Warehouses and Lakes

How to load data from Snowflake Data Cloud to PostgreSQL Destination

Learn how to use Airbyte to synchronize your Snowflake Data Cloud data into PostgreSQL Destination within minutes.


This can be done by building a data pipeline manually, usually a Python script (you can leverage a tool as Apache Airflow for this). This process can take more than a full week of development. Or it can be done in minutes on Airbyte in three easy steps:

  1. set up Snowflake Data Cloud as a source connector (using Auth, or usually an API key)
  2. set up PostgreSQL Destination as a destination connector
  3. define which data you want to transfer and how frequently

You can choose to self-host the pipeline using Airbyte Open Source or have it managed for you with Airbyte Cloud.

This tutorial’s purpose is to show you how.

What is Snowflake Data Cloud

Snowflake Data Cloud is a cloud-based data warehousing and analytics platform that allows organizations to store, manage, and analyze large amounts of data in a secure and scalable manner. It provides a single, integrated platform for data storage, processing, and analysis, eliminating the need for multiple tools and systems. Snowflake Data Cloud is built on a unique architecture that separates compute and storage, allowing users to scale up or down as needed without affecting performance. It also offers a range of features such as data sharing, data governance, and machine learning capabilities, making it a comprehensive solution for modern data management and analytics.

What is PostgreSQL Destination

An object-relational database management system, PostgreSQL is able to handle a wide range of workloads, supports multiple standards, and is cross-platform, running on numerous operating systems including Microsoft Windows, Solaris, Linux, and FreeBSD. It is highly extensible, and supports more than 12 procedural languages, Spatial data support, Gin and GIST Indexes, and more. Many web, mobile, and analytics applications use PostgreSQL as the primary data warehouse or data store.


  1. A Snowflake Data Cloud account to transfer your customer data automatically from.
  2. A PostgreSQL Destination account.
  3. An active Airbyte Cloud account, or you can also choose to use Airbyte Open Source locally. You can follow the instructions to set up Airbyte on your system using docker-compose.

Airbyte is an open-source data integration platform that consolidates and streamlines the process of extracting and loading data from multiple data sources to data warehouses. It offers pre-built connectors, including Snowflake Data Cloud and PostgreSQL Destination, for seamless data migration.

When using Airbyte to move data from Snowflake Data Cloud to PostgreSQL Destination, it extracts data from Snowflake Data Cloud using the source connector, converts it into a format PostgreSQL Destination can ingest using the provided schema, and then loads it into PostgreSQL Destination via the destination connector. This allows businesses to leverage their Snowflake Data Cloud data for advanced analytics and insights within PostgreSQL Destination, simplifying the ETL process and saving significant time and resources.

Step 1: Set up Snowflake Data Cloud as a source connector

1. First, you need to have a Snowflake Data Cloud account and the necessary credentials to access it.

2. Once you have the credentials, go to the Airbyte dashboard and click on "Sources" on the left-hand side of the screen.

3. Click on the "Create a new source" button and select "Snowflake Data Cloud" from the list of available sources.

4. Enter a name for your Snowflake Data Cloud source and click on "Next".

5. In the "Connection" tab, enter the following information:  
- Account name: the name of your Snowflake account  
- Username: your Snowflake username  
- Password: your Snowflake password  
- Warehouse: the name of the warehouse you want to use  
- Database: the name of the database you want to use  
- Schema: the name of the schema you want to use

6. Click on "Test connection" to make sure that the connection is successful.

7. If the connection is successful, click on "Next" to proceed to the "Configuration" tab.

8. In the "Configuration" tab, select the tables or views that you want to replicate and configure any necessary settings.

