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Google BigQuery

Data Connector

Connect PostgreSQL to Google BigQuery

Extract tables from PostgreSQL databases, transform and validate records, and load structured data directly into Google BigQuery — without writing pipeline code. Dagflux handles schema detection, incremental syncs, data quality gates, and warehouse loading from a visual canvas.

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PostgreSQL → BigQuery Pipeline — orders + customers

Completed

PostgreSQL

orders

312,840 rows

PostgreSQL

customers

84,210 rows

Join

Orders + Customers

LEFT JOIN on customer_id

Transform

Normalize + Enrich

types, dates, new columns

Branch

Quality Gate

validate schema + nulls

BigQuery

analytics.orders_view

warehouse output

6 nodes — 5 connections

312,840 ordersextracted

customer joinLEFT JOIN

Quality gate99.8% passed

BigQuery load312,214 rows

Completed in 22.1s

PostgreSQL to BigQuery

Move PostgreSQL data into BigQuery for analytics and reporting

PostgreSQL is a trusted operational database for applications, CRMs, and internal tools. Google BigQuery is built for large-scale analytics. Dagflux bridges the two — letting you extract tables from PostgreSQL, transform and validate records, and load structured, query-ready datasets into BigQuery without writing custom pipeline scripts.

How it works

From PostgreSQL tables to BigQuery datasets in three steps

Dagflux uses a visual node-based canvas to build the PostgreSQL to BigQuery pipeline. Connect your source, describe the transformation, validate, and load.

Step 01

Connect your PostgreSQL database

Add a Data Source node for your PostgreSQL instance. Dagflux detects available schemas, table names, column types, and row counts automatically. Select one or more tables to include in the pipeline.

Step 02

Transform and join records

Use Join nodes to combine related tables across schemas. Then describe transformations in plain English — type casting, date formatting, column renaming, calculated fields, or filtering — and review the generated SQL before it runs.

Step 03

Validate and load into BigQuery

Add a Branch node to check required fields, null rates, row counts, and schema before loading. Passing rows go to your BigQuery dataset; failing rows route to a quarantine output for review.

Transformer Node

Transform PostgreSQL records before loading into BigQuery

Raw PostgreSQL tables often need cleaning before they're useful in an analytics warehouse. Dagflux generates SQL transformations from plain English — you review the logic, refine it with follow-up prompts, and approve before execution.

Type casting and date normalization

Convert PostgreSQL timestamp types, numeric fields, and boolean columns into BigQuery-compatible formats — including ISO 8601 dates and correct INTEGER or FLOAT64 types.

Column selection and renaming

Select specific columns, rename fields to match your BigQuery naming conventions, exclude internal system columns, and add derived fields calculated from existing data.

Review before execution

Every generated SQL statement is displayed before it runs. Edit it directly or refine it with a follow-up prompt — no changes happen until you approve.

TRANSFORM — Normalize + Enrich — Configured

Loaded joined dataset with 312,840 rows. Columns include order_id, customer_id, created_at, status, total_amount, email, region, and others. What transformation do you need?

You

Cast created_at to DATE, rename total_amount to order_total, uppercase the status field, and add a year_month column from created_at.

Generated. Casts date, renames total_amount, uppercases status, and adds year_month derived column.

SELECT order_id, customer_id, CAST(created_at AS DATE) AS created_at, UPPER(status) AS status, total_amount AS order_total, FORMAT_DATE('%Y-%m', created_at) AS year_month, email, region FROM orders_customers_joined

You

Also exclude rows where order_total is null or less than zero.

Added WHERE total_amount IS NOT NULL AND total_amount >= 0. 626 rows excluded. Ready to review.

Branch Node

Validate data quality before loading into BigQuery

A failed BigQuery load — missing fields, wrong types, unexpected nulls — is costly to fix after the fact. The Branch node runs validation checks before the output step, so only clean rows reach your warehouse.

Schema and type validation

Check that output columns match the expected BigQuery schema — correct names, data types, and no unexpected nulls in required fields before loading begins.

Row count and completeness checks

Set expected row count ranges, minimum completeness thresholds for key columns, and alert conditions — so pipeline anomalies surface before data reaches dashboards.

Quarantine path for failed rows

Rows that fail validation route to a separate quarantine output — a CSV, staging table, or disconnected path — keeping your BigQuery dataset clean while preserving failed records for review.

