10 PostgreSQL ETL Tools That Will Shape Your Data Strategy in 2025

PostgreSQL is the most trusted database for developers and businesses, owing to its reliability, adaptability, and scalability. But even a powerful database like PostgreSQL can be challenging to manage when you need to move, transform, or integrate data from multiple sources. 

This is when ETL tools make the process simple and help teams focus on insights rather than manual data handling. Whether you are running real-time analytics or managing complex batch processes, ETL tools let you extract data, transform it into the correct format, and load it into PostgreSQL. 

With so many ETL tools available, choosing the right one can be tricky. Some excel at real-time syncs, others at handling complex transformations, and some are perfect for small teams or startups. 

In this blog, we explore the 10 best PostgreSQL ETL tools in 2025, highlighting their key features, pricing, pros and cons, and real-world use cases. Towards the end, you will have better clarity of which tools fit your PostgreSQL workflows and team needs.

List of Most Popular ETL Tools for PostgreSQL

So now that we know why we need these PostgreSQL ETL tools, let’s examine some of the best ETL tools on the market ranked by G2.

1. Hevo

G2 Rating: 4.3 / 5(234)

Hevo Data is a no-code, fully managed ETL platform designed to simplify data integration into PostgreSQL. Trusted by over 2,000 data teams across 45+ countries, Hevo empowers enterprises to centralize their data effortlessly, enabling faster analytics and data-driven decision-making.

Tailored for teams leveraging PostgreSQL as their central data warehouse, Hevo automates the extraction, transformation, and loading (ETL) processes. It seamlessly integrates with multiple data sources, ensuring that data flows into PostgreSQL in real-time, maintaining accuracy and consistency without manual intervention.

For organizations aiming to streamline their data operations, Hevo offers a robust solution that reduces engineering overhead, accelerates time-to-insight, and enhances the reliability of data pipelines. Its intuitive interface and powerful features make it a preferred choice for modern data teams.

Key Features

• Native PostgreSQL connector with automated schema mapping
• Real-time data pipelines for up-to-date analytics
• 150+ pre-built connectors for SaaS apps, databases, and cloud storage
• In-flight transformations to ensure clean, usable data in Postgres.
• Integrates with PostgreSQL’s Write-Ahead Logs (WAL) for reliable and consistent data replication.

Pros

• User-friendly, no-code interface for easy pipeline setup
• Scalable for small teams and enterprise-level Postgres operations
• Robust support and documentation for Postgres workflows

Cons

• Limited customization for highly complex ETL logic
• Pricing may be high for smaller teams or startups
• Some learning curve for users transitioning from traditional ETL tools

Pricing

• 14-day free trial available
• Paid plans start at $239/month, pricing based on monthly records processed.

2. Fivetran

G2 Rating: 4.2 / 5(379)

Fivetran is an automated data integration platform built for simplicity and reliability. For teams using PostgreSQL as a central warehouse, it continuously extracts data from multiple sources, applies transformations in the destination, and keeps your Postgres tables synchronized in real time. 

Designed for modern data teams, Fivetran handles complex workflows such as incremental updates and high-volume replication efficiently, so engineers can focus on analysis rather than maintenance. Its connectors cover a wide range of SaaS applications, databases, and APIs, all tailored to feed data into PostgreSQL accurately and securely.

For organizations of all sizes, Fivetran offers a reliable and scalable solution that reduces manual work, prevents replication errors, and speeds up decision-making with consistent, analytics-ready Postgres data.

Key Features

• Auto detects and adapts to schema changes in PostgreSQL tables
• Supports all major PostgreSQL versions, including RDS, Aurora, and on-premises instances.
• Provides automated transformations in the warehouse, simplifying the preparation of PostgreSQL data for analytics.
• Tracks replication jobs and alerts on failures, ensuring reliable data pipelines.

Pros

• No need to manage servers or write ETL scripts.
• Supports growing data volumes and complex Postgres workflows.
• Works seamlessly across different Postgres environments.

