10 Best Extraction, Transform, Load (ETL) Tools

Businesses manage a huge volume of data from a variety of sources on a daily basis. They depend on ETL technologies to help them to understand this data.

These tools are great for collecting data, cleaning it up, and storing it in an easy-to-analyse format.

Extraction, Transform, Load is ETL. Initially, data is extracted from a variety of sources, such as databases or files. After that, it undergoes a transformation, which involves the correct formatting and cleaning.

At last, it is transferred to a system that enables it to be utilized for analysis or reports. It is made sure that info from various sources works well with each other through this process.

The importance of ETL tools has increased in the last few years due to the growth of big data and cloud computing. They assist businesses in the rapid processing of substantial quantities of information, resulting in improved decisions and a competitive advantage.

Complex data operations require the correct ETL tool. The right tool can evolve with new technologies and manage big amounts of data. It should also be simple to use so teams can set up data procedures without requiring complex technological knowledge.

Operations such as real-time analytics and machine learning rely on correct and current data, which can only be achieved with a reliable ETL tool.

Getting the right ETL tool can help you save time, cut costs, and work more efficiently overall.

1. Fivetran

Fivetran is a data movement platform that is completely automated and streamlines the process of syncing data from multiple sources to data repositories.

It offers pre-built, no-code connectors that automatically adapt to source changes, in contrast to conventional ETL tools that require extensive setup and manual adjustments.

Data pipelines are reliably maintained with minimum effort because to this automation.

The platform is a preferred solution for organizations that manage significant amounts of data, as it enables real-time analytics, AI-driven workflows, and database replication.

Businesses select Fivetran for its seamless integration with major cloud services, including AWS, Google BigQuery, Snowflake, and Microsoft Azure, as well as its scalability and security.

Features

• 650+ Pre-Built Connectors – No-code connectors for the seamless integration of data from databases, SaaS applications, ERP systems, and files.
• Automated Schema Changes – The platform eliminates manual intervention by automatically adjusting data pipelines in response to changes in source structures.
• Real-Time Data synchronization – Allows continuous data synchronization to ensure that insights and analytics are current.
• Secure Data Movement – Provides compliance to industry standards such as GDPR, HIPAA, SOC 2, and ISO 27001.
• Deployment Flexibility – Provides cloud, hybrid, and on-premises deployment options to accommodate business requirements.
• Built-in Data Transformation – Allows users to sanitize and prepare data by utilizing integrations such as dbt (Data Build Tool).
• Reliability and High Uptime – Maintains a 99.9% availability rate for more than one million daily synchronizations.
• Scalability for AI and Machine Learning – Offers that businesses can efficiently transfer large quantities of data for AI-driven analytics.

Pricing

You can start using the platform with its free trial and premium pricings starts from $500/million Mar. 

2. Apache Airflow

Apache Airflow is an open-source platform that assists businesses in the automation, scheduling, and monitoring of operations.

Airflow, in contrast to conventional ETL tools, is written in Python and provides dynamic workflow administration through code.

This adaptability enables users to establish intricate workflows without relying on rigid configurations.

Airflow’s modular architecture is an ideal choice for cloud automation, machine learning pipelines, and data engineering due to its ability to scale seamlessly.

Its unique attribute is its capacity to seamlessly integrate with a diverse array of cloud services and third-party tools, which ensures the seamless transfer of data across platforms.

Airflow continues to be one of the most effective tools for managing workflows efficiently, thanks to its robust developer community and ongoing enhancements.

Features

• Python-Based Workflow Management – Automation is rendered more adaptable by the ability of users to generate, schedule, and supervise workflows using Python.
• Scalable Architecture – Airflow distributes duties through a message queue system, which enables it to manage substantial responsibilities.
• Rich User Interface – A modern web UI offers visibility into the execution of tasks, records, and workflow monitoring.
• Integrations –Supports cloud platforms such as AWS, Google Cloud, and Microsoft Azure, as well as databases and SaaS applications.
• Dynamic Workflow Scheduling – Enables the efficient management of dependencies by enabling the conditional execution of tasks.
• Extensible and Modular – Airflow’s functionality can be enhanced by users by developing custom modules, operators, and executors.
• Open Source Community – Airflow remains feature-rich and reliable due to the consistent updates and contributions of developers worldwide.

Pricing

It is free to use for everyone. 

3. Portable

Portable is an ELT (Extract, Load, Transform) tool that has been designed to facilitate the rapid and efficient connection of businesses with data sources that are difficult to locate.

It defines itself from numerous competitors by providing a fixed-price model that eliminates volume-based fees and unexpected costs.

It is an organization that specializes in the development of custom connectors, which allow businesses to access specialized data sources that are frequently unsupported by larger ETL providers.

The platform simplifies the management of data pipelines for both technical and non-technical users by offering no-code and API-driven solutions.

