Connect PostgreSQL on Amazon RDS to Databricks: 2 Ways to Integrate Data

Amazon RDS, with its support for the PostgreSQL database, is a popular choice for businesses looking for reliable relational database services. However, the increasing need for advanced analytics and large-scale data processing requires migrating data to more efficient platforms like Databricks. Connecting PostgreSQL on Amazon RDS to Databricks can help you gain valuable insights that will help you uncover patterns, trends, and correlations to drive business growth.

Let’s explore the two popular methods to load data from Amazon RDS PostgreSQL to Databricks.

Methods to Integrate PostgreSQL on Amazon RDS to Databricks

Prerequisites

• PostgreSQL version 9.4.15 or higher.
• Access credentials for your PostgreSQL RDS instance.
• An Amazon S3 bucket.
• A Databricks workspace and its URL.

Method 1: Move Data from PostgreSQL on Amazon RDS to Databricks Using CSV Files 

This method involves exporting data from PostgreSQL on Amazon RDS as CSV files and then uploading these files to Databricks. Here are the steps involved in the process:

Step 1: Export Data from PostgreSQL on Amazon RDS as CSV Files

Use the psql command line utility and run the following command to connect to your Amazon RDS instance:

psql -h rds-endpoint.amazonaws.com -U username -d database-name -p port-number

This command uses the:

• Name of the host or IP address of Amazon RDS where the PostgreSQL server is running.
• Your PostgreSQL username.
• The name of the PostgreSQL server database.
• The port number that the PostgreSQL server is listening to.

After executing this command, you will be prompted for your PostgreSQL password. Type your password and press Enter. Now, you can start executing SQL commands.

To export your PostgreSQL data from Amazon RDS to a CSV file, use the PostgreSQL COPY command. This command allows you to export data from a table directly to a CSV file. Here’s an example of how you can use the COPY command:

COPY your_table_name TO '/path/to/your_file.csv' WITH CSV HEADER DELIMITER ‘,’;

This command will copy data from the table your_table_name to the file your_file.csv, which will be sorted at the provided path. The HEADER option specifies that the first row of the CSV file should contain the column headers. Finally, the DELIMITER option specifies the use of a comma to separate the different fields in the CSV file.

Step 2: Move the CSV File to an Accessible Location

For Databricks to access the data, you need to initially move the data to S3 buckets. Then, you can connect Databricks with S3 to complete the data migration.

Use the AWS CLI to upload the CSV file to S3. To do this, open AWS CLI and run the following command:

aws s3 cp /path/to/your_file.csv s3://<BUCKETNAME>/<FOLDERNAME>/

This command will copy your_file.csv to the specified S3 bucket and folder. 

Step 3: Import the PostgreSQL on Amazon RDS CSV File to Databricks

Now, you must move the downloaded CSV file to Databricks. Ensure you have a workspace with Unity Catalog enabled. Then, you can follow these steps for loading data into Databricks:

• Log in to your Databricks account. Click on the Data tab on the left sidebar. 
• In the Data Explorer, click External Data > External Locations to enable data access from an external location.
• Then, click on + New > Add data to start uploading files to your Databricks workspace.
• Choose the Amazon S3 option in the add data UI.
• Select the S3 bucket from the drop-down list, followed by the folders and files you want to load into Databricks. Next, click on Preview table.
• Choose a catalog and a schema from the drop-down lists.
• Click on Create table.

Using the CSV export/import method for PostgreSQL on Amazon RDS Databricks migration involves the following benefits:

• Easy Implementation: The manual method is quite straightforward and doesn’t require in-depth technical or coding knowledge. Even if you aren’t familiar with scripting or coding, you can execute these steps.
• No SaaS Requirements: This method uses only Amazon RDS, S3, and Databricks. You don’t require any additional tools or services to migrate data between the platforms.
• Ideal for One-Time Transfers: You can use the manual method for infrequent or one-time migration, especially of smaller datasets.

Method 2: Use a No-Code Tool to Automate the PostgreSQL on Amazon RDS to Databricks ETL Process

The CSV export/import method to move data from PostgreSQL on Amazon RDS to Databricks has some limitations, including:

• Effort-Intensive: The migration of data between the two platforms using CSV export/import is time-consuming for large-scale and frequent data migrations.
• Lack of Automation: You cannot automate the migration of data from PostgreSQL on Amazon RDS to Databricks with the CSV export/import method. Every time you want to move data, you must perform the repetitive tasks manually.
• Lack of Real-Time Updates: Exporting data from PostgreSQL on Amazon RDS, copying it to S3, and then loading it to Databricks involves considerable time. This prevents real-time or near-real-time data updates in Databricks, leading to non-availability of up-to-the-second data for critical analysis.

