PostgreSQL, also known as Postgres, is an advanced object-relational database management system (ORDBMS) used for data storage, retrieval, and management. It is available on the Azure platform in a PaaS model (Platform as a Service) through the Azure Database for PostgreSQL service. 

Azure Postgres automates several tasks related to relational databases. However, it has low scalability and does not perform well when dealing with large volumes of data. To avoid inefficiency, you can integrate your data from Azure Postgres to BigQuery, which has high scalability at the petabyte level. 

This article explains two methods for Azure Postgres BigQuery integration to conduct complex analysis of high-volume data. 

Why Integrate Azure Postgres to BigQuery??

You should integrate your data from Azure Postgres to BigQuery for the following reasons:

  • BigQuery is serverless, so you do not have to worry about setting up or maintaining the infrastructure. 
  • The columnar storage format of BigQuery eliminates unnecessary data storage, reducing storage costs and increasing querying speed
  • You can monitor the access control in BigQuery at the project and database level. This ensures data security
  • BigQuery is a budget-friendly tool as it charges you according to your usage. 
  • The columnar storage format of BigQuery eliminates unnecessary data storage, reducing storage costs and increasing querying speed. This is not feasible in Azure Postgres as it has a row-based storage format. 
  • You can monitor the access control in BigQuery at the project and database level. This ensures data security.

Overview of Azure Postgres

Azure Postgres or Azure Database for PostgreSQL is a fully managed relational database service that offers high availability, scalability, and security features. It has three deployment options: single-server, flexible server, and hyperscale

Some key features of Azure Postgres are:

  • Managed Service: Microsoft Azure handles infrastructure provisioning, patching, security updates, and backups. This lets you focus on application development without worrying about the underlying infrastructure.
  • Built-in High Availability: It provides built-in high availability with no additional setup, configuration, or cost. 
  • Security: All data in Azure Postgres, including backups, is encrypted on disk by default. It also has Secure Sockets Layer (SSL) enabled by default, so all data in transit is encrypted. 
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Overview of Google BigQuery

BigQuery is a serverless and fully managed enterprise data warehouse. It can enable fast and scalable SQL querying of massive amounts of data. 

Here are some key features of Google BigQuery:

  • Built-in ML Integration: BigQuery ML creates and executes Machine Learning models in BigQuery using simple SQL queries, allowing you to build ML models without much expertise.
  • Multi-Cloud Functionality: BigQuery Omni allows you to analyze data across multiple cloud storages. It uses the BigQuery interface and SQL queries to do so.
  • Geospatial Analysis: BigQuery Geographic Information Systems (GIS) provides information about location and mapping by converting latitude and longitude columns into geographical points.

Methods to Integrate Azure Postgres to BigQuery

Method 1: Using Hevo Data to integrate Azure Postgres to BigQuery

Method 2: Using CSV file to integrate Azure Postgres to BigQuery

Method 1: Using Hevo Data to Integrate Azure Postgres to BigQuery

Hevo Data is a no-code ELT platform that provides real-time data integration and a cost-effective way to automate your data pipeline workflow. With over 150 source connectors, you can integrate your data into multiple platforms, conduct advanced analysis on your data, and produce useful insights.

Here are some of the most important features provided by Hevo Data:

  • Data Transformation: Hevo Data provides you the ability to transform your data for analysis with a simple Python-based drag-and-drop data transformation technique.
  • Automated Schema Mapping: Hevo Data automatically arranges the destination schema to match the incoming data. It also lets you choose between Full and Incremental Mapping.
  • Incremental Data Load: It ensures proper bandwidth utilization at both the source and the destination by allowing real-time data transfer of the modified data.

You can use Hevo to automatically sync Azure Postgres to BigQuery by following the steps below: 

Step 1: Configuration of Azure Postgres as Source


  • Ensure the availability of the IP address or hostname of your PostgreSQL database instance. Use Postgres version 9.5 or higher
  • If you use Logical Replication as Pipeline mode, enable Log-based incremental replication
  • Whitelist Hevo’s IP addresses. Grant SELECT, USAGE, and CONNECT privileges to the database user.
  • Retrieve the Database Hostname and Port Number of the source instance.
  • Ensure you are assigned the Team Administrator, Team Collaborator, or Pipeline Administrator role in Hevo to create the Pipeline.

Follow the steps mentioned below to configure Azure Postgres as a source:

  • In the Navigation Bar, Click PIPELINES.
  • Click + CREATE in the Pipelines List View.
  • Select Azure Postgres from the Select Source Type page.
  • On the Configure your Azure Postgres Source page, enter the mandatory details. 
  • Click the Test & Continue button to complete the source setup.
Azure Postgres to BigQuery: Configure Source Settings
Configure Source Settings

For more information on the configuration of Azure Postgres as a source in Hevo, refer to Hevo documentation.  

Step 2: Configuration of Google BigQuery as Destination


  • Create a Google Cloud Project if you do not have one already. 
  • Assign the essential roles for the GCP project to the connecting Google account in addition to the Owner or Admin role
  • Ensure that the active billing account is associated with the GCP project. 
  • To create a destination, you are assigned the role of Team Collaborator or any other administrative role except Billing Administrator. 