Portable ensures that businesses can extract and import data without concern for infrastructure limitations, due to its highly responsive support team and rapid turnaround times for new connectors.

Features

• 1,500+ Pre-Built Connectors – Allows data extraction by providing coverage for both widely used and niche SaaS applications.
• Custom Connector Development – Portable constructs new connectors within days in the event that a necessary data source is absent.
• Fixed Pricing Model – Businesses are not charged additional fees for increased data consumption, as there are no volume-based costs.
• No-Code and API-Driven Integration: The platform makes it easy for users to set up data flows, even if they don’t know how to code.
• Monitoring and Support Automation – Provides pipeline reliability through proactive monitoring and rapid troubleshooting.
• Rapid Data Syncing – Designed to facilitate the rapid loading of data into warehouses such as Redshift, Snowflake, BigQuery, and PostgreSQL.
• Enterprise-Grade Security – Secures and protects data in accordance with industry standards.

Pricing

You can start using it free for 7 days with its free trial and premium pricing starts from $290/month. 

4. Airbyte

The Airbyte platform is an open-source data migration solution that is designed for AI-driven analytics and large-scale data replication.

It has the capability to extract, import, and transform (ELT) structured and unstructured data from over 550 sources to cloud warehouses, data lakes, and vector databases.

When compared to other ETL tools, Airbyte lets users create custom data pipelines in minutes with low-code or no-code connection building.

The platform is adaptable to the infrastructure requirements of businesses with a wide range of requirements, as it supports self-hosted, cloud, and hybrid deployments.

Airbyte’s distinctive advantage is its open-source model, which allows companies to avoid vendor lock-in and capitalize on an ever-expanding ecosystem of integrations.

It is the preferred option for data engineering teams due to its compatibility with modern CI/CD tools, AI-powered workflows, and robust API support.

Features

• 550+ Pre-Built Connectors – Supports AI-specific data sources, SaaS applications, APIs, and databases.
• Custom Connector Builder – Allows the rapid development of new connectors in minutes through the use of AI-assisted tools and low-code.
• Self-Hosted and Cloud Deployment – Provides a variety of hosting options, such as Kubernetes-based scaling for large workloads.
• AI/LLM Data Integration: It lets you add unorganized data to vector systems like Milvus, Pinecone, and Weaviate.
• Python SDK (PyAirbyte) – Allows seamless integration with Python-based machine learning and analytics pipelines.
• API-Driven Data Pipelines – Enables complete programmatic control by automating data movement through the REST API.
• CI/CD Integration – Compatible with Terraform, GitHub Actions, and other DevOps tools to facilitate the automated deployment of pipelines.
• Advanced Security and Compliance – Provides compliance with ISO 27001, SOC 2, HIPAA, and GDPR, as well as data encryption and role-based access control (RBAC).
• Multi-Tenant Data Management – Allows the management of multiple data pipelines across teams through centralized governance.

Pricing

You can start using it for free and if you want to use cloud hosted it charges for volume of data to replicate (30GB/month) and number of rows to replicate from API source (4M/month) will be $360/month. 

5. AWS Glue

AWS Glue is a serverless data integration service that is fully managed and optimized for large-scale ETL and ELT operations.

It allows organizations to easily discover, prepare, and transfer data from a variety of sources to data lakes, warehousing, and analytics platforms with minimal setup.

AWS Glue is different from other ETL tools because it doesn’t require infrastructure management and can be scaled up or down instantly based on task needs.

It allows users to select the most appropriate data processing engine for their operations, such as Apache Spark, Python Shell, and Ray.

AWS Glue simplifies data engineering and reduces operational overhead by using AI-powered code generation, schema detection, and automation features.

It is the preferred option for cloud-based analytics and machine learning applications due to its extensive integration with AWS services such as Redshift, Athena, and S3.

Features

• Serverless Architecture: You don’t have to set up or handle infrastructure because AWS Glue grows itself based on how much work it has.
• Multi-Engine Support – Executes ETL tasks using Apache Spark, Python Shell, and Ray, enabling the implementation of diverse data workflows.
• AWS Glue Data Catalog – It is centralized metadata repository that is used to store table definitions, schema versions, and data source connections.
• Event-Driven ETL – Automatically initiates ETL tasks in response to the arrival of new data in Amazon S3 or other sources.
• No-Code & Code-Based ETL – Provides Visual ETL Job Creation with AWS Glue Studio and a Script Editor for Python and Spark-based workflows.
• AI-Powered Data Preparation – It uses AI-based recommendations to automate schema discovery, ETL code development, and debugging.
• Data Streaming and bulk Processing – Allows the execution of real-time data streaming and bulk ETL processes for both structured and unstructured data.
• Fine-Grained Access Control – Integrates with AWS IAM to provide secure, role-based access management.
• Data Quality Management Built-In – Offers monitoring tools to evaluate and preserve data consistency across sources.