Cons

• Exact costs depend on usage and require inquiry.
• Some advanced transformations may need external tools.
• Highly specific workflows may require additional configuration.

Pricing

• Free trial available
• Paid plans based on monthly active rows and destination warehouse

3. IBM Datastage

G2 Rating: 4.0 / 5(69)

IBM DataStage is an enterprise-grade data integration platform designed to handle complex ETL and ELT workflows. For teams working with PostgreSQL, it enables the extraction, transformation, and loading of large volumes of data efficiently while supporting parallel processing for faster performance.

The platform’s drag-and-drop interface allows users to design and manage pipelines without extensive coding, making it easier to implement sophisticated PostgreSQL workflows. It also optimizes data movement, reducing latency and ensuring Postgres tables are ready for analytics and reporting.

For organizations with high-volume or complex Postgres datasets, DataStage provides reliability, scalability, and flexibility, helping teams maintain consistent, accurate data pipelines while minimizing manual intervention.

Key Features

• Parallel processing accelerates PostgreSQL ETL jobs
• Optimized data movement reduces latency and costs
• Built-in monitoring and error handling ensures pipeline reliability
• Flexible ETL/ELT patterns adapt to PostgreSQL workflows

Pros

• Reduces the need for manual ETL scripting
• Supports large-scale PostgreSQL datasets efficiently
• Simplifies complex workflow management for teams

Cons

• Cloud integration can be complex
• Licensing fees vary depending on deployment
• Limited number of pre-built connectors compared to competitors

Pricing

• Free plan available for small datasets
• Based on capacity unit hours

4. Pentaho Data Integration(PDI)

G2 Rating: 4.3 / 5(15)

Pentaho Data Integration (PDI) is a versatile data orchestration platform designed to help teams consolidate multiple data sources into a PostgreSQL environment. It supports both batch and real-time ETL, enabling organizations to centralize and manage their Postgres data efficiently for analytics and reporting.

The platform offers two primary components: Spoon, a visual, codeless interface for designing data transformations and ETL workflows, and Kitchen, which executes these workflows reliably. Together, they allow PostgreSQL users to automate and streamline data ingestion, transformation, and loading processes without heavy coding.

For teams managing complex PostgreSQL datasets, PDI provides scalability, flexibility, and multi-cloud support. Its ability to handle large volumes of data, coupled with integrated reporting, ensures that Postgres tables remain accurate, up-to-date, and ready for analytics-driven decision-making.

Key Features

• Codeless interface for designing PostgreSQL ETL workflows
• Support for multi-cloud and hybrid PostgreSQL environments
• Scalable processing for large PostgreSQL datasets
• Flexible execution environments for batch and real-time pipelines
• Integrated reporting for analytics-ready Postgres data

Pros

• Flexible data integration across diverse PostgreSQL sources
• Supports multi-cloud and hybrid deployments
• Highly scalable for growing datasets
• Flexible execution for different ETL scenarios

Cons

• Limited documentation for advanced PostgreSQL setups
• Occasional bugs and glitches
• Resource-intensive for very large PostgreSQL datasets
• Limited customization for highly specific workflows

Pricing

• Subscription-based enterprise edition for advanced features and support
• Free community edition available

5. Apache Airflow

G2 Rating: 4.3/5(86)

Apache Airflow is an open-source workflow orchestration tool that automates, schedules, and monitors complex ETL and ELT pipelines. For teams working with PostgreSQL, it provides a reliable framework to orchestrate data workflows, ensuring tasks run on time and in the correct sequence without manual intervention.

Airflow integrates with PostgreSQL through dedicated hooks and operators, allowing teams to extract, transform, and load data efficiently into Postgres tables. Its DAG-based scheduling system, logging, and monitoring features help teams manage pipeline dependencies, track execution status, and quickly troubleshoot issues.

Organizations choose Airflow because it offers flexibility, scalability, and automation for PostgreSQL-related data workflows. Its open-source ecosystem, extensive integrations, and active community support make it a strong choice for teams looking to streamline and manage Postgres ETL processes.

