20 Best ETL & ELT Tools in 2025

ETL (Extract, Transform, Load) and ELT (Extract, Load, Transform) tools are the best modern-day methods for managing and migrating your data. If you’re still relying on legacy data integration solutions, it’s time to explore more modern platforms designed to handle the ever-growing volume of business data.

Not sure which tool (or type of tool) is the best solution for your business in 2025? We’ve got you covered. This comprehensive guide to the best ETL and ELT tools will walk you through everything you need to know to evaluate your options and find the best solution for your business. 

ETL vs. ELT Tools: What's the Difference?

ETL and ELT are two popular methods for data integration, but they take a slightly different (though significantly meaningful) approach.

ETL stands for Extract, Transform, Load, and it’s a traditional method for data integration. Here’s how it works:

1. Extract: Data is pulled from various sources such as databases, applications, and third-party systems.
2. Transform: The extracted data is then transformed into a usable format, which may involve cleaning, aggregating, and enriching the data.
3. Load: Finally, the transformed data is loaded into a data warehouse or other target destination where it can be analyzed.

ELT stands for Extract, Load, Transform. It’s a more modern approach to data integration that’s particularly suited for cloud environments. Here’s how it differs:

1. Extract: Similar to ETL, data is extracted from multiple sources.
2. Load: The extracted data is then loaded directly into a data warehouse or other storage solution.
3. Transform: Transformation occurs within the data warehouse itself, using its computational power to process the data.

Key Differences Between ETL and ELT Tools

Transformation Location

• ETL: Data is transformed before loading.
• ELT: Data is loaded first, then transformed using the data warehouse's power.

Efficiency

• ETL: Limited by the capabilities of the ETL tool and the server it runs on.
• ELT: Utilizes the enhanced processing power of cloud data warehouses, making it faster.

Scalability

• ETL: Less scalable as it depends on the ETL tool and server capacity.
• ELT: Highly scalable due to cloud infrastructure, capable of handling large volumes of data efficiently.

Flexibility

• ETL: Requires predefined transformations before loading, limiting flexibility.
• ELT: Allows transformations after loading, providing greater flexibility for future use cases.

Cost

• ETL: Generally incurs higher infrastructure costs due to the need for powerful ETL tools and servers.
• ELT: Leverages the cost-effective and scalable nature of cloud computing, often reducing overall costs.

Types of ELT and ETL Tools

When it comes to managing data integration and migration, different tools offer varied approaches. Here’s a breakdown of the main types of ELT and ETL tools available, each with its own advantages and drawbacks.

Hand-Coding

Hand-coding involves writing custom scripts to manage data migration and transformation. It’s a method often used by organizations with specific, highly customized needs.

Pros:

• Flexibility: Allows for highly tailored data integration solutions.
• Control: Offers complete control over the data processing workflow.

Cons:

• Time-Consuming: Writing and maintaining custom scripts is labor-intensive.
• Complexity: Increases complexity, especially with growing data volumes.
• Maintenance: Requires ongoing maintenance and updates by skilled developers.

Legacy ETL

Legacy ETL tools are traditional data integration tools designed primarily for on-premises environments. These tools follow the ETL process, transforming data before loading it into a data warehouse.

Pros:

• Established: Proven technology with a long history of use.
• Feature-Rich: Often come with a wide range of features beyond ETL, such as data quality and governance.

Cons:

• Speed: Typically slower than modern solutions due to on-premises limitations.
• Scalability: Limited scalability compared to cloud-native tools.
• Cost: Higher costs associated with maintaining on-premises infrastructure.

Data Pipeline Only

Data pipeline tools focus on the extraction and loading of data, without performing transformations as part of the process. These tools are often used in conjunction with other solutions that handle data transformation.

Pros:

• Ease of Use: Simplified processes for extracting and loading data.
• Performance: Efficient in moving data quickly from source to destination.

Cons:

• Limited Functionality: Lack built-in transformation capabilities, requiring additional tools or manual coding.
• Integration: May need complex integration with other systems for complete data processing.

Modern ELT

Modern ELT tools are cloud-native solutions designed to handle the entire data processing workflow, from extraction and loading to transformation. They include Data Pipeline functionality and leverage the power of cloud infrastructure for efficient and scalable data processing.

