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Postgres to BigQuery: CDC via Apache Iceberg

Postgres to BigQuery usually means a Datastream stream, a Debezium connector into Kafka, or a nightly export and bq load, and tables that only BigQuery can read. This guide takes a different route.

OLake Go reads the Postgres write-ahead log (WAL) and commits the changes as Apache Iceberg tables into a BigLake catalog, Google Cloud's Iceberg representational state transfer (REST) catalog. 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 Datastream, no Kafka, 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 Postgres into Iceberg tables in a BigLake catalog: a full historical snapshot first, then change data capture (CDC) from the WAL, with no Datastream or Kafka in between.
  • 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 Postgres to BigQuery with change data capture (CDC), run OLake Go with a Postgres replication slot as the source. Point it at a BigLake catalog with a Cloud Storage bucket as the Iceberg destination, and pick BigQuery as the target query engine.

The first sync copies each table in full (the historical snapshot); every later sync replays inserts, updates and deletes from the WAL. BigQuery, DuckDB and Spark then query the same Iceberg tables, with no second copy.

How Postgres CDC reaches BigQuery here​

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

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

Each sync ends with one commit to 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 replication slot pins WAL on the Postgres server until OLake Go has read it. A job that is paused for days makes the WAL grow by everything written since. Keep the job running on a schedule, and alert on pg_replication_slots lag if disk is tight.

What you need​

  • Postgres 12 or later with wal_level = logical and a user that can read the WAL. The tables on this page ran on Postgres 16. Hosted Postgres on Cloud SQL, AlloyDB, Amazon Relational Database Service (RDS), Aurora, Azure, Neon or Supabase works the same way; step 1 links the setup guides.
  • 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 Postgres for CDC​

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

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

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

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

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

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

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

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

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

Check the result before moving on:

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

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

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

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

Step 2: Set up the 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 Postgres to OLake Go​

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

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

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

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

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

Step 4: Configure the Iceberg destination​

  1. Open Destinations and click Create Destination.
  2. Pick Apache Iceberg as the connector and 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.

  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 + CDC and the ingestion mode to Upsert. Leave the Upsert Type as OLake Go resolved it (Positional Deletes in the run on this page).
  6. Click Create Job, then open the job's menu and choose Sync Now for the first run.

OLake Go offers every delete format 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 public schema, customers and orders selected, sync mode Full Refresh + CDC, ingestion mode Upsert and upsert type Positional Deletes on Iceberg format version V2
Each stream: Full Refresh + CDC, Upsert, and Positional Deletes as the upsert type. The Iceberg DB name at the top is editable.
OLake UI Job Logs and History for postgres_to_bigquery showing two completed syncs, the first in 35 seconds and the second in 58 seconds
Job history: the 35 s historical snapshot, then the 58 s change-only sync.

On this page the first run copied 8 customers and 40 orders (48 records, 2.23 KB) and finished in 35 seconds, container start included. OLake Go names the BigLake namespace after the job, the source database and the schema, so postgres_to_bigquery on database shop produced postgres_to_bigquery_shop_public. The Iceberg DB field at the top of the Streams page changes it before the first sync.

Step 6: Query the tables in 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>.postgres_to_bigquery_shop_public.customers`
ORDER BY id;

SELECT status, count(*) AS n, round(sum(amount), 2) AS total
FROM `<project-id>.<bucket>.postgres_to_bigquery_shop_public.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 change sync: 10 rows, one per key.

Step 7: Change rows in Postgres and sync again​

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

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

The second sync read 12 records (594 bytes) from the slot and finished in 58 seconds end to end, most of it the wait for the WAL reader to confirm the slot was idle. For customers that meant one new data file with 4 rows: 2 inserts plus the new versions of 2 updated rows.

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

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

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.

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

Schema changes. When a column is added in Postgres, OLake Go adds it to the Iceberg table on the next sync and commits the new schema to BigLake in the same commit. BigQuery shows the column on the next query.

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

Query the same tables from 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.postgres_to_bigquery_shop_public.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 Postgres to the cent in all three engines. It gave 16 delivered orders for 8659.68, 18 placed for 10522.26, 11 shipped for 6397.25.

