Organizations today have access to an immense volume of data. To put it into perspective, in 2024, an estimated 149 zettabytes of data were generated globally—that’s over 138 trillion gigabytes or 135 billion iPhone 16 Pros!

However, this data will remain untapped without the appropriate means to extract, transform, and load it into a storage system for decision-making. Consequently, ETL tools have become indispensable for forward-thinking organizations looking to integrate data without relying heavily on IT.

What are ETL tools?

ETL tools are software solutions that allow businesses to extract data from disparate sources, transform it, and load it into target systems, such as a data lake or data warehouse, for reporting and analytics.

These tools help leverage large amounts of data from multiple sources. AI-driven ETL solutions, like Astera, support real-time or batch processing, offer scalable architectures, and integrate with other technologies such as business intelligence (BI) and data visualization tools.

Here are the best ETL tools in 2025 at a glance:

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How Does an ETL Tool Help Data Teams?

Traditionally, businesses set up ETL pipelines through manual coding, which is inefficient and time-consuming. The primary problem with the programming stack is that data is manipulated by the element instead of columns and rows, making it challenging to integrate heterogeneous sources.

Moreover, adding or changing data pipelines requires building on top of previous libraries and complex code integration. Therefore, modern businesses have started using automated ETL tools that can handle intricate ETL processes more efficiently.

ETL tools simplify the ETL process by abstracting these complexities and enabling non-technical users to handle data transformations and workflows. This abstraction streamlines development, maintenance, and scalability, making ETL processes more accessible and efficient for a broader range of users within an organization.

Here are some reasons why you should opt for an ETL tool:

ETL Tools Build Data Pipelines Faster

Like other data integration tools, ETL tools use automation to integrate data and build data pipelines faster. They come equipped with pre-built connectors for diverse data sources and destinations, minimizing the need for custom coding and allowing for faster implementation. They also offer a visual interface for designing and managing data pipelines more seamlessly, so users with minimal to no coding experience can quickly build and maintain data pipelines.

ETL Tools are Future-Proof

Modern ETL tools are designed to be adaptable and flexible to handle constantly changing data requirements and technologies. Most ETL tool vendors continually update functionalities and add connectors in response to new technologies and best practices.

ETL Tools Can Handle Complex Data Easily

Handling complex and unstructured raw data is challenging. ETL tools have evolved to address this challenge by incorporating features and capabilities to handle data in various formats and structures. Moreover, many ETL solutions allow users to write custom transformations to ingest, cleanse, and manipulate complex data.

ETL Tools Reduce Error Probability

ETL tools significantly reduce the risk of errors in data pipelines, mainly through automation. Eliminating manual tasks also removes the risk of human errors. Moreover, many ETL tools come with data validation controls to ensure that data passes the specified criteria before reaching a target destination. The visual interface and workflow also help reduce the probability of errors.

What are the Different Types of ETL Tools?

The growing demand for effective data management and the increasing volume, variety, and velocity of data have led to the development of different classes of tools, including:

Open-Source ETL Tools

Open-source ETL tools are free to use. You can easily access their source code and extend its functionality to meet your data requirements. While open-source ETL tools are flexible, they lack the user-friendly UI most commercial tools feature. Moreover, they offer limited functionality, and a given open-source solution will likely be incompatible with your existing data pipelines.

Custom ETL Tools

Many businesses design their ETL solutions in-house using data pipelines and programming languages like Python and SQL. While these tools enable greater customization to meet specific data requirements, they are time-consuming to build and complex to maintain. An in-house ETL solution is feasible for businesses dealing with limited data and simple data pipelines.

Cloud-based ETL Tools

Cloud-based ETL tools allow you to manage data from various cloud-based applications. These solutions are deployed in the cloud to process large volumes of data without investing in additional infrastructure.

Enterprise ETL tools

Enterprise ETL software are specialized solutions for large organizations to perform ETL processes efficiently. You can ingest data from disparate sources into a centralized data repository for reporting and analytics. These solutions have advanced capabilities to perform complex data transformations and handle data processing at scale.

Many vendors charge an annual license fee or follow a pay-as-you-go model. Top providers offer extensive training and resources in addition to the ETL functionality. Enterprise ETL tools are easy to use, even for business users, as they feature no-code interfaces powered by drag-and-drop and point-and-click functionalities.

What are The Top ETL Tools in 2025?

Today, several ETL tools are available on the market, differing in their offerings and the types of use cases they support.