Pros:

• Scalability: Highly scalable, suitable for large data volumes.
• Efficiency: Faster processing by leveraging cloud data warehouse capabilities.
• Cost-Effective: Lower infrastructure costs due to cloud-based architecture.
• Flexibility: Allows for on-demand transformations, adapting to changing business needs.

Cons:

• Dependency on Cloud: Requires a reliable internet connection and cloud infrastructure.
• Learning Curve: May require training for teams to fully utilize all features.

How to Find the Right ELT or ETL Tool for Your Business

There’s no one-size-fits-all tool for handling every business’s date. The right solution will depend on several factors, such as price, expertise, integrations, ease of use, and scalability. Here’s a closer look at the characteristics you’ll want to examine:

• Pre-Built Integrations and Connectors: Check that the tool offers pre-built connectors for all your data sources, such as databases, SaaS applications, and cloud storage. Check if it allows creating custom connectors for unique data sources.
• Ease of Use: Look for a tool with an intuitive interface that simplifies the data integration process. Consider the learning curve and quality of support and documentation to help your team maximize the tool’s potential.
• Scalability: Evaluate the tool’s ability to process large volumes of data efficiently. Double-check that it can scale up or down based on your data needs, offering performance and elasticity for future growth.
• Pricing Structure: Compare the tool’s cost against its features to guarantee value for money. Look for transparent pricing models based on data volume, number of sources, or users, and check for flexible options that fit your budget.
• Security: Look for a tool that provides robust encryption for data at rest and in transit. Verify compliance with industry regulations like GDPR and HIPAA, and check for access controls to monitor and restrict data access.

Explore how top ETL tools use SQL under the hood. 

Best ETL Tools in 2025

When it comes to data integration and cloud data migration, having the right ETL tool can make all the difference. Here's a look at some of the best ETL tools available:

1. Matillion

Matillion is a cloud-native data integration platform, with AI functionality, designed to simplify and accelerate your ETL processes. Its intuitive interface and powerful capabilities make it a top choice for businesses looking to harness the full potential of their data in the cloud.

Key Features:

• Cloud-native architecture: Matillion is built specifically for cloud environments. It leverages the scalability and power of cloud platforms for efficient and reliable data processing.
• User-friendly interface: Matillion provides an intuitive, drag-and-drop interface that simplifies the ETL process, making it accessible even to those without technical expertise.
• Comprehensive ETL capabilities: Matillion covers all aspects of the ETL process: from extracting data from various sources to transforming it into a usable format and loading it into your target destination.
• Pre-built connectors and custom connector creation: Matillion includes a vast library of pre-built connectors (and the ability to create custom connectors) to easily integrate with a wide range of data sources.
• Robust data transformation tools: Matillion includes powerful tools for data transformation, allowing you to clean, enrich, and prepare your data for analysis.

In 2025, Matillion introduced Maia, a team of virtual data engineers that helps data teams rapidly build, optimize, and troubleshoot ELT pipelines. Whether you're building, testing, or analyzing, collaborate with Maia to accelerate pipeline development, automate repetitive tasks, and keep data flowing cleanly. aking Matillion even more productive and intuitive for engineers and analysts alike.

2. IBM DataStage

IBM DataStage is a powerful ETL tool designed to support data integration across multiple sources and targets. It is known for its high performance and scalability, making it an ideal choice for enterprises dealing with large volumes of data.

Key Features:

• High performance and scalability: IBM DataStage delivers robust performance and can easily scale to handle increasing data volumes.
• Extensive data transformation capabilities: With a wide range of transformation functions, DataStage allows for complex data manipulation to format your data for analysis.
• Integration with various databases and data warehouses: IBM DataStage integrates with numerous databases and data warehouses for smooth data transfer and consolidation.
• Advanced data quality and governance: The tool includes features for maintaining data quality and governance.
• Real-time data integration: IBM DataStage supports real-time data integration, enabling timely and up-to-date data access for critical business applications.

3. Informatica PowerCenter

Informatica is an enterprise-grade ETL platform known for its reliability and comprehensive features. It is widely used by businesses to guarantee robust and efficient data integration across various sources.