9. Click on "Create source" to save your Snowflake Data Cloud source and start replicating data.

Step 2: Set up PostgreSQL Destination as a destination connector

Step 3: Set up a connection to sync your Snowflake Data Cloud data to PostgreSQL Destination

Once you've successfully connected Snowflake Data Cloud as a data source and PostgreSQL Destination as a destination in Airbyte, you can set up a data pipeline between them with the following steps:

  1. Create a new connection: On the Airbyte dashboard, navigate to the 'Connections' tab and click the '+ New Connection' button.
  2. Choose your source: Select Snowflake Data Cloud from the dropdown list of your configured sources.
  3. Select your destination: Choose PostgreSQL Destination from the dropdown list of your configured destinations.
  4. Configure your sync: Define the frequency of your data syncs based on your business needs. Airbyte allows both manual and automatic scheduling for your data refreshes.
  5. Select the data to sync: Choose the specific Snowflake Data Cloud objects you want to import data from towards PostgreSQL Destination. You can sync all data or select specific tables and fields.
  6. Select the sync mode for your streams: Choose between full refreshes or incremental syncs (with deduplication if you want), and this for all streams or at the stream level. Incremental is only available for streams that have a primary cursor.
  7. Test your connection: Click the 'Test Connection' button to make sure that your setup works. If the connection test is successful, save your configuration.
  8. Start the sync: If the test passes, click 'Set Up Connection'. Airbyte will start moving data from Snowflake Data Cloud to PostgreSQL Destination according to your settings.

Remember, Airbyte keeps your data in sync at the frequency you determine, ensuring your PostgreSQL Destination data warehouse is always up-to-date with your Snowflake Data Cloud data.

Use Cases to transfer your Snowflake Data Cloud data to PostgreSQL Destination

Integrating data from Snowflake Data Cloud to PostgreSQL Destination provides several benefits. Here are a few use cases:

  1. Advanced Analytics: PostgreSQL Destination’s powerful data processing capabilities enable you to perform complex queries and data analysis on your Snowflake Data Cloud data, extracting insights that wouldn't be possible within Snowflake Data Cloud alone.
  2. Data Consolidation: If you're using multiple other sources along with Snowflake Data Cloud, syncing to PostgreSQL Destination allows you to centralize your data for a holistic view of your operations, and to set up a change data capture process so you never have any discrepancies in your data again.
  3. Historical Data Analysis: Snowflake Data Cloud has limits on historical data. Syncing data to PostgreSQL Destination allows for long-term data retention and analysis of historical trends over time.
  4. Data Security and Compliance: PostgreSQL Destination provides robust data security features. Syncing Snowflake Data Cloud data to PostgreSQL Destination ensures your data is secured and allows for advanced data governance and compliance management.
  5. Scalability: PostgreSQL Destination can handle large volumes of data without affecting performance, providing an ideal solution for growing businesses with expanding Snowflake Data Cloud data.
  6. Data Science and Machine Learning: By having Snowflake Data Cloud data in PostgreSQL Destination, you can apply machine learning models to your data for predictive analytics, customer segmentation, and more.
  7. Reporting and Visualization: While Snowflake Data Cloud provides reporting tools, data visualization tools like Tableau, PowerBI, Looker (Google Data Studio) can connect to PostgreSQL Destination, providing more advanced business intelligence options. If you have a Snowflake Data Cloud table that needs to be converted to a PostgreSQL Destination table, Airbyte can do that automatically.

Wrapping Up

To summarize, this tutorial has shown you how to:

  1. Configure a Snowflake Data Cloud account as an Airbyte data source connector.
  2. Configure PostgreSQL Destination as a data destination connector.
  3. Create an Airbyte data pipeline that will automatically be moving data directly from Snowflake Data Cloud to PostgreSQL Destination after you set a schedule

With Airbyte, creating data pipelines take minutes, and the data integration possibilities are endless. Airbyte supports the largest catalog of API tools, databases, and files, among other sources. Airbyte's connectors are open-source, so you can add any custom objects to the connector, or even build a new connector from scratch without any local dev environment or any data engineer within 10 minutes with the no-code connector builder.

We look forward to seeing you make use of it! We invite you to join the conversation on our community Slack Channel, or sign up for our newsletter. You should also check out other Airbyte tutorials, and Airbyte’s content hub!

Many businesses rely on online analytical processing (OLAP) solutions such as Snowflake to gain insights from data that is collected from multiple sources spread across the enterprise. Snowflake is a fully managed SaaS (software as a service) that provides a single platform for data warehousing, data lakes, data engineering, data science, data application development, and sharing of data.