1. Astera
2. Integrate
3. Fivetran
4. Talend
5. Hevo Data
6. Skyvia
7. SAS Data Management
8. Pentaho
9. Stitch
10. Blendo
11. Airbyte
12. Azure Data Factory
13. Oracle Data Integrator
14. Apache Airflow
15. Portable.io

Astera

Astera is an AI-driven, cloud-based platform for enterprises that want to simplify their ETL and ELT processes. Equipped with an intuitive, easy-to-use, no-code interface, it allows you to create automated data pipelines that integrate and process data from disparate sources. AI-driven semantic mapping maps and aligns data fields automatically across different sources and destinations. Beyond ETL, Astera provides an end-to-end data ecosystem for data extraction, data integration, electronic data interchange (EDI), API management, and data warehousing.

Key features of Astera:

• Powerful ETL Engine: Built on a robust parallel-processing engine, Astera effortlessly handles large volumes of datasets, ensuring smooth, efficient, and high-speed ETL operations.
• Automation and Orchestration: Astera streamlines workflows through job scheduling features, allowing you to set up processes once and automate execution.
• Cloud-Based Data Preparation: No matter your technical proficiency, you can quickly clean, transform, and analyze data through AI-powered, cloud-based data preparation tools.
• AI-Powered Data Extraction: The solution offers intelligent document processing. AI-powered extraction allows you to effortlessly capture data from semi-structured and unstructured documents.
• Data Transformation Capabilities: Astera offers a range of built-in advanced transformations and functions, such as Tree Join, Normalization, sort, filter, distinct, etc., simplifying data manipulation. You can easily drag and drop the required transformations and map them within your data pipelines.
• Extensive Connector Library: Astera supports a wide array of connectors for both on-premises and cloud-based sources and destinations, including databases, data warehouses, and data lakes. Additionally, you can easily connect to other sources or destinations through pre-built API connectors or build custom connectors for unique use cases.
• Data Quality Assurance: The tool features robust built-in data quality functionalities that enable data cleansing, profiling, and validation. You can also define data quality rules to flag erroneous records for subsequent review.
• Data Security and Governance: Astera’s ecosystem enhances data integrity and privacy through advanced security and data governance mechanisms, deterring unauthorized access or potential breaches.

Pricing: Astera offers custom pricing according to your specific data integration and management use case and needs. Contact us to get a quote.

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Integrate.io

Integrate.io is an ETL tool designed for data integration and transformation. The tool comes with a range of customization, integration templates, monitoring and logging features, and CDC capabilities, available through different pricing packages.

Key features of Integrate.io:

• Data Source Compatibility: io supports various data sources, including databases, cloud services, web apps, and flat files.
• Transformation Functions: The tool comes with data cleaning, enrichment, and transformation capabilities.
• Cloud Integration: io offers scalability and easily integrates with cloud platforms.

Pricing: Credit-based with 4 tiers. Starts at $3/credit.

Fivetran

Fivetran is a data movement platform that automates the extraction and loading of data from various source systems into a centralized data warehouse or destination. The tool supports the ETL process via dbt, allowing you to access and analyze your data for analytics.

Key features of Fivetran:

• Broad Source Compatibility: Fivetran supports a range of data sources, including databases, cloud services, applications, and APIs.
• Schema Mapping and Transformation: It offers schema mapping and basic data transformation.
• Monitoring and Alerts: The tool includes monitoring and alerting features to track data pipeline performance and detect issues.

Pricing: Five tiers: Free, Starter, Standard, Enterprise, and Business Critical. Free trial available for every tier.

Talend

Talend (acquired by Qlik) is a data integration and transformation platform that assists organizations in ingesting, cleansing, converting, and moving data from diverse sources into a target destination. The platform is compatible with on-premises and cloud-based data sources.

Key features of Talend:

• Talend provides a range of data transformation capabilities for cleaning and enriching data.
• Users can get both data integration and governance features within a single platform.
• The tool offers an extensive range of connectors and adaptors for various data sources and platforms to support integration.

Pricing: Variable, based on four tiers: Starter, Standard, Premium, and Enterprise.

Hevo Data

Hevo Data is an ETL platform that supports data integration, movement, and processing. Like other ETL tools, it supports a wide array of data sources and destinations.

Key features of Hevo Data:

• Hevo provides both Python-based and pre-built transformations.
• It provides near real-time data movement capabilities.
• The tool also has monitoring and alerting features, allowing you to track ETL job performance and receive notifications about anomalies.

Pricing: Four tiers: Free, Starter (starting at $239 per month), Professional (starting at $679 per month), and Business Critical (custom pricing).

Skyvia

Skyvia is an ETL platform that allows businesses to automate data extraction from various sources, perform data transformations, and load it into target destinations for data integration and processing.

Key features of Skyvia:

• The platform supports incremental data updates, reducing the need for full data transfers during ETL.
• Skyvia includes error-handling mechanisms to identify and manage issues during data integration.
• The tool supports data archiving, allowing organizations to securely store historical data for compliance and historical analysis.

Pricing: Five tiers: Free, Basic at $99 per month, Standard at $199 per month, Professional at $249 per month, and Enterprise (custom pricing). These plans are available in monthly or annual packages, with a 20% discount on the latter.

