How to Self-Host BigQuery in PostgreSQL

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How do I self-host BigQuery capabilities inside PostgreSQL?

Self-hosting BigQuery recreates BigQuery-style analytics by connecting PostgreSQL to Google BigQuery through the bigquery_fdw extension so you can query cloud data locally and join it with on-prem tables.

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Description

Table of Contents

What does self-hosting BigQuery in PostgreSQL mean?

It means installing the open-source bigquery_fdw extension so your Postgres instance can treat BigQuery datasets as foreign tables. You run SQL from Postgres, while execution happens in BigQuery and results stream back.

Why choose this approach?

You keep Postgres workflows and permissions, avoid exporting data, lower egress costs by streaming only the result set, and freely join BigQuery data with local tables such as Customers or Orders.

How do I install bigquery_fdw?

Build the extension from source or use a package manager (apt install postgresql-16-bigquery-fdw). Then run CREATE EXTENSION bigquery_fdw; inside the target database.

Which server options are required?

Create a foreign server with project_id, dataset_id, and optional location. Map a service-account JSON key with CREATE USER MAPPING so the FDW can authenticate.

How do I import BigQuery tables?

Use IMPORT FOREIGN SCHEMA to pull table definitions into the local public schema. PostgreSQL stores metadata only—data remains in BigQuery until queried.

How can I query BigQuery data?

Query foreign tables exactly like native tables. Joins, filters, and aggregates push down to BigQuery when possible, returning only final rows.

Example join question

SELECT c.name, SUM(oi.quantity * p.price) AS spend FROM Customers c JOIN Orders@bigquery o ON o.customer_id = c.id JOIN OrderItems@bigquery oi ON oi.order_id = o.id JOIN Products p ON p.id = oi.product_id GROUP BY c.name;

What are key performance best practices?

Select only needed columns, push filters early, and avoid CROSS JOINs. Use EXPLAIN VERBOSE to verify that predicates are being pushed down to BigQuery.

What common mistakes should I avoid?

First, storing service-account keys inside SQL scripts—use a secure path or Vault. Second, forgetting to cast Postgres types to match BigQuery, which prevents predicate pushdown.

How do I monitor FDW queries?

Check pg_stat_foreign_server for latency and rows, enable BigQuery audit logs, and log fdw_explain_verbose to understand remote SQL.

Why How to Self-Host BigQuery in PostgreSQL is important

How to Self-Host BigQuery in PostgreSQL Example Usage


-- Lifetime value per customer pulling Orders from BigQuery
SELECT c.name,
       SUM(oi.quantity * p.price) AS lifetime_value
FROM   Customers      c
JOIN   Orders         o  ON o.customer_id = c.id          -- foreign table
JOIN   OrderItems     oi ON oi.order_id    = o.id         -- foreign table
JOIN   Products       p  ON p.id           = oi.product_id
GROUP  BY c.name
ORDER  BY lifetime_value DESC;

How to Self-Host BigQuery in PostgreSQL Syntax


-- 1️⃣ Enable the foreign-data wrapper
CREATE EXTENSION IF NOT EXISTS bigquery_fdw;

-- 2️⃣ Register the BigQuery project as a foreign server
CREATE SERVER bigquery_srv
    FOREIGN DATA WRAPPER bigquery_fdw
    OPTIONS (
        project_id 'my-gcp-project',
        dataset_id 'ecommerce',
        location   'US'
    );

-- 3️⃣ Map credentials securely (service-account JSON key)
CREATE USER MAPPING FOR CURRENT_USER
    SERVER bigquery_srv
    OPTIONS (
        service_account '/etc/keys/bigquery-sa.json'
    );

-- 4️⃣ Import tables into Postgres schema
IMPORT FOREIGN SCHEMA ecommerce
    FROM SERVER bigquery_srv
    INTO public;

-- 5️⃣ Query as if native
SELECT * FROM orders LIMIT 10;

Common Mistakes

Frequently Asked Questions (FAQs)

Is bigquery_fdw open source?

Yes, it is licensed under Apache-2.0 and maintained on GitHub. You can audit or extend the code.

Can I write to BigQuery from PostgreSQL?

bigquery_fdw currently supports INSERT, but not UPDATE or DELETE. Use BigQuery streaming inserts for large loads.

Does the FDW support parameterized queries?

Yes. Parameters in prepared statements are pushed down, letting BigQuery filter data server-side and minimize network transfer.

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