Here are the steps to configure BigQuery as a destination: 

  • Click DESTINATIONS in the Navigation Bar.
  • In the Destinations List View, click + CREATE.
  • On the Add Destination page, select Google BigQuery as the destination type.
  • On the Configure your Google BigQuery Destination page, specify all the details. 
Azure Postgres to BigQuery: Configure Destination Settings
Configure Destination Settings

For more information on the configuration of Google BigQuery as a destination in Hevo, refer to the Hevo documentation
With these steps, you can integrate data from Azure Postgres to table in BigQuery in Hevo.

Method 2: Using CSV file to Integrate Azure Postgres to BigQuery

You can use CSV files to integrate your data in Azure Postgres with BigQuery. The steps below explain how to load Azure Postgres file in BigQuery using CSV in SQL. 

Step 1: Export Data from Azure PostgreSQL to CSV

You can use the COPY statement to export data from an Azure Database for PostgreSQL table to a CSV file.

COPY Table_name 
TO 'File_name.csv'
WITH (FORMAT 'csv');

Step 2: Export CSV to Google Cloud Storage

You can upload the CSV file extracted from Azure Postgres to Google Cloud Storage using the following steps:

  • Login to your Google Cloud account and click on Go to Console.
Azure MySQL to BigQuery: CSV to GCS Export Step 1
CSV to GCS Export Step 1
  • From the Navigation Menu on the left side, click Storage>Browser.
Azure MySQL to BigQuery: CSV to GCS Export Step 2
CSV to GCS Export Step 2
  • Click on Create Bucket to create a new bucket that acts like a folder to store your files. 
Azure MySQL to BigQuery: CSV to GCS Export Step 3
CSV to GCS Export Step 3
  • Enter a unique name for your bucket in the Name Your Bucket section and click on CREATE at the bottom of the page.
Azure MySQL to BigQuery: CSV to GCS Export step 4
CSV to GCS Export Step 4
  • You can either Upload files or Drop them in the drop zone.
Azure MySQL to BigQuery: CSV to GCS Export step 5
CSV to GCS Export Step 5
  • You can now access your CSV file from the dashboard once it is uploaded.
Azure MySQL to BigQuery: CSV to GCS Export step 6
CSV to GCS Export Step 6

Step 3: Import CSV Data into Google BigQuery

Here are the steps to load data from a CSV file stored in GCS and upload it to BigQuery:

  • Go to the BigQuery page in Google Cloud Console.
  • You can enter the below code in the query editor:
LOAD DATA OVERWRITE datasetname.tablename
(x INT64,y STRING)
  format = 'CSV',
  uris = ['file_path']);
  • Click Run.

Thus, you can use these steps to connect Azure Postgres to BigQuery. 

Limitations of Using CSV Files to Integrate Data from Azure Postgres to BigQuery

There are some drawbacks to using CSV files to export data from Azure Postgres to BigQuery. Some of these are: 

  • Limited Data Types: CSV files support basic data types. This can lead to data loss or conversion errors when dealing with complex or specialized data types like dates, timestamps, or custom types.
  • Performance Issues: Reading and writing large CSV files can be slow and resource-intensive, especially when you are dealing with large datasets. This can impact migration performance and increase processing times.
  • Security: CSV files do not offer built-in encryption or data validation mechanisms. This can pose risks to data integrity and security, especially when you are transferring sensitive or confidential data. 

Use Cases of Azure Postgres to BigQuery Integration

Here are some use cases of Azure Postgres to BigQuery integration: 

  • Business Intelligence: BigQuery’s BI engine enables rapid analysis of stored data with high concurrency. It also allows you to interact with business intelligence tools such as Looker, Power BI, and Tableau. You can use this to make reports and visualizations and perform analyses of the data collected in BigQuery.
  • Geospatial Analytics: BigQuery also has a Geographic Information System (GIS) that allows geospatial or geography-based data analysis. It converts latitude and longitude data into precise geographic locations. You can use it with Google Earth Engine, BigQuery Geo Viz, Jupyter Notebooks, and other applications. 
  • Machine Learning Integration: You can use BigQuery’s built-in ML integration feature to build machine learning models. This eliminates the need to export data to other applications and allows SQL practitioners to build ML models.


This blog comprehensively explains Azure Postgres to BigQuery integration. It provides two methods and best practices for migrating Azure Postgres data to BigQuery. One method uses CSV files for integration, while the other uses Hevo Data. The near real-time integration, automated data pipeline, and no-code interface features of Hevo make it a suitable platform for your data integration. You can schedule a demo today to take advantage of Hevo’s benefits!


  1. What is Azure?

Azure is Microsoft’s cloud computing platform. It supports several Microsoft-specific and third-party software services to facilitate data analytics, virtual computing, storage, and networking.

  1. What are the disadvantages of using BigQuery?

BigQuery is a cost-effective data warehousing solution. However, it can be expensive for certain workloads requiring a lot of data processing. Also, while it is fully integrated with other GCP services, BigQuery may not integrate with many non-GCP tools and services. 

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Shuchi Chitrakar
Technical Content Writer

Shuchi is a Physicist turned journalist with passion for data story telling. She enjoys writing articles on latest technologies specifically AI and Data Science.

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