Key Features:

• Robust data integration capabilities: Informatica offers powerful data integration tools that can handle complex data integration tasks.
• Real-time and batch processing: Informatica supports both real-time and batch data processing.
• Support for multiple data sources: The platform offers extensive support for a wide range of data sources, enabling integration from diverse systems and applications.

See Matillion vs. Informatica.

4. Talend Data Fabric

Talend is anETL tool that provides a range of data integration and management solutions. It stands out for its flexibility, governance capabilities, and user-friendly interface, making it a popular choice for businesses of all sizes.

Key Features:

• Integrated Data Platform: Talend Data Fabric offers a unified suite that seamlessly integrates data integration, data quality, data preparation, and governance capabilities, thereby enabling efficient end-to-end data management across diverse environments.
• Wide range of connectors: Talend offers an extensive library of connectors, enabling seamless integration with various data sources and applications.
• Big data integration: Talend provides powerful big data integration tools, enabling organizations to manage and process large datasets efficiently within big data environments.

See Matillion vs. Talend.

5. SAP BusinessObjects Data Services

SAP BusinessObjects Data Services is a robust ETL tool designed to integrate and transform data from various sources. It provides comprehensive data quality features and deep integration with SAP environments, making it ideal for businesses seeking reliable and scalable data management solutions.

Key Features:

• Comprehensive data quality and profiling: SAP Data Services offers advanced data quality and profiling tools.
• Integration with SAP and non-SAP sources: The tool seamlessly integrates with both SAP and non-SAP sources, providing flexibility and compatibility for diverse data environments.
• Metadata management: SAP Data Services includes robust metadata management capabilities, allowing users to track and manage data lineage and transformations.
• Data cleansing and enrichment: The platform excels in data cleansing and enrichment, enabling businesses to improve data quality and derive more meaningful insights from their data.

6. Oracle Data Integrator (ODI)

Oracle Data Integrator (ODI) is a comprehensive data integration platform that supports high-performance ETL and ELT processes, enabling seamless data movement across enterprise systems.

Key Features: 

• ELT architecture: ODI uses an extract-load-transform (ELT) approach, reducing processing overhead and improving efficiency.
• Comprehensive connectivity: Integrates with cloud, on-premises, and big data platforms, ensuring broad compatibility.
• Automated data workflows: Oracle Data Integrator offers declarative flow-based design for automating data transformation, cleansing, and enrichment.

7. Apache NiFi

Apache NiFi is an open-source data integration tool designed for automating and managing real-time data flows with high scalability.

Key Features: 

• Flow-based processing: Apache NiFi provides a drag-and-drop interface for building real-time data workflows without coding.
• Scalable data streaming: Supports large-scale data ingestion, routing, and transformation across distributed systems.
• Fine-grained data security: Apache NiFi ensures secure data flow management with access control, encryption, and audit logging.

8. Pentaho Data Integration (PDI)

Pentaho Data Integration (PDI) is an enterprise-grade ETL tool that provides data transformation and integration capabilities across various sources.

Key Features: 

• Graphical ETL development: PDI offers a visual interface for designing and orchestrating data pipelines without writing code.
• Hybrid and big data support: Natively integrates with cloud, on-premises, and big data ecosystems for unified data processing.
• Embedded data analytics: Pentaho Data Integration enables advanced analytics and reporting by integrating with BI platforms for real-time insights.

9. Hevo Data

Hevo Data is a fully managed no-code data pipeline platform that enables real-time data movement and transformation for analytics.

Key Features: 

• Instant data replication: Hevo Data supports real-time data ingestion from multiple sources to cloud data warehouses.
• Pre-built transformations: Provides built-in transformation templates for cleansing, deduplication, and enrichment.
• Automated schema management: Hevo Data detects and applies schema changes dynamically to ensure data consistency.

10. Astera Centerprise

Astera Centerprise is a powerful data integration solution that simplifies complex ETL processes with an intuitive, no-code approach.

