PostgreSQL to Redshift Migration Guide for Automated, Managed, and Manual Workflows

When the files on your PostgreSQL server become a storage liability rather than a growth engine, it delays your team’s ability to mine insights. Move your precious data to Amazon Redshift to prevent loss of time and delay in critical business decisions. 

Data engineers tasked with moving data between storage systems like applications, databases, data warehouses, and data lakes fall into a trap. Think long, cumbersome hours spent in manual CSV formatting, S3 staging, and schema mapping. The result is delayed data availability, higher chances of human error, and engineering burnout. 

Follow this step-by-step guide to transfer your data from PostgreSQL to Redshift and start delivering value from your data. 

What is PostgreSQL?

PostgreSQL is a powerful open-source relational database known for its flexibility, reliability, and strong support for modern data types. It’s widely used across industries and works equally well on Linux, Windows, and macOS.

Key Features of PostgreSQL

• 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.
• Reliable & Fault-Tolerant: Features like write-ahead logging ensure data integrity and high availability.

What is Amazon Redshift?

Amazon Redshift is a fully managed cloud data warehouse built for fast and scalable analytics. It uses powerful parallel processing to handle massive datasets and complex queries with ease.

Key Features of Amazon Redshift

• Managed & Cost-Efficient: Redshift handles setup, scaling, and maintenance automatically, with flexible pricing so you only pay for what you use.
• Highly Scalable: Easily scales from gigabytes to petabytes, keeping performance fast as your data grows.
• Optimized for Analytics: Built for OLAP workloads, Redshift runs complex queries quickly on large datasets.
• AWS Ecosystem Integration: Effortlessly connects with services like S3, Glue, and EC2 for orderly data workflows.
• Serverless, Sharing & Security: Supports serverless queries, secure data sharing, and includes built-in encryption, compression, and high availability.

Read more: Amazon Redshift vs. PostgreSQL: A Comparison

At a Glance: How to Connect or Migrate Postgres to Redshift

A comparison between the three methods available to you for Postgres Redshift replication or migration: 

Parameter	Hevo Data (No-Code)	AWS DMS (Managed)	S3 + COPY (Manual)
Ease of Use	High: Fully visual, no-code setup. Guided UI allows for connection in <5 minutes.	Moderate: Requires AWS console configuration, security groups, and IAM role management.	Low: Requires SQL scripting, CLI knowledge, and manual file management.
Scalability	Automated: Features an auto-scaling architecture and event-based scaling without manual tuning.	Semi-Automated: Requires you to choose the right replication instance size; needs manual intervention for spikes.	Manual: You must manually partition files and manage cluster resources to prevent bottlenecks.
Maintenance	Minimal: Auto-healing pipelines, intelligent retries, and automatic schema evolution.	High: Requires frequent manual intervention to fix sync lag, connection breaks, or LOB issues.	Extremely High: Every schema change in Postgres breaks the pipeline, requiring manual script updates.
Reliability	High: Fault-tolerant with batch-level checks and real-time observability logs.	Moderate: Prone to sync lag; failures often require restarting tasks from scratch.	Low: High risk of human error, data type mismatches, and silent failures during the COPY process.

A Detailed Guide: Postgres to Redshift Connection or Migration

Your migration strategy will depend on how much engineering effort, data reliability, and scalability you need. Depending on whether you need a one-time or real-time transfer, here are three methods to choose from: 

• Method 1: Automated Data Pipelines (Hevo Data): A fully managed, no-code ELT solution that offers speed, transparency, and reliability. This is the best choice for teams that want to set up automated, fault-tolerant pipelines in minutes without manual coding or infrastructure maintenance.
• Method 2: Manual Migration (CSV & S3 COPY): A traditional approach involving manual data exports and SQL commands. While it gives granular control, it still needs ample developer time to manage schema mapping, file staging in S3, and error handling.
• Method 3: AWS Database Migration Service (DMS): A managed service within the AWS ecosystem. It is ideal for users already comfortable with AWS console configurations who need a middle-ground solution for full-load migrations and basic change data capture (CDC).

Let’s look at each method in detail so that you can choose the one that best fits your workflows: 

Method 1: Automated Data Pipelines (Hevo Data)

Hevo is a fully managed, no-code ELT platform that makes data movement simple, reliable, and transparent. Hevo ensures your data stays trustworthy while your engineering team focuses on high-impact projects by automating the heavy lifting of schema mapping and maintenance. Hevo is also available on the AWS Marketplace, allowing for effortless billing and integration for Redshift users.

Prerequisites before you start

Make sure you have:

• IP address/hostname and credentials for your PostgreSQL server (version 9.5 or above).
• An active Amazon Redshift account with database credentials.
• A registered Hevo Data account.
• Access to the Team Administrator, Team Collaborator, or Pipeline Administrator role in Hevo to create the Pipeline.
• Access to the Source and the Destination. 
• Assigned the necessary PostgreSQL privileges (SELECT, USAGE, CONNECT) to the user.
• Enabled log-based incremental replication (WAL/logical replication) in PostgreSQL for real-time sync

Preparing PostgreSQL for Hevo integration

1. Enable log-based incremental replication by updating your PostgreSQL config files (set wal_level to logical, configure max_replication_slots, and set the right permissions in pg_hba.conf).
2. Whitelist Hevo’s IP addresses in the PostgreSQL server to allow Hevo connections.
3. (Optional) Create a dedicated database user for Hevo, then grant privileges with:

GRANT CONNECT ON DATABASE <database_name> TO <user>;
GRANT USAGE ON SCHEMA <schema_name> TO <user>;
GRANT SELECT ON ALL TABLES IN SCHEMA <schema_name> TO <user>;
ALTER DEFAULT PRIVILEGES IN SCHEMA <schema_name> GRANT SELECT ON TABLES TO <user>;

Step 1: Sign up and log in to Hevo

Create a free account on Hevo Data. From your dashboard, click + Create Pipeline to begin the setup.

Step 2: Set up PostgreSQL as a Source

Choose PostgreSQL from Hevo’s 150+ source catalog.

• Select Pipeline Model and Type: Logical Replication is the default for real-time, high-performance sync. Choose Edge for lower latency and localized processing. Choose Standard for traditional cloud-based movement. 
• Enter Credentials: Provide your Database Host (IP or DNS), Port (default 5432), Read-only User, and Password.
• Advanced Settings: Enable Load Historical Data to ingest all existing data before starting incremental sync. Toggle Include New Tables on so Hevo automatically tracks and replicates any new tables you create in Postgres in the future.

Note: Learn more about PostgreSQL as a source here.

Step 3: Configure Amazon Redshift as a Destination

• Select Destination: Select Amazon Redshift from Hevo’s destination catalog.
• Connection settings: Enter your Cluster Endpoint, Port, Database User, and Password.
• Security/connectivity: Enable SSH Tunnel (Recommended) to connect without exposing your database to the public internet. If not using SSH, add Hevo’s regional IP addresses to your Redshift VPC Security Group’s inbound rules.
• Schema mapping: Hevo securely connects and automatically maps your Postgres fields to the target Redshift schema.

Note: Learn more about Amazon Redshift as the destination here.

Source

Step 4: Finalize and activate

Once configured, Hevo continuously syncs PostgreSQL data to Redshift. You can monitor pipeline health, view transformation logs, and check the Historical Load Progress directly from the unified dashboard.

Key Benefits: 

• No-code automation: Move data without writing complex COPY commands or maintaining Python scripts.
• Auto-healing pipelines: Hevo’s fault-tolerant architecture automatically retries failed tasks, ensuring zero data loss during network blips.
• Edge vs. Standard flexibility: Choose the pipeline architecture that fits your compliance and speed requirements.
• Automated schema handling: Hevo detects source schema changes (like new columns) and updates Redshift automatically without downtime.
• End-to-end transparency: Track latency, event usage, and job history with real-time logging and audit tables.

New for 2026: 

• Edge pipelines: Introduced for teams requiring extremely low-latency data replication.
• Updated SSL support: Now includes mandatory CA File and Client Certificate validation for higher security standards.
• Unified workspace roles: New Team Administrator and Pipeline Administrator roles for better access control during setup.

Start your free trial and connect PostgreSQL to Redshift in minutes with Hevo Data.

Method 2: Manual Migration (CSV & S3 COPY):

A custom ETL (Extract, Transform, Load) workflow will give you greater granular control, provided you have dedicated engineering resources and a one-time migration need. In this method, you export data to CSV, stage it in an S3 bucket, and use the Redshift COPY command for ingestion.

Step 1: Extract data from PostgreSQL

First, extract your table data into a flat-file format (CSV) that Redshift can parse.

• Access your PostgreSQL terminal (psql) or use a client like pgAdmin.
• Run the \copy command to export your data locally.

\copy (SELECT * FROM products) TO '/tmp/products_data.csv' WITH DELIMITER ',' CSV HEADER;

• Note: For large datasets, it is a 2026 best practice to compress this file (e.g., using GZIP) before uploading to reduce transit time and storage costs.

Step 2: Stage data in Amazon S3 

Redshift is optimized to ingest data from S3. Upload your CSV to a bucket in the same region as your Redshift cluster.

• Log in to the AWS Management Console and navigate to S3.
• Create a new bucket (e.g., my-migration-staging) and click Upload.
• Select your products_data.csv file.
• Security tip: Keep your bucket private. Instead of making it public, you will use an IAM Role to permit Redshift to read the files.

Step 3: Configure Redshift permissions

Redshift requires an IAM Role to access your S3 bucket securely.

• In the IAM Console, create a new role for the Redshift – Customizable service.
• Attach the AmazonS3ReadOnlyAccess policy to this role.
• Note the Role ARN (e.g., arn:aws:iam::123456789012:role/MyRedshiftRole).
• Associate this role with your Redshift cluster under the Actions > Manage IAM Roles menu.

Step 4: Load data into Redshift

Once the data is in S3 and permissions are set, use the high-performance COPY command to move data into your target table.

• Open the Redshift Query Editor v2.
• Ensure your target table exists. If not, create it first.
• Execute the COPY command:

COPY products

FROM 's3://my-migration-staging/products_data.csv'

IAM_ROLE 'arn:aws:iam::123456789012:role/MyRedshiftRole'

FORMAT AS CSV

IGNOREHEADER 1

REGION 'us-east-1';

• Verify the load by running SELECT COUNT(*) FROM products;.

When to use this method

• One-time migrations: You only need to move a static dataset once and don’t require ongoing synchronization.
• Granular control: You need to manually transform data or filter specific rows during the export phase.
• Zero software budget: You have the engineering time to build and troubleshoot the workflow yourself.

Limitations to consider

• No real-time sync: This is a snapshot migration. Any changes made in PostgreSQL after Step 1 will not be reflected in Redshift.
• Schema drift: If you add a column in PostgreSQL, your COPY command will fail, requiring you to manually rebuild the target table and re-run the script.
• High maintenance: You are responsible for monitoring failures, managing S3 storage lifecycles, and ensuring data types map correctly (e.g., PostgreSQL JSONB to Redshift SUPER).

Tired of managing manual CSV exports? Try Hevo Data for free and automate your PostgreSQL to Redshift pipeline in under 5 minutes with zero maintenance. 

Method 3: AWS Database Migration Service (DMS)

AWS DMS is a managed migration service that helps you move data to Redshift with minimal downtime. It acts as an intermediary, capturing changes from your PostgreSQL source and applying them to your Redshift cluster. This is ideal for teams who want to stay entirely within the AWS ecosystem and have the technical capacity to manage AWS VPC and IAM configurations.

Step 1: Create a Replication Instance

The replication instance performs the actual data movement.

• Log in to the AWS DMS Console and select Replication Instances.
• Click Create Replication Instance.
• Choose an instance class (e.g., dms.t3.medium). For 2026, you can also opt for AWS DMS Serverless, which automatically scales resources based on your data volume.
• Specify your VPC and ensure the instance has network access to both your PostgreSQL source and Redshift target.

Step 2: Create Source and Target Endpoints

Endpoints tell DMS exactly where your data is coming from and where it is going.

• Source Endpoint (PostgreSQL): Enter your connection details, including the server name, port (5432), and credentials. Pro Tip: For Change Data Capture (CDC), ensure your Postgres wal_level is set to logical.
• Target Endpoint (Redshift): Provide your Redshift cluster identifier and database credentials.

Note: AWS DMS requires an intermediary S3 bucket to stage data before moving it into Redshift. You will need to provide the S3 bucket name and an IAM role with access to it.

• Click Run Test for both endpoints to ensure connectivity.

Step 3: Create a Database Migration Task

The task defines what data is moved and how it is moved.

• Go to Database Migration Tasks and click Create Task.
• Select your Replication Instance and the Endpoints you created.
• Migration type: Choose between:

• Migrate existing data: For a one-time full load.
• Migrate existing data and replicate ongoing changes: For continuous, real-time sync.
• Table mappings: Use the selection rules to specify which schemas and tables to include. You can also add transformation rules here (e.g., prefixing table names).

Step 4: Monitor and complete the migration

• Start the task. AWS DMS will begin the Full Load phase, followed by Ongoing Replication.
• Monitor the task status and CloudWatch metrics for any latency or errors.
• Once the Full Load is 100% complete and Ongoing Replication is in sync, your data is live in Redshift.

When to use this method

• AWS-native environments: You are already using AWS and prefer to keep all data traffic within your VPC for security and compliance.
• Large-scale migrations: You are moving massive databases and need the performance of a dedicated replication instance.
• Complex transformations: You need to perform basic column renaming or schema filtering during the migration process.

Limitations to consider

• Configuration overhead: Requires deep knowledge of AWS IAM roles, VPC peering, and security groups.
• Incremental maintenance: AWS DMS does not automatically handle all complex schema changes; if a source table undergoes a significant structural change, the task may fail and require manual intervention.
• Cost predictability: Costs can fluctuate based on the replication instance hours and the amount of data transferred, making it less predictable than flat-tier or event-based pricing.

Looking for a simpler way? Hevo Data offers a more intuitive, no-code alternative to AWS DMS that sets up in minutes rather than hours. Compare Hevo vs. AWS DMS here.

Why Replicate Data from Postgres to Redshift?

Migrating from Postgres to Redshift means a strategic transition from a transactional Online Transactional Processing (OLTP) environment to an analytical Online Analytical Processing (OLAP) powerhouse. While Postgres is excellent for handling day-to-day application queries, it isn’t designed to crunch through terabytes of historical data for business intelligence.

Here is why data-driven teams prioritize this migration:

1. Centralized analytics and reporting – Create a single source of truth instead of having data scattered across various production databases. Centralized data will ensure your marketing, finance, and product teams make decisions based on the same consistent data set. 
   
2. Superior performance for complex queries – Redshift uses columnar storage over PostgreSQL’s row-based storage. You’ll notice the Redshift advantage when you run a report to calculate Total Revenue by Month. Redshift will only calculate the Revenue and Date columns, skipping everything else. This reduces I/O hugely; so while analytical queries take minutes to finish in Postgres, it takes seconds in Redshift. 
   
3. Join operational data with multi-source analytics – Your business lives in more than just a database. With a Redshift migration, you can join your production records with data from CRM tools (Salesforce, HubSpot), advertising platforms (Google Ads, Facebook), and support desks (Zendesk). 
   
4. Huge scalability and BI integration – Redshift is built to scale horizontally. As your data grows, you can simply add more nodes to your cluster to maintain performance. Additionally, Redshift is the gold standard for integration with BI tools like Tableau, Looker, and PowerBI. The native Redshift connectors in these tools are ideal for high-speed data visualization. 
   
5. Advanced Analytics via Redshift Spectrum – Redshift Spectrum lets you run federated queries that join the data sitting in your Redshift local disks with vast amounts of unstructured data stored in Amazon S3 data lakes. This way, you can run deep-dive analytics on operational and historical data without having to load the data in the warehouse itself. 

Optimize your PostgreSQL to Redshift Pipeline

You’ll need to replicate data from PostgreSQL to Amazon Redshift to ensure your raw data is actually worth business intelligence. While Postgres is built to handle your daily transactions, Redshift provides the columnar storage and huge scalability required to run complex analytical queries across millions of rows in seconds.

The best migration method ultimately boils down to three key decision points. One, if you have the engineering bandwidth to build, monitor, and troubleshoot custom SQL scripts or complex AWS DMS configurations. Two, if your business needs near real-time insights (CDC), or if you can wait for manual daily batches. Three, if you’re in a position to manually fix broken pipelines every time your source schema evolves or an API changes. 

For teams that prioritize speed and reliability, a no-code ELT solution like Hevo offers a fully managed solution that removes the friction from data movement. With its transparent pricing, auto-healing pipelines, and complete end-to-end visibility, Hevo ensures your data remains trustworthy and accessible at every stage of the migration. Instead of spending weeks managing infrastructure, you can have your data flowing into Redshift in less than five minutes.

Stop wrestling with manual exports and fragile scripts. Experience a simple, reliable, and transparent way to connect your data.

Start Your 14-Day Free Trial with Hevo Data Today and join 1,000+ data-driven companies that trust Hevo for their mission-critical pipelines.

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