SAS Data Management

SAS Data Management provides ETL capabilities for integrating data from diverse sources, such as databases, CRM platforms, etc. It allows you to extract data from various file formats, apply transformations to meet desired formats and standards, and load the processed data into target systems for analysis and reporting.

Key features of SAS Data Management:

• The platform provides metadata management to document and track data lineage.
• It supports real-time and batch ETL processing to meet various data integration requirements.
• The platform includes error-handling mechanisms and logging features to troubleshoot ETL issues.

Pricing: Available on request.

Pentaho

Pentaho is an open-source ETL platform that enables businesses to perform data integration and analytics. The platform has a free community edition but offers a commercial license for enterprises. It supports various processes such as ingestion, cleansing, standardization, and storage.

Key features of Pentaho:

• You can extend Pentaho’s ETL capabilities with custom plugins and scripts for greater customization. However, it requires technical expertise.
• Pentaho supports integration with big data technologies like Hadoop, so you can process large datasets.
• The platform includes data quality features to clean and validate data during ETL processes.

Pricing: Available on request, with four tiers: Developer, Starter, Pro, and Pro Suite.

Stitch

Stitch is a cloud-based data integration service that enables ETL processes. It provides a platform for extracting, transforming, and loading data from various sources into the destination of choice.

Key features of Stitch:

• Stitch supports ETL from various data sources, including databases, cloud applications, and APIs.
• The tool has automated and scheduled data replication capabilities to update data.
• It is designed as a cloud-native ETL service that can accommodate changing data needs.

Pricing: Three tiers: Standard ($100 per month or $1000 per year), Advanced ($1250 per month, billed annually), and Premium ($2500 per month, billed annually).

Blendo

Blendo is a self-service ETL tool that provides access to various cloud data sources. It allows you to automate data transformation and transfer raw data sets into the database or a cloud data warehouse.

Key features of Blendo:

• Blendo supports connectivity to Redshift, BigQuery, and other data warehouses.
• It offers the ability to automate the entire ETL process.
• Blendo is designed to scale with evolving data requirements.

Pricing: Starts at $250 per year. Free trial available.

Airbyte

Airbyte is an open-source data integration tool. It allows users to ETL/ELT data to various data lakes, warehouses, and databases. It specializes in data movement and requires external tools, such as dbt, to transform data.

Key features of Airbyte:

• RAG and unstructured data integration for loading unstructured data.
• Connectivity to various databases.
• Data security and governance for deployment models.

Price: Tier-based with four tiers: Open-Source (self-hosted and free), Cloud, Team, and Enterprise (self-hosted).

Azure Data Factory

Azure Data Factory is Microsoft’s cloud-based service for creating and managing data flows on the cloud. It enables both programmatic and UI-based approaches to handling data.

Key features of Azure Data Factory:

• Data preview and validation.
• Custom event triggers for ETL
• Security features, including role-based access controls.

Price: Variable. Based on pipeline orchestration and execution, data flow execution and debugging, and data factory operations.

Oracle Data Integrator

Oracle Data Integrator (ODI) is Oracle’s offering for all data integration processes, including ETL. It supports various integration requirements and is compatible with Oracle Warehouse Builder (OWB) and Oracle Enterprise Manager.

Key features of Oracle Data Integrator:

• SOA-enabled data services.
• ETL with Big data support.
• Parallel processing for improved ETL

Price: Variable. Based on GBs consumed, workspace usage, pipeline execution, and OCI streaming.

Apache Airflow

Apache Airflow is another open-source tool with support for ETL. It utilizes Python frameworks for managing data pipelines, allowing ETL workflows to be dynamic and extensible

Key features of Apache Airflow:

• Workflow parameterization using Jinja engine.
• Multiple workflow automation options.
• Visual record of all workflows and pipeline tasks.

Price: Open-source/free.

Portable.io

Portable is a cloud data integration software that provides connectivity to many sources and can design custom integrations on demand. Portable.io can also ELT/ELT data to various major databases.

Key features of Portable:

• Data replication across many long-tail sources.
• Dedicated 24/7 customer support for source data changes.
• Free development and running of new connectors.

Price: Four tiers, billed monthly: Starter ($290 per month), Scale ($1490 per month), Pro ($2490 per month), and Enterprise (custom pricing).