Data types: Postgres to Iceberg to BigQuery​

OLake Go maps Postgres types to Iceberg types, and BigQuery maps Iceberg types to its own when it reads the catalog. Most columns pass through unchanged; three groups are worth knowing about.

PostgresIceberg (written by OLake Go)BigQuery showsNotes
integer, serialintINT64
bigint, bigseriallongINT64
numeric(p,s), real, double precisiondoubleFLOAT64OLake Go stores decimals as doubles. The orders.amount sums on this page matched Postgres to the cent in every engine; round at query time when you need exact decimal arithmetic.
timestamptz, timestamptimestamptz (UTC)TIMESTAMPBigQuery shows the UTC instant; timestamp has no zone in Postgres and is read as UTC.
varchar, text, uuid, json, jsonbstringSTRINGParse JSON with PARSE_JSON or JSON_VALUE in BigQuery.
booleanbooleanBOOL
byteabinaryBYTES

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

Cost and maintenance​

  • Storage is your bucket: you pay 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.
  • Maintenance. A CDC 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.
  • WAL disk. The replication slot keeps WAL until OLake Go reads it. A job paused for a weekend on a busy server can hold tens of gigabytes. Drop the slot if you retire a job.
  • Key rotation. The 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 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.

2. BigQuery cannot find the dataset​

Not found: Dataset <project>:<bucket>.postgres_to_bigquery_shop_public 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.

3. INFORMATION_SCHEMA rejects the four-part name​

Invalid project ID '<project>.<bucket>'. Project IDs must contain 6-63 lowercase letters, digits, or dashes.

INFORMATION_SCHEMA views take a dataset-qualified name and do not resolve catalog tables. Use bq show --schema <project>:<bucket>.<namespace>.<table> to list the columns and their types instead.

4. The next sync waits on the slot​

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

How this compares with other ways to move Postgres data to BigQuery
MethodFreshnessUpdates and deletesWhere the data landsWhat you run
Datastream for BigQueryNear real time, by Google's descriptionYesNative BigQuery datasets; BigQuery is the only readerNothing to run; a connection profile and a stream in the console
Debezium and Kafka with a BigQuery sink connectorSecondsYes, with the connector's upsert modeNative BigQuery tablesKafka, Kafka Connect, Debezium and the sink connector
Export and bq load on a scheduleHours to dailyNo; full reloads or your own mergeNative 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)CDC on a schedule down to one minuteYes, as Iceberg row-level 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 BigQuery is the only reader and a managed Google service is the preference. 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​

  • 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.
  • Tables have no primary key. Inserts replicate; updates and deletes cannot be matched to a row and are not applied. Add a primary key or set REPLICA IDENTITY FULL first.
  • You want deletion vectors. BigQuery reads them only in preview, with a cap of 100,000 deletion vector entries per query. Keep the job on positional deletes while BigQuery is a reader.

FAQs​

How do I replicate Postgres to BigQuery without Datastream?

Run OLake Go (open source, Apache 2.0) against a Postgres database with logical replication on, and point it at a BigLake catalog with a Cloud Storage bucket. The first sync copies the tables in full, every later sync replays only the changes from the write-ahead log, and BigQuery queries the tables through the catalog with a four-part name. No Datastream stream, no Kafka and no load job sit in the path.

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.

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

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

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.

Why Iceberg on BigLake rather than loading BigQuery tables?

An Iceberg table in a bucket you own is one copy that BigQuery, Spark, DuckDB, Trino and Flink all read, with no load job and no export. Native BigQuery tables are faster to query but make BigQuery the only reader, and a second engine means a second copy. Keep the Iceberg tables as the shared source and load a hot slice into a native table when a dashboard needs it.

Which Postgres data types change on the way to BigQuery?

numeric columns become Iceberg double and show up in BigQuery as FLOAT64, so round at query time when you need exact decimal arithmetic. timestamptz becomes TIMESTAMP in UTC. integer and bigint become INT64, varchar and text become STRING, and json and jsonb arrive as STRING, which PARSE_JSON turns back into JSON.

What does OLake Go need on the Postgres side?

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

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

Deeper reading on this site: