Replicating data from PostgreSQL on Amazon RDS to Redshift offers a multitude of benefits, unlocking the full potential of your data-driven initiatives. Amazon RDS provides a scalable and fully-managed relational database solution, ensuring effortless deployment and efficient data management. Meanwhile, Amazon Redshift, with its robust analytics capabilities, delivers a powerful data warehousing solution optimized for complex queries and massive datasets.

By replicating data, you create a centralized repository that enables real time analytics and empowers data-driven decision-making. This seamless integration allows you to leverage Redshift’s performance, data processing capabilities, and scalability. As a result, you can drive deeper insights, identify trends, and gain a competitive edge in today’s data-driven landscape.

Read on to explore the two straightforward methods explained in this article to achieve a seamless data pipeline that enhances your ability to make data-driven decisions.

Method 1: Load Data from PostgreSQL on Amazon RDS to Redshift

This method divides the integration and replication process into three different steps. To begin, we’ll export data from AWS RDS for PostgreSQL into CSV files. Next, the downloaded files will be uploaded into the Amazon S3 bucket. Finally, the data will be replicated from the S3 bucket to Amazon Redshift.

For PostgreSQL on Amazon RDS to Redshift data migration, follow these steps:

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

  • Use the PostgreSQL administration tool or psql to connect to your Amazon RDS PostgreSQL database. You’ll require the endpoint of your RDS instance, username, and password. If you’re using psql, enter the following command:
psql -h rds_endpoint -U username -d database_name

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

  • Use the COPY command to export the data to a CSV file. Replace the table_name and file_path with the table name, desired file path, and name for the CSV file.
COPY table_name TO 'file_path' WITH CSV HEADER;

WITH CSV HEADER option will include the column headers in the CSV file.

  • You can also export one or more tables into a CSV file using the COPY command using the UNION clause. But remember, the data from the different tables should have the same column structure to be combined successfully in the CSV file.
  • After executing the above command, specified table data will be exported into a CSV file. Verify and clean the data before uploading it into the S3 bucket.

Step 2: Upload CSV Files to Amazon S3

You’ve got two options to upload CSV files to S3 using AWS console and AWS CLI. This step covers both ways, allowing you to choose the one that suits you.

  • Using AWS Console: Navigate to the S3 service.
  • Create a bucket in Amazon S3. If you’re already using the existing one, move on to the next step to upload a CSV file.
  • Click on Create Bucket.
  • Follow the prompts to name your bucket, select a region, configure any additional settings as needed, and choose Create.
  • Select the name of the new bucket. Choose the Actions button, and click Create Folder in the drop-down list. Next, name the new folder.
  • Upload and Verify CSV files
  • With your S3 bucket selected or created, click on the bucket’s name to open its details page and select the folder to move the CSV files.
  • Click on the Upload button to start the file upload process.
  • In the file upload dialog, click on Add Files or drag and drop the CSV files you want to move to the S3 bucket.
  • Confirm the upload by clicking the Start Upload button.
  • Once the upload is complete, you can verify that the CSV files are now present in your S3 bucket. Click on the bucket name and navigate through the folder to find your uploaded CSV files.
  • Using AWS CLI: Open the AWS CLI.
  • Use the following command to upload CSV files to S3:
aws s3 cp path_csvfile.csv s3://bucket-name/

Replace path_csvfile with the local path to your CSV file and bucket-name with the specific folder and bucker name of your S3 bucket.

Step 3: Amazon S3 to Amazon Redshift

  • Create a new IAM role or use an existing one that has permission to access the S3 bucket where the CSV file is stored.
  • Attach the AmazonS3ReadOnlyAccess policy to the IAM role.
  • Run the COPY command to import the data from the CSV file into your Redshift table.
COPY redshift_table_name 
FROM 's3://bucket-name/folder-name/csv-file.csv' 
CREDENTIALS 'aws_iam_role=redshift_iam_role' 

Replace redshit_table_name with the actual name of your Redshift table, and redshift_iam_role with the ARN of the IAM role. The COPY command will load the data from the CSV file stored in the S3 bucket into redshift_table_name in Redshift.

  • After executing the COPY command, verify that the data has been transferred correctly.

That’s it! You have manually connected PostgreSQL on Amazon RDS to Amazon Redshift using CSV files.

Although utilizing a manual approach with CSV files may require significant time and effort, it proves advantageous for limited datasets or one-time transfers from Amazon RDS PostgreSQL to Redshift. It is efficient and manageable, making it an ideal choice for one-time backups without the need to invest in specialized software.