As discussed in our article about data integration, moving data from across the enterprise into a centralized data analytics platform such as Snowflake provides the following benefits:

  • A unified view of data from across the enterprise, and a single source of truth.
  • A platform that is purpose built for running large analytics jobs.
  • A single location to transform and join data from across the enterprise.

While the above benefits are significant, additional value may be extracted by moving analytical data from Snowflake back into operational systems. Furthermore, moving data back to an operational system may be beneficial if access to your Snowflake deployment is restricted.

Moving data from Snowflake into an operational system, also known as reverse ETL, is the flip side of ETL/ELT. With reverse ETL, Snowflake becomes the data source rather than the destination. Data from Snowflake is transformed to match a destination's data formatting requirements, and loaded into the destination. In other words, reverse ETL “operationalizes” analytical data by pushing it back into operational systems.

In this tutorial, you will learn how to implement reverse ETL from Snowflake to Postgres. Postgres is an online transaction processing (OLTP) relational database which is often used in operational systems. It is a robust, open-source system with a reputation for reliability and performance.

Because Airbyte supports source connectors for popular data warehouses such as Redshift, BigQuery, and Snowflake, and destination connectors for popular databases such as Postgres, MySQL, or Microsoft SQL Server, Airbyte can be used to to easily create reverse ETL pipelines.


In order to follow along the exact steps given in this tutorial, you will need to meet the prerequisites given below.

Step 1: Set up snowflake as an Airbyte source

Go to Airbyte Cloud and create a new connection. Give the source a name and select Snowflake as the source type. You can read all the configuration details on the Airbyte Snowflake source documentation.

We will be creating a new database and table. Login to your snowflake dashboard and create a new database in a warehouse where you have ACCOUNTADMIN privileges. You can skip this step if you already have a database setup.

On creating the database, run the following SQL query to create a new table for our tutorial.

create or replace TABLE FLOWER (

Our next step will be to populate the table with some sample data. INSERT the following query into our newly created table to add data.

(name, color, fcount)
 ('Lily', 'White', 12),
 ('Rose', 'Red', 1),
 ('Lotus', 'Pink', 8),
 ('Sunflower', 'Yellow', 9),
 ('Daisy', 'White', 10),
 ('Poppy', 'Red', 3),
 ('Narcissus', 'Yellow', 12),
 ('Blue Morning Glory', 'Blue', 10),
 ('Chamomile Vine', 'Yellow', 19),
 ('Scarlet', 'Red', 16);

The next steps is setting up a new source in your Airbyte Dashboard. Here is a sample configuration:

Once the configurations are complete, click Set up source.

Step 2: Set up a PostgreSQL as Airbyte destination

Once Snowflake has been successfully configured as the source; you will be prompted to configure your destination. For this tutorial, we will use Heroku's managed PostgreSQL add-on to create a database host. You can skip this step if you already have a PostgreSQL host setup.

We first need to create a new Heroku app to host a new database on Heroku. Login to your Heroku dashboard and choose to create a new app

Additionally, you can change the Region to make the app's servers closer to your location.

Once you have provided the app name, click Create app.

To add a PostgreSQL database to our app, which we just created, we need to install Heroku’s Postgres Add-on. Go to Resource and look for the Heroku PostgreSQL add-on.

Perfect, we now have our database setup without any hassle. But we do need credentials to access this database.

To find the credentials and the connection URL for the PostgreSQL database, you need to navigate to the Resources tab in your app's dashboard again and select the Heroku Postgres resource

This will bring you to the configuration screen for your database. To find the db credentials, click on the Settings tab and View Credentials.

Take note of the Host, Database, User, Post, and Password; we will need these details while setting up the Airbyte destination.

To test out if we can set up a connection to our newly created database. Copy the URI provided by Heroku inside Credentials and run the following command


If everything went correctly, you should see the following output in the terminal

Now we create a new Airbyte destination using this newly obtained Postgres database, Go to Destinations in your Airbyte cloud account and click on New Destination.

Step 3: Set up Snowflake to Postgres Airbyte connection

Using your connection settings, you can configure your source and destination. You should be able to see the loaded schemas and tables from Snowflake.

Give the connection a name, and define a replication frequency and destination namespace. For this tutorial, we have chosen Manual as the replication frequency and the mirror source structure option.

