How to Use BigQuery Data Types in PostgreSQL

How do BigQuery data types work and how can I map them to PostgreSQL?

BigQuery data types define how Google BigQuery stores, processes, and validates each column’s values.

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Description

What are the core BigQuery data types?

BigQuery supports primitive (INT64, FLOAT64, NUMERIC, BIGNUMERIC, BOOL, STRING, BYTES), date-time (DATE, DATETIME, TIME, TIMESTAMP), and complex (ARRAY, STRUCT, GEOGRAPHY, JSON) types. Each type controls storage format, valid operations, and function compatibility.

Why map BigQuery types to PostgreSQL?

Teams often stage data in PostgreSQL before loading to BigQuery or query BigQuery from Postgres FDW. Aligning types avoids truncation, rounding, or cast failures during ETL and federated queries.

How do common BigQuery types translate to PostgreSQL?

INT64 → BIGINT, FLOAT64 → DOUBLE PRECISION, NUMERIC → NUMERIC(38,9), STRING → TEXT, BOOL → BOOLEAN, BYTES → BYTEA, DATE/TIME/TIMESTAMP map to their PostgreSQL counterparts, GEOGRAPHY → GEOGRAPHY (PostGIS), JSON → JSONB.

Which syntax declares BigQuery-compatible columns?

Use CREATE TABLE or ALTER TABLE in PostgreSQL with matching types. When loading with the BigQuery postgres_translator tool, supply a JSON schema that mirrors BigQuery’s definitions.

Example: can I store prices exactly?

Yes—declare NUMERIC(10,2) in PostgreSQL to mirror BigQuery NUMERIC for currency. This prevents floating-point drift when transferring order totals.

How to handle arrays and structs?

PostgreSQL has native ARRAY and composite types. Alternatively, serialize to JSONB for flexibility, then unpack with -> operators when querying.

What’s the best practice for timestamps?

Store all times in UTC TIMESTAMP WITH TIME ZONE in PostgreSQL. BigQuery TIMESTAMP is always UTC, so this avoids implicit shifts during export/import.

How do I load PostgreSQL data into BigQuery safely?

1) Cast columns to their final BigQuery equivalents in SELECT. 2) Export to CSV/Avro/Parquet. 3) Supply an explicit BigQuery schema matching those types. Validate with bq load --autodetect=false.

Are there performance considerations?

Choose the narrowest type that still holds all values. Smaller columns speed scans in both engines. For example, store stock as INT64 not NUMERIC.

What tools automate type mapping?

open-source pg2bq, Airbyte, and Fivetran generate BigQuery schemas from PostgreSQL catalogs, flagging mismatches before runtime errors occur.

Why How to Use BigQuery Data Types in PostgreSQL is important

How to Use BigQuery Data Types in PostgreSQL Example Usage


-- Find orders with JSON array length > 3 after import into BigQuery
SELECT id, total_amount
FROM   `myproject.ecommerce.Orders`
WHERE  ARRAY_LENGTH(JSON_QUERY_ARRAY(items)) > 3;

How to Use BigQuery Data Types in PostgreSQL Syntax


-- PostgreSQL table mirroring BigQuery types
CREATE TABLE orders_bq (
    id            BIGINT PRIMARY KEY,          -- INT64
    customer_id   BIGINT NOT NULL,             -- INT64
    order_date    DATE NOT NULL,               -- DATE
    total_amount  NUMERIC(10,2) NOT NULL,      -- NUMERIC
    items         JSONB,                       -- ARRAY<STRUCT>
    created_at    TIMESTAMP WITH TIME ZONE DEFAULT now(),  -- TIMESTAMP
    shipped       BOOLEAN DEFAULT FALSE        -- BOOL
);

-- Export to BigQuery via SELECT casting
COPY (
    SELECT id,
           customer_id,
           order_date,
           total_amount,
           items::text        AS items_json,  -- BigQuery JSON
           created_at,
           shipped
    FROM   orders_bq
) TO '/tmp/orders.csv' WITH CSV;

Common Mistakes

Using FLOAT64 for currency. Floating-point math causes rounding errors; use NUMERIC in BigQuery and NUMERIC(precision, scale) in PostgreSQL.
Ignoring time zones. BigQuery TIMESTAMP is UTC, but PostgreSQL TIMESTAMP without time zone assumes server offset. Always use TIMESTAMPTZ.