Branch — Quality Gate — result

Validation Steps

OKorder_id present and non-null

OKcreated_at cast to DATE successfully

OKorder_total ≥ 0 for 99.8% of rows

OKRow count within expected range

VALID PATH

312,214

rows to BigQuery

REVIEW PATH

626

rows to quarantine

Validation passed — loading to BigQuery

PostgreSQLAuto-detected schemas, tables, columns, and types

BigQueryLoad to any dataset and table with schema mapping

BranchValidation gates before every warehouse load

No SQLDescribe transformations in plain English

Why Dagflux

PostgreSQL to BigQuery pipelines with full visibility

Dagflux gives data, analytics, and engineering teams a reviewable, configurable pipeline from PostgreSQL to BigQuery. Every transformation is visible as SQL, every validation rule is configurable, and every run produces logs with row counts, duration, and error details.

Speed

Build pipelines faster

Create a working PostgreSQL to BigQuery pipeline without manually authoring every extraction query, JOIN, transformation step, and load script.

Control

Review every generated query

Inspect the SQL generated for each transformation — selected columns, filters, type casts, and joins — before any data is moved or changed.

Quality

Catch data issues before BigQuery

Use Branch nodes to validate required fields, type compatibility, null rates, and row counts before the load step runs.

Use cases

PostgreSQL to BigQuery workflows built with Dagflux

Analytics

Operational data to analytics warehouse

Move application database tables — orders, users, events, subscriptions — from PostgreSQL into BigQuery for reporting, dashboarding, and self-serve analytics.

Data Modeling

Prepare dimensional models

Join fact and dimension tables from PostgreSQL, apply transformations, and load clean, validated datasets ready for dbt models or BI tool connections in BigQuery.

Migration

Migrate historical data

Extract full historical snapshots from PostgreSQL, normalize schemas, and load structured data into BigQuery as a migration or audit archive.

Incremental Sync

Regular scheduled syncs

Schedule recurring pipeline runs to incrementally sync new or updated PostgreSQL rows to BigQuery on hourly, daily, or custom cron schedules.

Multi-source

Combine PostgreSQL with other sources

Add CSVs, JSON files, or other database tables alongside PostgreSQL sources and join them before loading the combined dataset into BigQuery.

Quality

Audit and validate before loading

Use Branch nodes to enforce schema compliance, check referential integrity, and quarantine bad rows before they reach production BigQuery tables.

More connectors

Connect BigQuery to other data sources

Dagflux supports multiple source types alongside PostgreSQL. Add CSV exports, JSON files, MongoDB collections, or other databases and join them with your PostgreSQL tables before loading into BigQuery.

PostgreSQL

Extract from any schema or table with auto-detected columns and types.

CSV / Excel

Add flat file exports alongside database sources and join on shared keys.

MongoDB

Pull documents from MongoDB collections and combine with structured tables.

MySQL

Source from MySQL databases and merge with PostgreSQL tables in one pipeline.

Snowflake

Pull from Snowflake warehouse tables and load transformed outputs into BigQuery.

Amazon S3

Read Parquet, CSV, or JSON files from S3 and join with PostgreSQL sources.

FAQ

PostgreSQL to BigQuery FAQ

You add a Data Source node and provide your PostgreSQL connection details — host, port, database name, username, and password. Dagflux connects, lists available schemas and tables, and detects column names, data types, and row counts automatically.

You add an Output node configured for BigQuery and specify the project, dataset, and target table. Dagflux maps the transformed output schema and loads validated rows into the specified BigQuery table. You can choose to append, replace, or merge on each run.

Yes. You can add multiple Data Source nodes pointing to different PostgreSQL tables or schemas and connect them through Join nodes before the Transform and Output steps.

No. You describe the transformation in plain English — for example, "cast created_at to DATE, rename total_amount to order_total, and exclude rows where status is null" — and Dagflux generates the SQL. Technical users can still review and edit the generated SQL directly.

Yes. The Transform node can cast PostgreSQL types — including timestamps, numeric types, booleans, and arrays — into BigQuery-compatible equivalents. You can describe the required type mappings in plain English or review and adjust the generated SQL directly.

Yes. Pipelines can be scheduled to run hourly, daily, weekly, or on a custom cron expression. Each run produces execution logs with row counts, duration, validation results, and error messages.

Rows that fail the Branch node validation can be routed to a separate output — such as a quarantine CSV, staging table, or a disconnected stop path. This keeps your production BigQuery table clean while preserving failed rows for review.

Yes. You can add multiple source types in the same pipeline — PostgreSQL tables, CSV exports, JSON files, MongoDB collections, or other databases — and join them before the transform and BigQuery output steps.

Build your PostgreSQL to BigQuery pipeline

Connect your PostgreSQL database, describe the transformation, validate the output, and load structured data into Google BigQuery.

Start for free Book a demo

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