Key Features

• Incremental task execution: only runs the tasks that need updating, reducing load on Postgres
• PostgreSQL hooks and operators: native support for connecting, querying, and loading data into Postgres
• DAG-based scheduling: orchestrates complex ETL/ELT workflows reliably
• Real-time monitoring and logging: track Postgres pipeline execution and troubleshoot errors

Pros

• Automates PostgreSQL ETL/ELT workflows, reducing manual intervention
• Flexible orchestration for complex Postgres data pipelines
• Real-time monitoring and logging improve reliability
• Highly scalable for growing Postgres workloads

Cons

• No built-in version control for workflow changes
• Steep learning curve for new users managing Postgres pipelines
• Production setup and maintenance can be resource-intensive
• Documentation may not cover all Postgres-specific use cases

Pricing

• Free, as it is open-source. 

6. Microsoft SSIS

SQL Server Integration Services (SSIS) is Microsoft’s flagship ETL tool, built for managing data integration, migration, and transformation tasks at scale. While it is deeply embedded in the Microsoft ecosystem, SSIS can also integrate with PostgreSQL through ODBC drivers and third-party connectors, enabling teams to move data between SQL Server, Postgres, and other systems.

For PostgreSQL users, SSIS provides powerful transformations, scheduling, and workflow automation, making it a reliable choice for organizations that already rely on Microsoft technologies but want to extend their data operations into Postgres. Its visual package designer allows users to create complex ETL pipelines without extensive coding.

Teams choose SSIS when they need enterprise-grade performance, broad connectivity, and robust data management while keeping PostgreSQL aligned with existing Microsoft infrastructure.

Key Features

• Pre-built transformations and workflows to streamline ETL pipelines into Postgres
• Visual package designer for creating and managing data flows without coding
• Strong scheduling and automation for recurring Postgres data loads
• Catalog database for centralized storage and management of ETL packages

Pros

• Seamless integration with Microsoft ecosystem and tools
• Highly flexible for complex ETL scenarios with Postgres
• Intuitive drag-and-drop design improves productivity
• Strong automation and scheduling capabilities

Cons

• Requires SQL Server license, making it costly
• Primarily designed for Microsoft environments, with limited Postgres-native support
• Complex setup for hybrid or non-Microsoft systems

Pricing

• Varies based on SQL Server edition and licensing model.

7. Talend

Talend is a modern data integration and management platform that supports ETL and ELT pipelines across cloud, hybrid, and on-premises setups. For PostgreSQL users, Talend provides built-in connectors that make extracting data simple from diverse sources and transform it into analytics-focused tables in Postgres.

With its drag-and-drop interface and advanced data quality features, Talend enables teams to design complex pipelines while ensuring clean, consistent data flows into PostgreSQL. Its open-source base combined with enterprise-ready capabilities makes it versatile for both mid-sized companies and large enterprises.

Teams choose Talend for its wide range of connectors, flexibility across architectures, and ability to deliver trusted data into PostgreSQL at scale.

Key Features

• Native PostgreSQL connector for streamlined ETL pipelines
• Drag-and-drop interface for pipeline design without heavy coding
• Data quality and governance features ensure clean Postgres datasets
• Supports hybrid, multi-cloud, and on-prem architectures

Pros

• Easy to set up with broad connectivity, including PostgreSQL
• Open-source foundation with enterprise-grade options
• Compatible with AI/ML workflows for advanced use cases

Cons

• Customer support response times can be slow
• Pricing can be high for small teams or startups
• Requires expertise for large-scale Postgres deployments

Pricing

• Now part of Qlik. Pricing is available upon request from the sales team.

8. Informatica PowerCenter

Informatica PowerCenter is a robust data integration platform widely adopted for large-scale ETL and data governance. For PostgreSQL users, it offers strong support for integrating multiple data sources and transforming them into analytical datasets within Postgres environments. Its advanced metadata management and automation features help maintain consistency and accuracy across complex pipelines.