Pricing

AWS Glue has a pay-as-you-go pricing model, which means that the cost is dependent upon the number of Data Processing Units (DPUs) consumed.

6. Meltano

Meltano is an open-source ELT (Extract, Load, Transform) platform that is specifically designed for data architects who require complete control over their data pipelines.

Meltano uses the Singer framework, which allows engineers to construct and personalize connectors for any data source, in contrast to conventional ETL tools that depend on black-box solutions.

It offers a modular architecture that facilitates the use of plug-and-play components for extraction, transformation, and orchestration, as well as version control and CI/CD integration.

Meltano’s open-source model enables businesses to maintain the flexibility and full customization of pipelines, while also preventing vendor lock-in.

It offers a command-line interface (CLI) for pipeline automation and supports more than 600 data connectors. It is an optimal choice for data teams that prioritize reproducibility and transparency due to its robust integration with Docker and Git.

Features

• 600+ Pre-Built & Customizable Connectors – Uses Singer’s open-source framework to extract data from a diversity of sources.
• Full Version Control and CI/CD Integration—It works with Git-based processes to control and release data pipelines.
• Modular & Extensible Architecture – The architecture is extensible and modular, enabling users to incorporate any tool for orchestration, transformation, and extraction.
• Command-Line Interface (CLI) – Facilitates the automation and programming of ELT processes with precise control.
• Containerized Deployments – Scalability is achieved through the seamless operation of containerized deployments in Docker and Kubernetes environments.
• Data Quality Monitoring – Provides consistency and dependability through the integration of error management and logging.
• Self-Hosted and Open-Source – Offers complete transparency without any concealed costs or vendor restrictions.

Pricing

You can start using it for free.

7. Stitch

Stitch is a cloud-based ETL platform that is specifically designed for businesses that require the rapid transfer of data from a variety of sources to analytics platforms.

It offers a zero-maintenance, fully managed solution that centralizes, imports, and extracts data without requiring a complex infrastructure.

Stitch automates data replication with minimal engineering effort, in contrast to conventional ETL tools that necessitate custom scripting and laborious pipeline management.

The platform enables the connection of databases, SaaS applications, and cloud storage services to data warehouses in a matter of minutes, with over 140 integrations.

Stitch is an optimal choice for organizations that prioritize reliability and efficacy in their data operations, as it highlights scaleability, security, and automation.

Features

• 140+ Pre-Built Data Connectors – Allows seamless integration with key SaaS applications, databases, and cloud storage.
• Automated Schema Management – Detects and implements schema modifications without the need for manual intervention.
• Incremental Data Replication – Reduces resource consumption and load times by syncing only new or updated data.
• Fully Managed Infrastructure – There is no requirement to configure servers, administer pipelines, or monitor system performance.
• Secure Data Transfers – Complies with the security standards of ISO 27001, HIPAA, GDPR, and SOC 2 Type II for secure data transfers.
• Integration with Data Warehouses – Connects to a variety of databases, including Amazon Redshift, Google BigQuery, and Snowflake.
• Scalability for High-Volume Data – Designed to accommodate enterprises that manage terabytes of data across multiple regions.

Pricing

Pricing begins at $100 per month for a maximum of 5 million rows per month.

8. Matillion

Matillion is a cloud-native data integration platform that is designed to optimize ETL (Extract, Transform, Load) procedures for organizations that are utilizing cloud data warehouses.

It operates exclusively within cloud environments, providing seamless integration with platforms such as Amazon Redshift, Google BigQuery, and Snowflake, in contrast to conventional ETL tools that necessitate on-premises infrastructure.

This cloud-centric approach supports automatic scaling, high availability, and reduced maintenance overhead.

Matillion’s distinctive advantage is its intuitive, low-code interface, which is coupled with potent transformation capabilities.

This enables data teams to efficiently design and deploy complex data pipelines. It is adaptable to a broad range of data operations due to its modular architecture, which supports a wide array of data connectors and transformation components.

Features

• Comprehensive Data Ingestion: Provides access to a wide range of data sources, such as databases, SaaS applications, and cloud storage services, through extensive data source integration.
• Visual Orchestration: Provides a drag-and-drop interface for the design of ETL workflows, facilitating the rapid development and deployment of data pipelines without the need for advanced coding.
• Scalable Processing: Uses the computational capabilities of cloud data warehouses to execute in-database transformations, thereby guaranteeing the efficient management of enormous datasets.
• Advanced Security Measures: Uses audit monitoring, multi-factor authentication, and role-based access control to ensure data security and compliance.
• Collaborative Development Environment: Version control and collaborative features are supported in the collaborative development environment, which enables multiple users to work on data projects simultaneously with streamlined change management.
• Comprehensive Monitoring and Logging: Offers real-time monitoring dashboards and detailed logs to facilitate troubleshooting and trace the performance of ETL tasks.  
• Flexible Deployment Options: Allows effortless integration into existing cloud infrastructures through cloud marketplaces such as AWS, Azure, and Google Cloud.

