Are you struggling to connect PostgreSQL on Amazon RDS to Snowflake? Do you find it challenging to integrate the two platforms and leverage the powerful analytics features of Snowflake? You’re not alone. To analyze the vast amounts of data stored in PostgreSQL databases on Amazon RDS, integration with Snowflake becomes a viable solution.

In this guide, we will explore two simple methods for migrating data from PostgreSQL Amazon RDS to Snowflake. 

Let’s dive in!

How to Connect PostgreSQL on Amazon RDS to Snowflake?

Prerequisites

Keep the following prerequisites in mind for the PostgreSQL on Amazon RDS to Snowflake migration process:

  • Access to PostgreSQL database on Amazon RDS.
  • An active Snowflake account.

Method 1: Move Data from PostgreSQL on Amazon RDS using CSV files

Step 1: Export Data from PostgreSQL on Amazon RDS to a CSV File

  • Use the psql to connect to your Amazon RDS PostgreSQL database. You’ll require the necessary credentials like, the hostname (endpoint) of your RDS instance, database name, username, and password. To access your Amazon RDS PostgreSQL database, enter the following command:
psql -h rds_endpoint -U username -d database_name

Replace rds_endpoint, username, and database_name with the appropriate values.

  • Now, you can execute the COPY TO command to export data from a table to a CSV file.
COPY table_name TO 'file_path/file_name.CSV' WITH CSV HEADER;

Replace table_name with the name of the table from where you want to export data. Replace file_path/file_name.CSV with the name and path of the CSV file.

  • After executing the above command, specified table data will be copied into a CSV file. Make sure the CSV files are in the correct format and that the structure matches the target Snowflake table. Verify and clean the data as per the requirement before loading it into the Snowflake table.

Step 2: Create a Snowflake Internal Stage

  • To load CSV files from a local machine into a Snowflake table, you would need to create an internal stage. This stage will map to a location within your Snowflake account. It’ll be used to temporarily store the CSV files during the data loading process. You can create an internal stage using the following command in SnowSQL CLI:
CREATE STAGE internal_stage_name

Replace internal_stage_name with the name of your choice for the stage.

  • Now you need to upload CSV files from your local machine into the Snowflake internal stage. This is achieved by using the PUT command. Execute the below command to upload CSV files into the internal stage:
PUT file:///path_to_local_csv_file/file.csv @internal_stage_name;

Replace /path_to_local_csv_file/file.csv with the path and name of the CSV file on your local machine, and @internal_stage_name with the internal stage name that you’ve just created.

Step 3: Upload CSV Files to Snowflake

  • Before uploading the data, ensure you have a target table in Snowflake with the correct schema that matches the CSV file’s structure. You can create the table in Snowflake using a CREATE TABLE statement.
  • Finally, use the COPY INTO command to load data from the internal stage into the Snowflake table.
COPY INTO snowflake_table_name 
FROM <internal_stage_name>/<path_to_csv_file> 
FILE_FORMAT = (TYPE = ‘CSV’);

Replace snowflake_table_name with the name of the target table you want to load data into. Also, update the path and CSV filename you uploaded to the internal stage.

That’s it! You’ve successfully loaded data from your PostgreSQL on Amazon RDS into Snowflake using the CSV approach.

CSV files can be beneficial for the following use cases:

  • One-Time Data Transfer: Using CSV files method is ideal for one-time migrations between PostgreSQL on Amazon RDS to Snowflake. It simplifies the process, lowers migration costs, and ensures efficient data replication.
  • Efficient for Small Data Volumes: Since smaller data volumes may not require complex integration processes, the manual approach becomes a more practical and cost-effective choice. As a result, integration expenses are minimized, making it a suitable option for you to optimize your data integration process.
  • Custom Transformation: The CSV format provides a simple and structured representation of the data, making it easier to apply various transformations. It facilitates straightforward data manipulation and enrichment before loading it into Snowflake.

