How to Load Postgres to Snowflake: 3 Easy Steps

In the current technological landscape, businesses often look to capitalize on the benefits of both analytical and transactional information with integrations such as Postgres to Snowflake data types. For financial operations, PostgreSQL is a commonly used relational database, and Snowflake provides its users with scalable and effective data warehousing and analytical capabilities.

By integrating these two platforms, companies could optimize performance, gain insights from real-time data, and make informed choices.

In this article, we have provided you with 3 efficient methods for both skilled and non-technical users to move data from PostgreSQL to Snowflake.

Methods to Connect Your PostgreSQL Database to Your Snowflake Warehouse

Method 1: Using ETL Tools to Move Data from Postgres to Snowflake
Method 2: Write a Custom Code to Move Data from Postgres to Snowflake
Method 3: Using a Snowflake Connector to Move Data from Postgres to Snowflake

Here’s a comparison of the three methods,

Method	Best For	Pros	Cons
Hevo (Fully Managed ELT Platform)	Simple, reliable, no-code,auto-healing pipelines	24/7 expert support, transparent pricing, zero maintenance	Cloud-only
Manual Export + Snowflake COPY	One-time or occasional batch loads	Full manual control	Time-consuming, no automation
Snowflake PostgreSQL Connector (CDC)	Native replication	Near real-time sync	Complex setup and configuration

Method 1: Use Hevo ETL to Move Data From Postgres to Snowflake With Ease

Step 1: Set up PostgreSQL as the Source

1. Log in to your Hevo account.
   
2. On the dashboard, click “+ Create Pipeline”.
   
3. Choose PostgreSQL as your source.
   
4. Enter the required connection details:
   
   • Hostname
     
   • Port
     
   • Database name
     
   • Username and password
     
5. Click Test Connection to verify connectivity.
   
6. Once verified, click Save and Continue.

Step 2: Set up Snowflake as the Destination

1. After adding your source, select “Add Destination”.
   
2. Choose Snowflake from the list of available destinations.
   
3. Enter the following Snowflake credentials:
   
   • Warehouse name
     
   • Database name
     
   • Schema
     
   • Role (if applicable)
     
   • Account identifier
     
   • Username and password
     
4. Click Test Connection to confirm everything is working.
   
5. Hit Save and Continue to finalize your destination setup.

Step 3: Start the Data Load

1. Once both source and destination are configured, select the tables you want to sync.
   
2. Set the sync mode (Full Load, Incremental, or CDC based on your use case).
   
3. Enable the pipeline and watch your data flow from PostgreSQL to Snowflake in real time.

Here are some additional features that Hevo provides, which may help your integration:

•  Incremental Data Load: Hevo enables the transfer of data that has been modified in real-time. This ensures efficient utilization of bandwidth on both ends.
• Schema Management: Hevo eliminates the tedious task of schema management, automatically detecting schema changes.
• Scalable Infrastructure: Hevo has built-in integrations for 150+ sources that can help you scale your data infrastructure as required.

Method 2: Write a Custom Code to Move Data from Postgres to Snowflake

This method is best suited for one-time migrations or infrequent batch loads, where real-time updates are not required, and teams want full control over the data movement process.

Step 1: Export Data from PostgreSQL

Use PostgreSQL’s native COPY command to export table data into CSV files.

COPY (SELECT * FROM public.orders)

TO '/tmp/orders.csv'

WITH CSV HEADER;

Best practices:

• Use SELECT queries to filter or pre-transform data
• Export tables incrementally using a timestamp column if needed
• Compress files (GZIP) to reduce transfer time

Step 2: Upload Files to a Snowflake Stage

Snowflake loads data from staging locations, either internal or cloud-based.

Option A: Internal Snowflake Stage

PUT file:///tmp/orders.csv @~/orders;

Option B: External Stage (S3, GCS, Azure Blob)

• Upload files to cloud storage
• Create an external stage pointing to that location

CREATE OR REPLACE STAGE pg_stage

URL='s3://my-bucket/postgres/'

CREDENTIALS=(AWS_KEY_ID='xxx' AWS_SECRET_KEY='yyy');

Step 3: Create Target Tables in Snowflake

Define tables that match (or intentionally differ from) your PostgreSQL schema.

