, harmless to set for a single bucket.
Enable Arrow WritesoffKeeps the default Java writer, which is what this page was tested with.

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

This job lists BigQuery, Spark and DuckDB as target query engines. OLake Go then offers only the delete formats all three read, and the run on this page picked Positional Deletes from that list. 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 Google BigQuery, Apache Spark and DuckDB. 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 BigQuery, Spark and DuckDB all read: equality and positional deletes. Deletion vectors are not in the list because BigQuery reads them only in preview.

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_bigquery showing two completed syncs, the first in 32 seconds and the second in 45 seconds
Job history: the 32 s historical snapshot, then the 45 s incremental sync.

On this page the first run copied 8 customers and 40 orders (48 records, 2.24 KB) and finished in 32 seconds, container start included. OLake Go names the BigLake namespace after the job and the source schema, lower-cased, so oracle_to_bigquery on schema SHOP produced oracle_to_bigquery_shop. The Iceberg DB field at the top of the Streams page changes it before the first sync.

Step 6: Query the tables in BigQuery​

There is no registration step and no external table to declare. A BigLake catalog is a BigQuery data source in the same project: the catalog is the bucket name, the namespace OLake Go created is the dataset, and the tables are the tables. Address them with the four-part name in backticks, and run the query in the bucket's region.

BigQuery SQL editor (location: the bucket region)
SELECT id, name, city, tier
FROM `<project-id>.<bucket>.oracle_to_bigquery_shop.customers`
ORDER BY id;

SELECT status, count(*) AS n, round(sum(amount), 2) AS total
FROM `<project-id>.<bucket>.oracle_to_bigquery_shop.orders`
GROUP BY status ORDER BY status;

From a terminal, bq query --project_id=<project-id> --location=<region> --use_legacy_sql=false "<sql>" runs the same statements. Set the location to the bucket's region (us-central1 on this page); the default US multi-region returns Not found: Dataset for a catalog whose bucket is regional.

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.

BigQuery reads Iceberg v2 tables with positional and equality deletes, which is why the job on this page uses positional deletes. Deletion vectors on v3 tables are a preview feature in BigQuery with per-query limits, so keep the job off deletion vectors while BigQuery is a reader.

BigQuery SQL editor in the Google Cloud console running the customers query on the olake-x2y-lab-bq catalog and returning 10 rows with id, name, city and tier
BigQuery reading the customers table through the BigLake catalog after the incremental sync: 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 45 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.

BigQuery 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 and commits the new schema to BigLake in the same commit. BigQuery shows the column on the next query.

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 BigQuery tables. Nothing is exported, copied or refreshed: both engines read the files OLake Go wrote, through the same BigLake catalog.

DuckDB attaches BigLake as an Iceberg REST catalog with a Google access token. The catalog on this page is in end-user credential mode, so DuckDB also needs its own Cloud Storage credentials to read the files: an HMAC key for the service account (Cloud Storage, Settings, Interoperability, Create a key for a service account).

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

-- gcloud auth print-access-token, or a token minted for the service account
CREATE SECRET biglake_catalog (
TYPE iceberg,
TOKEN '<access token>',
EXTRA_HTTP_HEADERS MAP {'x-goog-user-project': '<project-id>'}
);
-- the HMAC key pair for the bucket
CREATE SECRET biglake_files (TYPE gcs, KEY_ID '<hmac access id>', SECRET '<hmac secret>');

ATTACH 'gs://<bucket>' AS biglake (
TYPE iceberg,
ENDPOINT 'https://biglake.googleapis.com/iceberg/v1/restcatalog',
SECRET biglake_catalog
);

SHOW ALL TABLES;
SELECT id, name, city, tier FROM biglake.oracle_to_bigquery_shop.customers ORDER BY id;

The access token lives an hour; CREATE OR REPLACE SECRET with a fresh one when a query fails with 401. If the catalog is in vended-credentials mode instead, drop the gcs secret and add ACCESS_DELEGATION_MODE 'vended_credentials' to the ATTACH: BigLake then hands DuckDB a short-lived storage token per table. DuckDB reads positional deletes, so the rows match BigQuery after every sync.

Google Cloud console Cloud Storage Interoperability settings with the Select a service account dialog, the OLake service account selected and the Create key button
The key for DuckDB: Cloud Storage, Settings, Interoperability, then a key for the same service account OLake Go uses.

DuckDB 1.5.5 returned the same 10 customers and the same orders roll-up as BigQuery in the run on this page. It read the current metadata file of each table through the catalog and the data files through the hash-based message authentication code (HMAC) key.

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 BigQuery​

OLake Go maps Oracle types to Iceberg types, and BigQuery maps Iceberg types to its own when it reads the catalog. Numbers are the group to watch.

OracleIceberg (written by OLake Go)BigQuery showsNotes
NUMBER(n,0)longINT64The NUMBER(10) identity keys on this page arrived as INT64.
NUMBER(n,m) with a scale, FLOAT, BINARY_DOUBLEdoubleFLOAT64OLake 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_FLOATfloatFLOAT64
DATE, TIMESTAMP, TIMESTAMP WITH TIME ZONE, TIMESTAMP WITH LOCAL TIME ZONEtimestamptz (UTC)TIMESTAMPOracle DATE carries a time; it arrives as a UTC instant.
VARCHAR2, NVARCHAR2, CHAR, CLOB, NCLOB, LONGstringSTRING
RAW, LONG RAW, BLOB, BFILE, INTERVAL typesstringSTRINGBinary values arrive as text; keep large binaries out of the sync.