Limitations of Manual Migration from PostgreSQL on Amazon RDS to Snowflake

There are several drawbacks to performing PostgreSQL on Amazon RDS to Snowflake ETL using CSV files. Here are some of them:

  • Time-Consuming: It involves downloading PostgreSQL data from Amazon RDS to a CSV file and then uploading it into Snowflake. This process can be time-consuming, especially while handling large volumes of data.
  • Data Consistency and Validation: Ensuring data consistency between PostgreSQL on Amazon RDS and Snowflake is essential but can be a limitation for integration. Without proper testing and validations after migration, data integrity issues may arise, leading to potential inaccuracies and errors in the integrated data.
  • Data Security: While Snowflake provides robust security features, it is crucial to manage access controls carefully during manual migration. This ensures protection against vulnerabilities and authorized access, making the data transfer process smooth, secure, and reliable.

Method 2: Automating the Data Replication Process Using a No-Code Tool

Automating data replication from your PostgreSQL on Amazon RDS to Snowflake using a no-code tool provides significant advantages compared to using CSV files. Here are some of them:

  • User-Friendly: No-Code tools are user-friendly, allowing non-technical users to effortlessly manage data replication tasks. Additionally, it eliminates the need for writing code to connect databases, making data synchronization efficient and straightforward.
  • Integration with Multiple Data Sources: No-Code tools enable seamless integration of various data sources with pre-built connectors. This enables you to consolidate and analyze data from multiple sources in a unified manner, simplifying your data management.
  • Real-Time or Scheduled Updates: With no-code tools, you have the flexibility to set up real-time or scheduled updates for data replication. This ensures that your analytics platform constantly has the most up-to-date information without the need for manual intervention.

Hevo Data is a popular no-code tool for automating data replication. It provides a simple and intuitive interface for setting up data pipelines between PostgreSQL on Amazon RDS and Snowflake.

To migrate data from PostgreSQL on Amazon RDS to Snowflake using Hevo Data, follow these steps:

Step 1: Configure Amazon RDS PostgreSQL as Source

PostgreSQL on Amazon RDS to Snowflake: Configure PostgreSQL on Amazon RDS as a Source

Step 2: Configure Snowflake as Destination

PostgreSQL on Amazon RDS to Snowflake: Configure Snowflake as a Destination

You have successfully connected your PostgreSQL database on Amazon RDS to Snowflake. Now you can use Snowflake to work with your PostgreSQL data, allowing you to explore and analyze your data with ease.

What can you Achieve by Migrating Data from PostgreSQL on Amazon RDS to Snowflake?

Here are some of the analyses you can perform after PostgreSQL on Amazon RDS to Snowflake replication:

  • Gain valuable insights by exploring the different stages of your sales funnel.
  • Obtain in-depth customer insights by analyzing every email touchpoint to identify and segment highly engaged customers.
  • Analyze employee performance data from project management platforms and Human Resources to assess your team performance, behavior, and efficiency.
  • Integrating transactional data from different functional groups (Sales, marketing, product, Human Resources) and finding answers. For example:
    • Analyze data from marketing campaigns and sales to identify which campaigns have the highest conversion rates and lead generation.
    • Monitor social media data and engagement metrics to assess the effectiveness of online marketing efforts.
    • Assess financial data from different departments to gain insights into overall company performance and growth.

Conclusion

PostgreSQL on Amazon RDS to Snowflake integration can be achieved by using CSV files or with an automated no-code tool. With CSV files you get more control and flexibility, allowing you to tailor data transformations to your needs. However, it requires a higher level of expertise and effort to implement.

On the other hand, no-code tools like Hevo Data are an excellent choice for a simple and efficient data integration solution. Its user-friendly interface and pre-built connectors make the integration process easy and accessible to users of all levels without coding expertise.

With real-time data streaming and transformation capabilities, Hevo enables seamless integration and data synchronization between PostgreSQL on Amazon RDS and Snowflake. This functionality ensures that you can work with up-to-date data, facilitating timely and data-driven decision-making.

If you don’t want SaaS tools with unclear pricing that burn a hole in your pocket, opt for a tool that offers a simple, transparent pricing model. Hevo has 3 usage-based pricing plans starting with a free tier, where you can ingest up to 1 million records.

Schedule a demo to see if Hevo would be a good fit for you, today!

Tejaswini Kasture
Freelance Technical Content Writer, Hevo Data

Tejaswini's profound enthusiasm for data science and passion for writing drive her to create high-quality content on software architecture, and data integration.

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