CREATE OR REPLACE TABLE orders (

  order_id INT,

  customer_id INT,

  amount NUMBER,

  created_at TIMESTAMP

);

Be mindful of:

• Timestamp and timezone differences
• JSON → VARIANT mappings
• Boolean and numeric type conversions
  

Step 4: Load Data into Snowflake

Use COPY INTO for high-performance bulk ingestion.

COPY INTO orders

FROM @pg_stage/orders.csv

FILE_FORMAT = (TYPE = CSV SKIP_HEADER = 1);

Step 5: Merge or Upsert Data (Optional)

For incremental loads, merge data from staging tables into final tables.

MERGE INTO orders t

USING orders_staging s

ON t.order_id = s.order_id

WHEN MATCHED THEN UPDATE SET amount = s.amount

WHEN NOT MATCHED THEN INSERT VALUES (...);

Method 3: Automated Replication Using the Snowflake Connector for PostgreSQL

This method uses Snowflake’s native PostgreSQL Connector, available via the Snowflake Marketplace, to continuously replicate data from PostgreSQL into Snowflake using Change Data Capture (CDC). It is best suited for teams that want near real-time sync without building or maintaining custom ingestion pipelines.

Prerequisites

• PostgreSQL 11+ with logical replication enabled
  (wal_level = logical)
• A Snowflake account with admin access
• Docker installed on a host that can reach PostgreSQL
• Network access between PostgreSQL and Snowflake

Step 1: Install the Connector from Snowflake Marketplace

1. Log in to Snowsight
2. Navigate to Data Products → Marketplace
3. Search for Snowflake Connector for PostgreSQL
4. Click Get, select a warehouse, and install the app

The connector will now appear under Apps in Snowsight.

Step 2: Create an Event Table for Monitoring

Snowflake uses an event table to track replication status and errors.

CREATE EVENT TABLE IF NOT EXISTS pg_connector_events;

ALTER ACCOUNT SET EVENT_TABLE = pg_connector_events;

This enables observability into sync progress and failures.

Step 3: Deploy the Connector Agent (Docker)

The connector relies on a lightweight Docker agent to extract changes from PostgreSQL.

Pull the agent image:

docker pull snowflakedb/pg-cdc-agent:latest

Create a configuration file (agent-config.json) with PostgreSQL and Snowflake credentials, then start the agent:

docker run -d \

  -v $(pwd)/agent-config.json:/config/agent-config.json \

  snowflakedb/pg-cdc-agent:latest \

  --config /config/agent-config.json

Best practice: Use key-pair authentication for Snowflake instead of passwords.

Step 4: Configure Replication in Snowsight

1. Open the installed connector app
2. Create a PostgreSQL source
3. Select tables to replicate
4. Choose replication mode:
   
   • Initial Load only
   • CDC only
   • Initial Load + CDC

Step 5: Start and Monitor Replication

Once started, the connector:

• Performs an initial snapshot (if configured)
• Streams ongoing changes in near real time
• Automatically retries on transient failures

Monitor replication health using:

SELECT * FROM pg_connector_events ORDER BY timestamp DESC;

Additional Resources for PostgreSQL Integrations and Migrations

• PostgreSQL to Oracle Migration
• Connect PostgreSQL to MongoDB
• Connect PostgreSQL to Redshift
• Integrate Postgresql to Databricks
• Export a PostgreSQL Table to a CSV File

What is Postgres?

Postgres is an open-source Relational Database Management System (RDBMS) developed at the University of California, Berkeley. It is widely recognized for its reliability, robust features, and performance, and has been in use for over 20 years.

Postgres not only supports object-relational data but also supports complex structures and a wide variety of user-defined data types. This gives PostgreSQL a definitive edge over other open-source SQL databases, such as MySQL, MariaDB, and Firebird.

Businesses rely on Postgres as their primary data storage/data warehouse for online, mobile, geospatial, and analytics applications. Postgres runs on all major operating systems, including Linux, UNIX (AIX, BSD, HP-UX, SGI IRIX, Mac OS X, Solaris, Tru64), and Windows.