Limitations of Manual Migration from PostgreSQL on Amazon RDS to Redshift

While loading data from PostgreSQL on Amazon RDS to Amazon Redshift using CSV files and S3 offers certain advantages, it also comes with some limitations:

  • Time-Consuming: Exporting data from RDS PostgreSQL to CSV files and then loading them into Redshift using S3 is a manual process. It can be time-consuming and prone to human errors, especially for larger datasets. For instance, exporting larger datasets to CSV files can demand a considerable time, and the process may need to be repeated periodically. Additionally, if there are frequent updates or changes in the data, keeping both databases synchronized becomes a challenging task, requiring constant monitoring and coordination.
  • Data Cleaning: It is a crucial step in the data integration process when using CSV files. It includes rectifying errors and identifying inconsistencies present in the data before it is loaded into the target database. CSV files might contain various data quality issues, such as missing values, duplicate records, and inconsistent data formats. If these issues aren’t handled properly, they will impact data analysis. 

Method 2: Using a No-Code Tool to Build PostgreSQL on Amazon RDS to Redshift ETL Pipeline

Hevo Data is a cloud-based data replication service that accelerates the process of extraction, transformation, and loading of data from various sources and destinations. It is designed to meet the demands of modern data-driven organizations. Hevo enables real-time data ingestion, ensuring the data is continuously updated and available for analysis.

With its extensive library of 150+ pre-built connectors, Hevo eliminates the need for manual coding and custom integrations. This makes the data integration and replication process effortless and agile.

Here are the steps involved in PostgreSQL on Amazon RDS to Redshift integration using the Hevo platform:

Step 1: Connect and Configure Amazon RDS PostgreSQL as Data Source

Connect Amazon RDS PostgreSQL to the Hevo platform by simply entering database details, such as database host, port, username, and password.

PostgreSQL on Amazon RDS to Redshift: Configure Source
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Step 2: Connect and Configure Amazon Redshift

To replicate data from AWS RDS PostgreSQL to Redshift, simply provide your Redshift database credentials, including the Database Port, Username, Password, Name, Schema, and Cluster Identifier, along with the Destination Name.

PostgreSQL on Amazon RDS to Redshift: Configure Destination
Image Source

That’s it! You’ve seamlessly loaded data from PostgreSQL on Amazon RDS Redshift in 2 steps using the Hevo Data platform.

Check out some of the unique features of the Hevo Data platform:

  • Fully Managed: With Hevo being a fully managed data replication platform, you’re free from infrastructure management efforts. The Hevo Data pipelines are designed to effortlessly scale and handle large volumes of data.
  • Auto Schema Mapping: The platform automatically maps the data fields between the source and target, making the integration process hassle-free. It also provides auto schema management to handle any schema changes in the source data.
  • Data Transformation: Hevo offers a range of data transformation capabilities, including preload and post-load functionalities. You can leverage the platform’s intuitive no-code drag-and-drop feature for simple data transformations. However, for advanced transformations, you can use the Python console.
  • Monitoring and Alerts: Hevo provides real time monitoring of data pipelines, allowing you to keep track of data flows and performance. In case of any errors, delays, or issues during the data replication process, Hevo triggers an alert and notifies you immediately. This proactive alerting and monitoring enables you to rectify the problems and ensure smooth data flow.
  • Data Security: Hevo Data adheres to stringent data security and retention policies, ensuring the privacy and confidentiality of your data. It complies with SOC2, HIPAA, GDPR, and CCPA regulations, providing robust protection. The platform connects to sources and destinations via SSH tunnel, while SaaS sources are encrypted through HTTPS.
  • Live Support: Hevo offers round-the-clock support to its customers through documentation, chat, email, or calls.

What Can You Achieve with PostgreSQL on Amazon RDS to Redshift ETL?

  • Enhanced Customer Insights: Centralizing various data, including customer interaction, sales data, and website behavior, help you gain a comprehensive view of business operations. This allows you to obtain insight into customer preferences, buying patterns, and engagement across different channels, leading to personalized marketing and targeted campaigns. For instance, segmentation based on attributes like location, purchase history, and behavior allows tailored marketing, enhancing overall strategy effectiveness.
  • Gain Deeper Team Insights: By loading data from PostgreSQL on Amazon RDS to Redshift, you can gain deeper insights into team performance and dynamics. For instance, analyzing individual contributions and project completion time can help identify team strengths and areas for improvement.
  • Acquire In-Depth Customer Insights: By using Redshift to analyze how customers interact with your products, you can create personalized suggestions based on their preferences and past purchase. This makes their buying experience more tailored, leading to increased chances of them buying related or upgraded products. In return, this benefits both customers, who find relevant products, and the business, which sees improved sales and customer satisfaction.


So far, you’ve seen two different approaches for integrating PostgreSQL on Amazon RDS to Redshift. The first method was using the CSV files and Amazon S3, and the second included the Hevo Data Platform. Each approach has its benefits and is suited for specific scenarios.

Using CSV files and Amazon S3 provides a manual approach for one-time data transfer. While it can be suitable for small datasets or occasional backups, it might not be efficient for real time integration due to its time-consuming nature.

In contrast, Hevo provides a fully managed solution that automates the data integration and replication process. With its real time data ingestion, transformation, and loading capabilities, you can effortlessly transfer data from Amazon RDS PostgreSQL to Redshift.

If you are interested, you can try Hevo by signing up for the 14-day free trial.

Visit our Website to Explore Hevo

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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