With built-in data quality checks and monitoring, PowerCenter ensures that data loaded into PostgreSQL is clean, reliable, and compliant with governance requirements. It is designed for organizations handling mission-critical data where performance, accuracy, and governance are non-negotiable.

Enterprises choose Informatica PowerCenter when they need a scalable, governance-focused ETL solution that integrates smoothly with PostgreSQL while supporting compliance and wide collaboration.

Key Features:

• Native PostgreSQL connectivity for data extraction and loading
• Built-in data quality and governance tools to ensure reliable Postgres datasets
• Metadata-driven workflows for pipeline automation and transparency
• Scalable architecture for high-volume PostgreSQL ETL workloads

Pros

• Strong governance and data quality features for Postgres pipelines
• Highly scalable for enterprise-level PostgreSQL use cases
• AI-powered cataloging improves visibility into data assets

Cons

• High licensing costs can be prohibitive for smaller businesses
• Cloud replication into PostgreSQL can be time-consuming
• Requires specialized expertise for administration

Pricing

• Volume-based pricing model, with a free plan and three paid tiers available for cloud data management.

9. Airbyte

Airbyte is an open-source ETL platform designed for replicating and syncing data across systems. For PostgreSQL users, it offers native connectors and CDC (Change Data Capture) support, enabling reliable, real-time replication of Postgres data to warehouses, lakes, or other destinations.

With over 350 pre-built connectors and a Connector Development Kit (CDK), teams can extend Airbyte to support custom Postgres sources or targets. Its open-source model ensures flexibility, while its version control and scheduling features help maintain consistent Postgres pipelines.

Organizations choose Airbyte for high-volume, customizable, and real-time Postgres replication that is scalable, transparent, and adaptable to evolving data architectures.

Key Features

• Automated schema mapping for PostgreSQL tables
• Incremental replication for efficient Postgres data updates
• Built-in monitoring dashboards for pipeline health
• Native support for PostgreSQL-specific data types

Pros

• Extensive connector library including PostgreSQL
• Supports large-scale Postgres replication efficiently
• Open-source transparency and community support

Cons

• Cloud pricing per credit can be unclear
• Scheduler can be tricky, occasionally interrupting jobs
• Frequent updates require regular maintenance

Pricing

• Team & Enterprise: Contact sales
• Open Source: Free
• Cloud: Free trial, then $360/mo for 30GB/month

5. Apache Nifi

Apache Nifi is an open-source data flow automation tool designed to transfer and process data across systems efficiently. PostgreSQL users can leverage NiFi’s processors to move data from multiple sources into Postgres, automate ingestion workflows, and apply transformations in real time.

NiFi’s flow-based programming model allows teams to design dynamic ETL pipelines with prioritization, back-pressure handling, and runtime flow modifications, making Postgres data ingestion resilient and low-latency. Its capabilities also extend to event streaming, cybersecurity data pipelines, and AI data preparation.

Organizations select NiFi for loss-tolerant, high-throughput, and dynamically configurable Postgres data pipelines, particularly when automating complex workflows across multiple systems.

Key Features

• Native PostgreSQL processors for ingesting and routing data
• Fine-grained provenance tracking for Postgres datasets
• Built-in data encryption and secure transmission support
• Configurable flow templates to standardize Postgres pipelines

Pros

• Ensures reliable, loss-tolerant Postgres data ingestion
• Supports high-throughput pipelines efficiently
• Dynamic configuration and prioritization of workflows
• Open-source flexibility for complex Postgres flows

Cons

• Limited documentation
• State persistence challenges with node failover
• Running long SQL queries can be difficult
• Setup for complex Postgres pipelines requires expertise

Pricing

• Free, as it is open-source.

