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.
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Pre-requisites

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

• A Snowflake account.
• A Heroku account to host our PostgreSQL database.
• An active Airbyte cloud account. You can also choose to use Airbyte locally. Follow the instructions to set up Airbyte on your system using docker-compose.
• PostgreSQL & Heroku CLI are installed locally on your system.

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Methods to Move Data From Snowflake to postgres

• Method 1: Connecting Snowflake to postgres using Airbyte.
• Method 2: Connecting Snowflake to postgres manually.

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Method 1: Connecting Snowflake to postgres using Airbyte

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 (
NAME VARCHAR(200),
COLOR VARCHAR(200),
FCOUNT INT
);

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.

INSERT INTO flower
(name, color, fcount)
VALUES
 ('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:

• Account Name or Snowflake account identifier
• Warehouse: E.g., AIRBYTE_WAREHOUSE
• Role: ACCOUNTADMIN
• Database: SAMPLE_DATABASE
• Schema: PUBLIC

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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.

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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

psql

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.

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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.

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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.

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Method 2: Connecting Snowflake to postgres manually.

Migrating data from Snowflake to PostgreSQL without third-party connectors or integrations involves several steps, including exporting data from Snowflake, preparing the PostgreSQL database, and importing the data into PostgreSQL. Below is a detailed step-by-step guide:

Step 1: Export Data from Snowflake

1. Log in to Snowflake: Use your credentials to log in to the Snowflake web interface or connect using SnowSQL, the command-line client.

2. Select the Data to Export: Identify the tables or specific data you want to migrate to PostgreSQL.

3. Export Data to a File:

   - Choose a suitable file format for the export, such as CSV.

   - Execute a `COPY INTO <location>` command to export the data to a stage. For example:

     ```sql

     COPY INTO @my_stage/my_table.csv FROM my_table FILE_FORMAT = (TYPE = CSV HEADER = TRUE);

     ```

   - If you don't have a stage set up, you can use Snowflake's internal stage or create a new one.

4. Download the Exported Files:

   - Use the `GET` command in SnowSQL to download the files from the stage to your local system.

     ```shell

     GET @my_stage/my_table.csv file://path/to/local/directory;

     ```

Step 2: Prepare the PostgreSQL Database

1. Install PostgreSQL: If not already installed, download and install PostgreSQL on your system or server.

2. Create a Database:

   - Log in to your PostgreSQL instance.

   - Create a new database or choose an existing one where you want to import the data.

3. Create Tables:

   - Define the schema for the tables you are importing. Ensure that the data types in PostgreSQL correspond to those in Snowflake.

   - Create tables using the `CREATE TABLE` command. For example:

     ```sql

     CREATE TABLE my_table (

         column1 INT,

         column2 VARCHAR(255),

         ...

     );

     ```

Step 3: Import Data into PostgreSQL

1. Prepare the Data Files:

   - Ensure the CSV files are accessible to the PostgreSQL server.

   - Check the CSV files for any inconsistencies or data that might not comply with PostgreSQL's data types or constraints.

2. Copy Data into PostgreSQL:

   - Use the `psql` command-line tool or another PostgreSQL client to connect to the database.

   - Use the `COPY` command to import the data from the CSV files into the PostgreSQL tables. For example:

     ```sql

     COPY my_table FROM '/path/to/local/directory/my_table.csv' DELIMITER ',' CSV HEADER;

     ```

   - If there are any errors, PostgreSQL will provide feedback. You may need to adjust the data or the table schema accordingly.

3. Verify the Data:

   - After the import, run some queries to ensure the data has been correctly imported.

   - Check for any inconsistencies or missing data.

4. Indexing and Optimization:

   - Once the data is imported, consider adding indexes to improve query performance.

   - Analyze the tables to update the statistics for the query planner.

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Step 4: Clean Up and Final Checks

1. Remove Temporary Files:

   - After confirming the data transfer, you can remove any temporary files that were created during the process.

2. Audit and Compliance:

   - Ensure the data transfer complies with any relevant data governance and privacy policies.

3. Performance Testing:

   - Perform some load testing and query performance testing to ensure the PostgreSQL instance is performing as expected with the new data.

4. Backup:

   - Consider taking a backup of the PostgreSQL database now that it contains the new data.

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By following these steps, you can migrate data from Snowflake to PostgreSQL without using third-party connectors or integrations. Remember to test the process with a small subset of data before performing a full migration to ensure everything works smoothly.

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!

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