Airbyte presents the available tables given in a warehouse. For this demo, we will only have the FLOWER table (or stream) from PUBLIC schema (or namespace).

We will set the sync mode to Incremental sync mode. In this approach, on each sync, Airbyte will only sync the new or modified data. Any changes will be recorded as a new row append to the table.

The incremental mode requires us to specify a cursor field for determining whether to sync new data or not. A typical example of a  cursor field would be updated_at or modified_at columns in your database, usually timestamps. However, since our demo table FLOWER doesn’t have a field like that, we select a user-defined cursor that can function as a cursor field; one such column is FCOUNT, which contains unique incremental values for all rows.

Once satisfied with your configuration, save the connection and click Sync now to run your first sync once configured.

Once the sync is complete, you should see how many rows were copied (10 in our case). Next, we will use heroku CLI and connect to the database to view the tables created by Airbyte:

heroku pg:psql --app app-name

Now to find our tables that Airbyte synced, we would first have to make sure that we have access to the correct schema. Use the following Postgres command to list all available schemas in our database.

\dt *.*

As you can see Airbyte successfully created a new table, with the name flower we can now query this table using the SELECT * operation in SQL.

As you can see, there are exactly ten rows Airbyte synced into our database.

Let’s modify one of the records in flowers and see how Incremental Appends work. Go back to your Snowflake dashboard, create a new worksheet, and run the following SQL command.

UPDATE FLOWER SET COLOR = 'White-Light-Pink', FCOUNT = 25 WHERE NAME = 'Lily';

Once you update the record, Sync the table again.

As you can see, Airbyte created a new copy row with the modified values of color and fcount columns instead of updating the original record.

Wrapping up

In this tutorial, you have learned how to:

  1. Configure a Snowflake Airbyte source.
  2. Configure a PostgreSQL Airbyte destination using Heroku.
  3. Create an Airbyte connection that automatically syncs data from Snowflake to PostgreSQL.
  4. Incrementally sync data from Snowflake to Postgres.

If you have enjoyed this tutorial, you may be interested in other Airbyte tutorials or Airbyte’s blog. You can also join the conversation on our community Slack Channel, participate in discussions on Airbyte’s discourse, or sign up for our newsletter. Furthermore, if you are interested in Airbyte as a fully managed service, you can try Airbyte Cloud for free!

Frequently Asked Questions

What data can you extract from Snowflake Data Cloud?

Snowflake Data Cloud provides access to a wide range of data types, including:

1. Structured Data: This includes data that is organized in a specific format, such as tables, columns, and rows. Examples of structured data include customer information, financial data, and inventory records.
2. Semi-Structured Data: This type of data is partially organized and may not fit into a traditional relational database structure. Examples of semi-structured data include JSON, XML, and CSV files.
3. Unstructured Data: This includes data that does not have a specific format or organization, such as text documents, images, and videos.
4. Time-Series Data: This type of data is organized based on time stamps and is commonly used in industries such as finance, healthcare, and manufacturing.
5. Geospatial Data: This includes data that is related to geographic locations, such as maps, GPS coordinates, and satellite imagery.
6. Machine Learning Data: This type of data is used to train machine learning models and includes features and labels that are used to predict outcomes.

Overall, Snowflake Data Cloud provides access to a wide range of data types, making it a versatile tool for data analysis and management.

What data can you transfer to PostgreSQL Destination?

You can transfer a wide variety of data to PostgreSQL Destination. This usually includes structured, semi-structured, and unstructured data like transaction records, log files, JSON data, CSV files, and more, allowing robust, scalable data integration and analysis.

What are top ETL tools to transfer data from Snowflake Data Cloud to PostgreSQL Destination?

The most prominent ETL tools to transfer data from Snowflake Data Cloud to PostgreSQL Destination include:

  • Airbyte
  • Fivetran
  • Stitch
  • Matillion
  • Talend Data Integration

These tools help in extracting data from Snowflake Data Cloud and various sources (APIs, databases, and more), transforming it efficiently, and loading it into PostgreSQL Destination and other databases, data warehouses and data lakes, enhancing data management capabilities.