Comparison of the PostgreSQL ETL Tools

ETL Tool	Ease of Use	Support	Integration Capabilities	Pricing
Hevo	User-friendly interface, No-code	24/7 customer support, comprehensive	Supports 150+ data sources, real-time data	transparent tier-based pricing
SSIS	Moderate, requires some technical knowledge.	Strong customer support. Good amount of documentation.	Integrates with Microsoft tools.	SQL server license required. Various pricing models.
Fivetran	Very easy, automated	24/7 support, extensive documentation	Supports 400+ data connectors, automated ELT	Subscription-based, transparent pricing
IBM Datastage	Complex,  requires expertise	Robust support, comprehensive	Extensive integration capabilities	Enterprise pricing, typically expensive
PDI	Moderate, some learning curve	Comprehensive support, community	Integrates with many databases and services	Subscription-based, tiered pricing, free trial available
Apache Airflow	Complex, requires expertise	Community support, some enterprise	Highly customizable, many integrations	Free, open-source
Talend	User-friendly Interface	Poor customer support	Contact sales to get the quotations. Free version is also available.	Various connectors are suited for real-time data flows.
Informatica	Complex- requires expertise	Extensive support options, community	Highly scalable, 200 pre-built connectors	Expensive, enterprise-focused
Airbyte	Easy, open-source, customizable	Community support	350+  pre-built connectors	Free, open-source
Apache Nifi	Low, requires technical knowledge.	Good, active and supportive community of users.	Contact sales to get the quotations. The free version is also available.	Free, open-source

What are the key considerations while choosing a PostgreSQL ETL tool?

Choosing an ETL tool for PostgreSQL affects how smoothly you move, process, and use your data. Here are the main things to keep in mind:

1. Connector Coverage

Make sure the tool can connect to all your important data sources and destinations. Built-in connectors for databases, cloud platforms, and SaaS apps save time, and the option to add custom connectors is useful for special needs.

2. Scalability and Performance

The tool should handle large datasets and grow with your business. Look for features like parallel processing and real-time updates to keep data pipelines reliable as volumes increase.

3. Ease of Use

Pick a tool that matches your team’s skill level. No-code or low-code tools let non-technical users manage pipelines, while code-based tools give more control for complex workflows.

4. Deployment and Platform

Decide whether cloud or on-premise works best for your setup. Cloud tools offer flexibility and automatic updates. On-premise tools give more control and security for sensitive data.

5. Support and Community

Good support helps avoid downtime. Commercial tools usually provide dedicated customer service. Open-source tools rely on community forums and documentation.

6. Pricing and Value

Consider total cost, not just the license. Think about how much time, effort, and errors the tool can save. A higher upfront cost can be worth it if it improves efficiency.

What is PostgreSQL? 

You must be wondering why we must migrate data to/from PostgreSQL. Probably one of the many reasons why the decision to learn about PostgreSQL will be an essential career investment in your life, Postgres is a free, open-source, Object-relational Database Management System that is robust, scalable, and supports advanced data types and performance optimization features— it’s used for small projects and substantial enterprise systems alike.

Benefits of Using PostgreSQL

• Open Source
• Highly extensible
• Supports MVCC (Multi-version currency control)
• Point-in-Time Recovery
• Tablespaces
• Asynchronous replication
• Nested transactions

What is a PostgreSQL ETL Tool?

• A PostgreSQL ETL tool extracts data from various sources, transforms it to fit desired formats, and loads it into a PostgreSQL database for storage or analysis.
• These tools automate and streamline the ETL (Extract, Transform, Load) process, allowing for efficient data migration and integration with PostgreSQL.
• They are especially useful when handling large datasets, complex transformations, or frequent data updates.
• PostgreSQL ETL tools often support various data formats, making it easier to integrate data from diverse sources like databases, APIs, or cloud storage.
• Using an ETL tool helps ensure data accuracy, reduces manual work, and speeds up the process of moving data into PostgreSQL for insights or reporting.

Also, take a look at how the process of ETL works step-by-step to get a detailed understanding of the process.

Conclusion

Having an ETL tool to migrate your data to/from your PostgreSQL database can ease the pressure of creating manual data pipelines and provide more time for analyzing loaded data. This blog provides a list of various viable ETL PostgreSQL tools, along with the pros and cons of each. 

Sign up for Hevo’s 14-day free trial and explore more about the numerous data migrations possible with its no-code platform.

FAQ on ETL Tools for PostgreSQL