Key Features of Postgres

• Reliable & Fault-Tolerant: Features like write-ahead logging ensure data integrity and high availability.
• Security-First Design: Includes advanced access controls and supports major security standards like LDAP and GSSAPI.
• Flexible & Developer-Friendly: Supports complex data types and offers full control for custom database setups.
• Open-Source & Cross-Platform: Free to use and runs smoothly on all major operating systems.
• Trusted by Top Companies: Used by Apple, Spotify, Facebook, and more for everyday data operations.

What is Snowflake?

Snowflake is a fully managed, cloud-based data warehouse that helps businesses modernize their analytics strategy. Snowflake can query both structured and unstructured data using standard SQL. It delivers results of user queries spanning Gigabytes and Petabytes of data in seconds.

Snowflake automatically harnesses thousands of CPU cores to quickly execute queries for you. You can even query streaming data from your web, mobile apps, or IoT devices in real-time.

Snowflake comes with a web-based UI, a command-line tool, and APIs with client libraries that make interacting with Snowflake pretty simple. Snowflake is a secure platform that meets the most stringent regulatory standards, including HIPAA, FedRAMP, and PCI DSS. When you store your data in Snowflake, your data is encrypted in transit and at rest by default, and it’s automatically replicated, restored, and backed up to ensure business continuity. Plan smarter with our Snowflake pricing calculator – accurate, fast, and reliable.

Key Features of Snowflake 

Ever since the Snowflake Data Warehouse got into the growing cloud Data Warehouse market, it has established itself as a solid choice. It offers five editions, going from ‘standard’ to ‘enterprise’. This is a good thing as customers have options to choose from based on their specific needs.

• It has some cool querying features like undrop, fast clone, etc. These might be worth checking out as they may account for a good chunk of your day-to-day data operations.
• The ability to separate storage and compute is something to consider, and how that relates to the kind of data warehousing operations you’d be looking for.
• Snowflake is designed in a way to ensure the least user input and interaction required for any performance or maintenance-related activity. This is not a standard among cloud DWHs. For instance, Redshift needs user-driven data vacuuming.

Use Cases of Postgres to Snowflake Data Replication

Let’s look into some use cases of Postgres-Snowflake replication.

Backup and Disaster Recovery – Transfer your Postgres data to a dependable and secure cloud environment provided by Snowflake. You can be assured that your data is constantly accessible and backed up, ensuring business continuity even in the event of unforeseen events.

Transferring Postgres data to Snowflake- Transfer your data to Snowflake’s endlessly scalable cloud platform with ease. Take advantage of easy performance enhancements, cost-effectiveness, and the capacity to manage large datasets.

Data Warehousing- Integrate data into Snowflake’s data warehouse from numerous surces of sources, including Postgres. This can help uncover hidden patterns, provide a deeper understanding of your company, and enhance strategic decision-making.

Advanced Analytics- Utilize Snowflake’s quick processing to run complex queries and find minute patterns in your Postgres data. This can help you stay ahead of the curve, produce smart reports, and gain deeper insights.

Artificial Intelligence and Machine Learning- Integrate your Postgres data seamlessly with Snowflake’s machine-learning environment. Allowing you to develop robust models, provide forecasts, and streamline processes to lead your company toward data-driven innovation.

Collaboration and Data Sharing- Colleagues and partners can securely access your Postgres data within the collaborative Snowflake environment. Hence, this integration helps promote smooth communication and expedite decision-making and group achievement.

Conclusion

Migrating data from PostgreSQL to Snowflake becomes far more manageable when you choose the right approach. Whether you prefer manual control, native CDC capabilities, or a fully automated ELT pipeline, the goal remains the same: reliable, scalable, and consistent data flow for analytics.

If you want to eliminate operational overhead, avoid brittle scripts, and maintain real-time, schema-resilient pipelines, a no-code platform like Hevo offers the fastest and most dependable path forward. It lets your team focus on insights instead of engineering pipelines.

Explore how Hevo can simplify your Postgres-to-Snowflake migration and keep your data pipelines running effortlessly. Schedule a free 1:1 with an expert now!

FAQ on PostgreSQL to Snowflake