No-code tools are an efficient alternative to the CSV export/import process. These tools help overcome the limitations associated with the previous method, with beneficial features such as:

• Fully Managed: No-code tools are usually fully managed, and the solution providers often take care of maintenance, upgrades, and bug fixes. This ensures that you always have access to up-to-date features.
• Secure: Leading no-code ETL tools implement strong encryption, authentication, and other security measures to ensure the data integration processes are secure.
• Real-Time Capabilities: Many no-code ETL tools offer real-time or near-real-time integration capabilities. This helps maintain data consistency between platforms and ensures that stakeholders always have the most current data.
• Reduced Errors: No-code tools use pre-built connectors, and this reduces the possibility of errors in the ETL process when compared to manually-driven solutions.

Hevo Data is one such fully managed no-code tool that helps overcome the hassles of the manual method. With this cloud data pipeline platform, you can achieve an error-free, near-real-time PostgreSQL on Amazon RDS to Databricks integration.

An easy-to-use interface of Hevo simplifies the process of setting up a data transfer pipeline in just a few clicks. Here are the steps involved in migrating data from PostgreSQL on Amazon RDS to Databricks:

Here are some prerequisites involved when using Hevo Data for the PostgreSQL on Amazon RDS to PostgreSQL data integration:

• The PostgreSQL database user is granted SELECT, USAGE, and CONNECT privileges.
• Whitelist Hevo’s IP address.
• If Pipeline mode is Logical Replication:
  • PostgreSQL database instance is a master instance.
  • Log-based incremental replication is enabled.
• If you want to connect to your workspace with your Databricks credentials:
  • Create the Databricks cluster or SQL warehouse.
  • The database hostname, port number, and HTTP path are available.
  • The Personal Access Token (PAT) is available.

Step 1: Configure PostgreSQL on Amazon RDS as the Data Source

Step 2: Configure Databricks as the Destination

Upon completing these two simple steps, which will only take a few minutes, you can seamlessly load data from PostgreSQL on Amazon RDS to Databricks.

Let’s look at some other essential features of Hevo Data that make it a must-try integration tool:

• Built-in Connectors: Hevo supports 150+ integrations to databases, including SaaS platforms, BI tools, analytics, and files. The readily available connectors simplify the process of setting up a data migration pipeline between any two platforms.
• Auto Schema Mapping: Hevo automatically maps the schema of the incoming data to the destination schema. This takes away the tedious task of schema management.
• Built to Scale: Hevo has a fault-tolerant architecture that functions with minimal latency and zero data loss. As the data volume and number of sources grow, Hevo scales horizontally. It is designed to handle millions of records per minute with negligible latency.
• Transformations: Hevo offers a Python interface and preloaded transformations with a drag-and-drop interface to simplify data transformations. You can also use its Postload transformation capabilities for data loaded in the warehouse.
• Live Support: Hevo has a dedicated support team that ensures round-the-clock help for your data integration projects. The 24×7 support includes chat, email, and voice call options.

What Can You Achieve with PostgreSQL on Amazon RDS to Databricks Integration?

Migrating your data from PostgreSQL on Amazon RDS to Databricks can help answer the following questions:

• How to cluster or segment customers based on purchasing behavior, preferences, or demographics?
• What are the emerging trends in customer preferences or behavior?
• How do customers interact across different touchpoints, such as websites, mobile apps, etc.?
• Which marketing channels have the highest ROI?
• How are resources being utilized across teams?
• Which features of a product are most and least used by customers?
• How quickly are customer support queries resolved?

Conclusion

A PostgreSQL on Amazon RDS to Databricks migration will help you achieve more with your datasets. You can unlock advanced insights, optimize your workflows, improve your operational strategies, and drive innovation.

There are two methods to integrate PostgreSQL on Amazon RDS to Databricks. The first method involves exporting Amazon RDS PostgreSQL data as CSV files and loading these files to Databricks. However, it has a few drawbacks, including being effort-intensive and lacking automation or real-time capabilities. To overcome these drawbacks, you can use a no-code tool. Such tools are often fully managed and help simplify the process of setting up a data migration pipeline.

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.

Consider using a no-code tool like Hevo Data for near-real-time data integrations. It will ensure your data warehouse always has up-to-date data for efficient analytics and decision-making.