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What Features to Look for in an ETL Tool

Before you invest in an ETL platform, you must evaluate its capabilities & features to determine whether it’ll meet your data management requirements. Here are some important features that an ETL solution should have:

1. Data Extraction: An effective ETL tool should be able to connect to a wide range of data sources, including databases (SQL, NoSQL), APIs, and various file formats (CSV, JSON, XML). It should also support unstructured data extraction, so you can easily extract data from PDF and scanned documents to optimize data transfer and reduce processing time.
2. Library of Connectors: Modern ETL tools offer an expansive library of connectors, including file formats, databases, and cloud platforms. Ensure that the tool you purchase can natively support your data sources.
3. Ease of Use: Managing custom-coded ETL mappings is a complex process that requires deep development expertise. To save developer resources, you need an enterprise ETL solution that offers an intuitive, code-free environment to extract, transform, and load data. Evaluate the tool’s user interface for ease of use, as well as its support for no-code ETL development. A no-code solution can provide substantial cost savings in the long run because hiring technical resources to manage and maintain ETL pipelines can be costly. 
4. Data Transformations: Data transformation needs vary from simple transformations such as lookups and joins to more complex tasks like denormalizing data or converting unstructured data into structured tables. You should select an ETL tool that offers a range of simple and more advanced transformations per your data manipulation requirements.
5. Data Quality and Profiling: You want only clean and accurate data loaded into your repository. So, prioritize data quality management and look for an ETL platform that offers inbuilt data quality and profiling capabilities to determine enterprise data’s consistency, accuracy, and completeness.
6. Automation: Large enterprises must handle hundreds of ETL jobs daily, which is only possible through automation. Look for an advanced ETL automation solution with end-to-end automation capabilities, including job scheduling and process orchestration, to streamline the data management processes.
7. Monitoring and Logging: Comprehensive monitoring features are vital for keeping track of ETL job performance. Look for tools that offer detailed logging of job execution, error handling, and notifications. This allows you to identify and address issues promptly, ensuring data quality and reliability.
8. Scalability and Performance: An ETL tool should offer parallel processing capabilities to handle large data sets. Parallel processing splits tasks into smaller units and processes them simultaneously, significantly reducing processing times. Additionally, tools should have optimization features like query optimization and memory management to enhance performance.
9. Security and Compliance: Assess the tool’s security features, including data encryption, access controls, and compliance certifications (e.g., GDPR, HIPAA). Ensure that it aligns with your organization’s data security and compliance requirements. The ETL tool should also support effective data governance practices to ensure that data security measures align with organizational data policies.
10. Integration with Existing Systems: Ensure that the selected ETL tool integrates smoothly with your existing systems, such as data warehouses, BI tools, and analytics platforms. Compatibility with your technology stack is essential for a seamless data ecosystem. Many enterprises opt for a solution that provides a unified, end-to-end data ecosystem to avoid working with multiple vendors.

How to Choose the Right ETL Tool

Selecting the right ETL tool is a crucial decision that can significantly impact a company’s data integration and analytics capabilities. Here’s how to choose the most suitable ETL tool:

Define Your Requirements

Begin by clearly defining your organization’s ETL requirements. Which data sources do you need to integrate? What types of transformations are necessary? What volume of data are you dealing with, and what are the expected growth rates? Consider your organization’s long-term goals and scalability requirements.

Evaluate Data Sources and Formats

Take stock of your data sources. Ensure that the ETL tool you choose can connect to and ingest data from these sources, whether databases, cloud services, web services, or flat files. Verify that the tool supports the data formats commonly used in your organization, such as CSV, JSON, XML, or proprietary formats.

Assess Data Transformation Capabilities

Consider the complexity of data transformations required for your business processes. Look for ETL tools with the necessary transformation functions and capabilities to meet your needs. Evaluate the tool’s support for handling data quality, data cleansing, and error handling to ensure the reliability of transformed data.

Compare Available Tools

When selecting an ETL tool, it’s essential to compare different options’ features, scalability, ease of use, and integration capabilities. Some tools excel in cloud environments, while others offer robust on-premise solutions. A thorough comparison helps ensure the chosen tool aligns with your data volume, transformation needs, and long-term strategy.

Evaluate Cost/ROI

Cost considerations go beyond licensing fees to include infrastructure, maintenance, and training expenses. Assessing ROI involves analyzing how efficiently the tool automates data workflows, reduces errors, and improves decision-making. A high ROI ETL tool should minimize manual intervention while ensuring scalability and long-term value.

Testing/POC

A Proof of Concept (POC) allows you to validate an ETL tool’s capabilities in a controlled environment before full deployment. Testing should cover data ingestion, transformation speed, error handling, and compatibility with your data sources. This phase helps mitigate risks, ensuring the tool meets performance and compliance requirements before making a final decision.

Automate ETL Processes with Astera

Astera Data Pipeline Builder is an enterprise-grade, AI-driven solution with a powerful ETL/ELT engine. Our zero-code platform has a vast library of connectors, transformations, and built-in functionalities, making it easier for users to extract, manipulate, and load data to the destination of choice without writing any code. Our tool’s automation and orchestration capabilities can save up to 80% time required to manage ETL processes.

Interested in trying Astera and exploring how it meets your unique ETL requirements? Access a free 14-day trial version and experience it firsthand!