Key Features: 

• Drag-and-drop data mapping: Astera Centerprise allows users to create data pipelines visually, reducing development effort.
• Parallel processing engine: Ensures high-speed data transformation and movement for large-scale integration tasks.
• Data quality and governance: Astera Centerprise offers built-in data validation, profiling, and governance features for improved accuracy.

Best ELT Tools

Unlike traditional ETL tools, ELT solutions leverage the computational power of cloud data warehouses. Here are some of the best tools to make it happen:

1. Matillion

Matillion is a cloud-native data integration platform that simplifies and accelerates the ELT process by leveraging the power of leading cloud data warehouses.

Key Features:

• Cloud-native architecture: Matillion seamlessly integrates with cloud data warehouses like Snowflake, Google BigQuery, and Amazon Redshift to offer superior performance and scalability.
• User-friendly interface: The platform provides an intuitive drag-and-drop interface, making it accessible to users of all technical skill levels.
• Comprehensive ELT capabilities: Matillion handles the complete extract, load, and transform process within the cloud environment, maximizing efficiency and reducing data latency.
• Pre-built connectors and custom connector creation: Matillion offers a wide range of pre-built connectors and tools to create custom connectors.
• Advanced transformation tools: The platform includes robust data transformation features, enabling complex data manipulations directly within the cloud data warehouse.

In 2025, Matillion introduced Maia, a team of virtual data engineers that helps data teams rapidly build, optimize, and troubleshoot ELT pipelines. Whether you're building, testing, or analyzing, collaborate with Maia to accelerate pipeline development, automate repetitive tasks, and keep data flowing cleanly. aking Matillion even more productive and intuitive for engineers and analysts alike.

2. Fivetran

Fivetran is a Data Pipeline tool that includes free Fivetran Transformations for dbt Core to form a data integration platform that automates the ELT process, allowing businesses to centralize their data with minimal effort.

Key Features:

• Automated data pipelines: Fivetran offers fully automated data pipelines, reducing the need for manual intervention and maintenance.
• Seamless integration: The platform provides seamless integration with a wide range of data sources, including databases, SaaS applications, and cloud storage solutions.
• Incremental data updates: Fivetran supports incremental data updates, guaranteeing only changed data is synced (which optimizes performance and reduces costs).
• Reliable and secure: Fivetran emphasizes data security and reliability, with features like automatic backups, monitoring, and compliance with industry standards.

See Matillion vs. Fivetran.

3. Stitch

Stitch is a simple, extensible Data Pipeline tool that helps businesses move data from various sources to their data warehouse with ease.

Key Features:

• Simplicity and ease of use: Stitch provides an intuitive interface and easy setup, allowing users to quickly start integrating their data without extensive technical knowledge.
• Scalable architecture: Stitch’s architecture is designed to scale with your data needs, accommodating growing data volumes and varying workloads efficiently.
• Open-source integration: Stitch supports open-source integration, giving users the flexibility to extend and customize the platform according to their specific requirements.

4. Azure Data Factory

Azure Data Factory is a cloud-based data integration service that manages and automates the movement and transformation of data from various sources to destinations.

Key Features:

• Comprehensive data integration: Azure Data Factory supports integration with a wide range of data sources, including on-premises databases, cloud storage, and SaaS applications.
• Built-in data transformation: Azure Data Factory offers built-in data transformation capabilities using Data Flow, enabling complex data transformations without the need for additional tools.
• Serverless architecture: As a serverless service, Azure Data Factory eliminates the need for infrastructure management, allowing users to focus on developing data workflows.
• Integration with Azure services: The platform seamlessly integrates with other Azure services, such as Azure Synapse Analytics and Azure Machine Learning, providing a comprehensive solution for advanced analytics and machine learning workflows.

5. Google Cloud Dataflow

Google Cloud Dataflow is a fully managed stream and batch data processing service that enables users to develop and execute data processing pipelines with ease.

Key Features:

• Unified stream and batch processing: Google Cloud Dataflow supports both stream and batch data processing, allowing users to handle real-time and historical data within the same pipeline framework.
• Auto-scaling: The platform automatically scales resources up or down based on workload demands for optimal performance and cost-efficiency without manual intervention.
• Integration with Apache Beam: Cloud Dataflow uses Apache Beam SDKs, enabling users to write pipelines in multiple programming languages and run them on various processing engines.
• Seamless integration with Google Cloud ecosystem: Google Cloud Dataflow integrates with other Google Cloud services, such as BigQuery, Cloud Storage, and Pub/Sub, providing a comprehensive data processing and analytics solution.