Oracle types in the table come from the Oracle connector reference. The BigQuery side comes from the BigQuery Iceberg tables page (checked 2026-10-30) and from the table schemas of this run.

Cost and maintenance​

  • Storage is your bucket: you pay Cloud Storage rates for the Parquet and delete files. The BigLake catalog itself has no per-table charge.
  • Compute. OLake Go runs wherever you run Docker. BigQuery bills on-demand queries by bytes scanned, or by slot time on an edition; a query over the catalog reads the Parquet files in the bucket, so filter on the columns you need. DuckDB and Spark cost whatever machine they run on.
  • 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. A table that gets a new small file and delete file per sync wants compaction, and BigLake keeps every snapshot OLake Go commits in the table's metadata.json. BigLake rejects a metadata.json above 1 MB, which a busy table reaches after a few thousand syncs. Expire old snapshots and compact on a schedule with OLake Fusion or with Spark's expire_snapshots and rewrite_data_files procedures through the same catalog.
  • Key rotation. The service account key is what OLake Go writes with. Rotate it on your schedule from the service account's Keys tab and paste the new JSON into the destination.

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 catalog in vended-credentials mode​

failed to test destination: failed to create or get table: rpc error: code = Internal desc = Failed to process request: Malformed request: X-Iceberg-Access-Delegation header must be present and contain `vended-credentials` when credential mode is `CREDENTIAL_MODE_VENDED_CREDENTIALS`.

The catalog was created, or switched, with --credential-mode=vended-credentials, and OLake Go 1.1.0 does not send the header BigLake wants in that mode (olake#1369). Switch the catalog back with gcloud beta biglake iceberg catalogs update <bucket> --credential-mode=end-user, which is the default for a catalog OLake Go created itself. With the CLI, "rest_access_delegation": "vended-credentials" in destination.json makes the same test pass; the UI has no field for it yet.

5. BigQuery cannot find the dataset​

Not found: Dataset <project>:<bucket>.oracle_to_bigquery_shop was not found in location US

The query ran in a location other than the bucket's region. Set the query location to the bucket's region in the editor's More, Query settings panel, or pass --location=<region> to bq. Leaving the location unset lets BigQuery pick it from the table.

How this compares with other ways to move Oracle data to BigQuery
MethodFreshnessUpdates and deletesWhere the data landsWhat you run
Datastream for OracleNear real time, by Google's description; CDC from the redo logYesNative BigQuery datasets; BigQuery is the only readerNothing to run; a connection profile and a stream in the console, plus supplemental logging on Oracle
Oracle GoldenGate to BigQuerySecondsYesNative BigQuery tablesGoldenGate Extract and Replicat, a licensed Oracle option
Export (Data Pump or SELECT to CSV), then bq loadHours to dailyNo; full reloads onlyNative BigQuery tablesA script, a scheduler and a staging bucket
Managed ELT (Fivetran, Estuary, Airbyte Cloud)Minutes, by planVaries by toolBigQuery tables, a second copy you pay to move and storeNothing; priced per row, per GB or per connector
OLake Go to Iceberg on BigLake (this guide)Incremental on a schedule down to one minuteInserts and updates through the cursor; no deletesApache Iceberg tables that BigQuery, Spark, DuckDB, Trino and Flink all readOne Docker Compose stack, open source; your bucket plus your own compute

Datastream is the simpler choice when you need deletes replicated and BigQuery is the only reader. Writing Iceberg once avoids paying to move and store a second copy. That matters as soon as Spark, DuckDB 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 BigQuery speed. A catalog table is read from Parquet in the bucket 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 BigQuery without GoldenGate or Datastream?

Run OLake Go (open source, Apache 2.0) against the Oracle database with a read-only user, and point it at a BigLake catalog with a Cloud Storage 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. BigQuery queries the tables through the catalog with a four-part name. GoldenGate, Datastream, Kafka and a 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, Datastream for Oracle 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 BigQuery read Iceberg tables with row-level deletes?

Yes. BigQuery reads Iceberg v2 merge-on-read tables with positional and equality deletes. The run on this page used positional deletes and BigQuery returned one row per key after the updates. Deletion vectors on Iceberg v3 tables are a preview feature with per-query limits, so keep the job on positional deletes while BigQuery is a reader.

Do I need to create a BigQuery dataset or an external table?

No. A BigLake catalog is a data source BigQuery reads directly: the bucket name is the catalog, the namespace OLake Go creates is the dataset, and the tables appear under it. The query uses the four-part name project.bucket.namespace.table, and there is no CREATE EXTERNAL TABLE and no metadata refresh.

Which Oracle data types change on the way to BigQuery?

NUMBER columns with a scale become Iceberg double and show up in BigQuery as FLOAT64, so round at query time when you need exact decimal arithmetic. Whole-number NUMBER columns become INT64. DATE and every TIMESTAMP variant become TIMESTAMP in UTC. VARCHAR2, CLOB and the binary types arrive as STRING.

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, Oracle on Google Cloud 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 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 BigQuerySame engine, replicated from Postgres.MongoDB to BigQuerySame engine, replicated from MongoDB.MySQL to BigQuerySame engine, replicated from MySQL.Kafka to BigQuerySame engine, replicated from Kafka.MariaDB to BigQuerySame engine, replicated from MariaDB.S3 to BigQuerySame engine, replicated from S3.DB2 to BigQuerySame engine, replicated from DB2.SQL Server to BigQuerySame engine, replicated from SQL Server.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.

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