Pricing

Pricing starts at $0 per month, with a pay-as-you-go rate of $2.50 per credit.

9. Pentaho

Pentaho is a robust data integration and analytics platform that enables organizations to develop, oversee, and optimize ETL workflows on a large scale.

Pentaho is different from other ETL tools because it does more than just move data. It has an orchestration layer that combines organized and unstructured data sources into a single file that is ready for analytics.

The platform enables teams to efficiently orchestrate complex operations by providing a drag-and-drop visual interface and support for Python, R, and SQL-based transformations.

Pentaho’s hybrid cloud capabilities ensure that data is seamlessly transferred across a variety of infrastructure configurations, making it suitable for on-premises, cloud, and multi-cloud environments.

It is a flexible option for businesses that need to handle big data tasks. Its advanced metadata injection, machine learning model operationalization, and scalable execution make it stand out.

Features

• Visual and Code-Based ETL – Provides a drag-and-drop interface for workflow design and supports Python, R, and SQL-based transformations.
• Hybrid Cloud Integration – Links to on-premises, cloud, and multi-cloud environments, such as AWS, Azure, and GCP.
• Metadata Injection for Dynamic ETL – Automatically changes processes by using the same templates for many data sources.
• Machine Learning Integration – Supports Spark, R, Python, and Scala models for advanced AI/ML applications.
• Streaming and Batch Processing – Manages batch workloads for both structured and unstructured data as well as real-time data input.  
• Adaptable Deployment Options – Runs on Spark clusters, containers (Docker, Kubernetes), and traditional servers.
• Enterprise-Grade Security – Implements encryption, access controls, and compliance features to meet regulatory requirements.

Pricing

You can start using it for free and other pricing structure is not available on website. 

10. Oracle Data Integrator

Oracle Data Integrator (ODI) is a data integration platform of enterprise-grade that automates high-volume bulk processing, real-time event-driven integration, and service-oriented data pipelines.

ODI is different from other ETL options because it uses an Extract, Load, and Transform (ELT) method. This method uses the target databases’ built-in processing power to make changes go quickly.

This architecture optimizes efficacy for large-scale deployments, reduces latency, and minimizes data migration.

ODI 12c, the most recent version, improves developer productivity by incorporating a redesigned flow-based user interface and a more in-depth integration with Oracle GoldenGate for real-time data transmission.

It also works with big data and parallel processing, which makes it a great choice for businesses that have to deal with complex data environments.

Features

• ELT-Based Architecture – Enhances efficiency and reduces overhead by executing transformations within target databases.
• Oracle GoldenGate Integration – Allows the replication and transmission of data in real time for high-availability applications.
• Big Data Support – Provides integration with Hadoop, Spark, and cloud-based data platforms to facilitate large-scale processing.
• Declarative Flow-Based UI – Allows the development of ETL through a graphical drag-and-drop workflow designer.
• Automated Metadata Management – Ensures consistency among schemas, mappings, and transformations.
• Parallel data processing improves scalability by utilizing distributed duties and multi-threaded execution.
• Seamless Integration with Oracle Ecosystem – Compatible with Oracle Autonomous Data Warehouse, Oracle Exadata, and Oracle Analytics.
• Security & Compliance – Enforces encryption, audit monitoring, and role-based access controls to meet enterprise security standards.
• Integration with Oracle Enterprise Manager – Monitoring of data flows and performance insights from one place.

Pricing

You can download it for free and premium pricing is available on website. 

Conclusion

ETL (Extract, Transform, Load) tools have become indispensable for organizations to effectively integrate and process data from a variety of sources, which allows the development of well-informed decisions. Throughout 2025, the evolution of ETL tools is being influenced by a variety of trends:

• AI-Driven Data Management: The accuracy and efficacy of data integration tasks are being improved by the increasing automation of these tasks by AI and machine learning. 
• Focus on Privacy and Security of Data: To make sure they follow strict rules like GDPR and CCPA and keep private data safe, ETL tools are adding advanced security features.
• Methods of Unified Data Integration: The use of data fabric designs is encouraging smooth interaction between different systems, which is lowering the number of data silos and making data easier to reach. 
• Cloud-Native Solutions: Organizations are increasingly dependent on cloud-native ETL tools for their cost-effectiveness, flexibility, and scalability, particularly when managing large datasets.
• Data democratization: There is a growing focus on ensuring that data is accessible to a broader range of consumers within organizations, which allows them to derive insights without requiring extensive technical expertise. 

These trends suggest a transition to ETL solutions that are more user-friendly, secure, and automated, which are in accordance with the changing requirements of modern enterprises.