6. AWS Glue

AWS Glue is a serverless data integration service that can be used for analytics, machine learning and application development. It makes it easy for data from multiple sources to be discovered, prepared, moved and integrated. 

Key Features: 

• Discover and Organize Data: AWS Glue crawlers catalog, index, and search data across multiple sources to manage schemas and permissions, and connect to diverse data sources to build a data lake.
• Transform, Prepare, and Clean Data: AWS Glue uses a visual job editor to define ETL processes, automate scheduling, clean streaming data in transit, leverage machine learning for deduplication, and interactively edit, debug, and test ETL code.
• Build and Monitor Data Pipelines: DAWS Glue dynamically scales resources, automates jobs with event-based triggers, monitors performance with Spark or Ray engines, and defines workflows for seamless ETL and integration activities.

7. Dataform

Dataform is a data transformation tool which streamlines the process of transforming raw data into actionable insights. It has been designed for modern cloud data warehouses, enabling teams to easily and efficiently manage ELT workflows efficiently.

Key Features:

• Discover and organize data: Dataform centralizes and manages data models within cloud warehouses, version control SQL workflows and collaborate on data transformation projects.
• Transform, prepare and clean data: Dataform uses SQL-based transformations, automates dependencies and runs tests to ensure data quality and consistency prior to analysis.
• Build and monitor data pipelines: Schedules and orchestras ELT workflows, tracks job performance and ingrates seamlessly with BigQuery and other cloud data platforms. 

8. Airbyte

Airbyte is an open-source data integration platform that simplifies ETL and ELT processes by providing pre-built connectors for seamless data movement across various sources and destinations.

Key Features:

• Democratize data access: Syncs data and builds new connectors or cloud intelligence platforms using no-code or low code.
• Optimize data operations: Streamlines data operations and AI, using optimal security and governance to create data infrastructure that is efficient and trusted.
• Customizable Connectors: Pre-built connectors or build your own with Airbyte’s Connector Development Kit (CDK) to support unique data sources.

9. Rivery

Rivery is a cloud-native ELT platform that automates data integration, transformation, and orchestration without requiring infrastructure management.

Key Features:

• No-code ELT automation: Rivery streamlines data extraction, loading, and transformation with an intuitive, code-free interface designed for rapid deployment.
• Pre-built and customizable data pipelines: Utilizes pre-configured data pipelines or create custom workflows to move and transform data efficiently across cloud environments.
• Built-in orchestration and management: Rivery automates workflows with scheduling, event triggers, and dependency management, while gaining real-time insights into pipeline performance.

10. ETLeap

ETLeap, as a fully managed ETL solution, is designed for modern data pipelines, and eliminates the need for complex infrastructure management. It simplifies data integration for analytics and reporting. 

Key Features:

• Schema-aware data ingestion: ETLeap automatically detects and adjusts schema changes to ensure seamless data integration without manual intervention.
• Code-free transformation: Enables users to clean, enrich, and structure data using an intuitive, no-code transformation interface.
• Automated scaling & monitoring: ETLleap dynamically scales resources to handle varying workloads while providing real-time monitoring and alerts for pipeline health.

ELT for Cloud Data Migration

Modern ELT tools simplify cloud data migration by loading raw data into your destination warehouse before transforming it. Tools like Matillion automate much of this process, reducing engineering effort and minimizing downtime.

Matillion’s pipeline orchestration and metadata-aware transformations, enhanced by Maia, make it particularly effective for large-scale, phased migrations.

Partner with the Best Modern-Day ETL/ELT Tool

The right ETL/ELT tool can streamline your data processes, maintain data accuracy, and uncover valuable business insights. And modern-day tools (like Matillion) provide the scalability, efficiency, and flexibility needed to handle today's complex data environments.

Don’t just take our word for it—see for yourself. Sign up for free and give it a try, or schedule a demo with our team to get a hands-on walkthrough of the platform and its capabilities.