Top 7 AWS ETL Tools Ranked by Popularity in 2025

Key Takeaways

Here are the 7 best AWS ETL tools to consider in 2025:

1. Hevo Data: Best for no-code real-time data pipelines with 150+ source integrations
2. AWS Glue: Best for serverless ETL jobs with automated data discovery and cataloging
3. AWS Data Pipeline: Best for scheduled data movement between AWS services with fault tolerance
4. Stitch Data: Best for simple cloud-first ETL with easy setup and compliance features
5. Talend: Best for enterprise data integration with 900+ components and hybrid deployments
6. AWS Kinesis: Best for real-time streaming data analysis and processing at scale
7. Informatica: Best for comprehensive ETL with versatile GUI and enterprise-grade features

As cloud adoption accelerates, Amazon Web Services(AWS) has become the go-to platform for modern data infrastructure. Within this ecosystem, ETL (Extract, Transform, Load) processes play a key role in moving and shaping data for analysis and reporting.

Choosing the right AWS ETL tool ensures your pipelines are scalable, cost-effective, and aligned with your data goals. The right tool can reduce engineering effort, streamline workflows, and unlock faster, more reliable insights.

But with so many AWS ETL tools and services at your disposal, narrowing down the best fit can feel overwhelming. This content piece simplifies your decision-making by comparing the top tools in AWS, ranked by popularity, usability, and features.

What is AWS ETL?

ETL on AWS involves using all of AWS’s comprehensive services to handle your data processing tasks efficiently. AWS provides a Glue for performing extract, transform, and load (ETL). The product consists of a serverless platform and the necessary tools for data integration. It also helps modify data based on your use case.

You can extract data from different sources, transform it using AWS Glue, and load it into Amazon Redshift or S3 for storage and analysis. You can also integrate with other services to help build robust and scalable ETL pipelines that can easily process massive amounts of data.

Here’s a quick overview of the tools we are going cover in detail:

	Try Hevo for Free
Reviews	4.5 (250+ reviews)	4.1 (100+ reviews)	4.8 (70+ reviews)	4.3 (100+ reviews)	4.4 (80+ reviews)
Pricing	Usage-based pricing	Pay-as-you-go	Row based pricing	Capacity-based pricing	consumption-based pricing
Free Plan
Free Trial	14-day free trial		14 days free trial		30 day free trial
Data Sources	150+ sources including AWS S3, RDS, Salesforce, etc.	All AWS services plus JDBC-connected databases	90+ built-in integrations	900+ connectors across databases and SaaS apps	Wide range of sources, including legacy systems
Replication / Streaming Mode	Near real-time	Batch-oriented (serverless)	Scheduled batch replication	Batch and real-time (product-dependent)	Batch and real-time
Customer Support	24×7 live chat & email	AWS forums; limited live support	Email (higher-tier plans)	Email or chat, per plan	Dedicated enterprise support
Pricing Model	Free tier + usage-based plans	Pay-per-use (job runtime, crawlers)	Free trial; tiered pricing	Free trial; enterprise pricing	Free trial; enterprise pricing
Adding New Data Source	No-code, instant connector setup	Manual script-based addition	Limited GUI + API connectors	Drag-and-drop for supported apps	Manual mapping; custom integrations

Top 7 AWS ETL Tools to Consider in 2025

1. Hevo Data

G2 review: 4.4/5 (259)

At Hevo, we have built a no-code, real-time ELT platform that helps you move data effortlessly from 150+ sources, including S3, RDS, Salesforce, and more, to your destination of choice, such as Redshift, BigQuery, Snowflake, Firebolt, and Databricks. We focus on automation, reliability, and zero-maintenance pipelines so your team can spend more time on analysis than firefighting.

Client Testimonial

What I like best about Hevo Data is its intuitive user interface, clear documentation, and responsive technical support. The platform is straightforward to navigate, even for users who are new to data migration tools. I found it easy to set up pipelines and manage data flows without needing extensive technical support. Additionally, Hevo provides well-organized documentation that clearly explains different migration approaches, which makes the entire process smooth and efficient.

Henry E.

Software Engineer

2. AWS Glue

G2 review: 4.3/5 (194)

AWS Glue is a fully managed ETL service that streamlines data discovery, preparation, and loading within the AWS ecosystem. It’s built for scalability and ease of use, letting you set up data pipelines with just a few clicks in the AWS Console. AWS Glue automatically discovers your data, catalogs metadata, and provisions the necessary infrastructure to run your jobs, all without requiring server setup or manual orchestration.

Client testimonial

Absolutly love Glue for its functionality. Provides everything needed.

Sourabh S.

Data Analyst

3. AWS Data Pipeline

G2 review: 4.1/5 (25)

AWS Data Pipeline is a dependable service designed to automate the movement and transformation of data across AWS services and on-premises systems. It enables you to define data-driven workflows that run on a schedule, helping you seamlessly process and migrate data between Amazon S3, RDS, DynamoDB, EMR, and more.

Client testimonial:

It has made possible to connect multiple data sources and workloads and analyze data especially AWS Redshift and AWS Glue service.

Katha V.

QA Engineer

4. Stitch Data

G2 review: 4.8/5

Stitch Data is a cloud-first, developer-friendly ETL platform that makes it effortless to connect a wide range of data sources with AWS destinations like Redshift and S3. What sets the platform apart from others is its simplicity and speed. You can set up pipelines in just a few clicks without needing to write code or manage complex APIs. 

Client testimonial 

Stitch integrates with most large companies such as Google Ads, Microsoft Ads, etc. One of the best things is that it sets up cost allocation in a very easy straightforward manner.

Megan S.

Digital Marketing Director

5. Talend

G2 review: 4.5 (63)

Talend is a comprehensive data integration platform that provides robust ETL capabilities and strong support for AWS environments. Designed for enterprise-grade data operations, it offers a wide set of tools for data preparation, cleansing, transformation, and orchestration. Whether you are dealing with hybrid cloud systems or fully cloud-native applications, Talend helps unify your data workflows through reusable components and visual interfaces. 

Client testimonial:

UI of Talend Open studio is straightforward to use and understand. Easily users can set up big queries and join the tables, which is amazingly helpful and a time-saver when using big data for operations.

Archana J.

Application Analyst

6. AWS Kinesis

G2 review: 4.7/5 (26)

AWS Kinesis is purpose-built to stream data in real-time. It’s ideal for scenarios where businesses need to ingest and analyze massive volumes of data as they’re generated, be it from IoT devices, app logs, or live user interactions. Integrated tightly into the AWS ecosystem, Kinesis empowers teams to build event-driven ETL workflows that can feed analytics systems like Amazon Redshift with minimal latency.

Client Testimonial:

Amazon Kinesis effortlessly scales to handle varying data loads, ensuring it can adapt to the evolving needs of your business. Enables real-time processing of streaming data, providing timely insights and analytics for informed decision-making. Seamlessly integrates with other AWS services, facilitating a comprehensive and cohesive cloud environment for various applications.

Ashu S.

Analyst

7. Informatica

G2 review: 4.3/5 (543)

Informatica is a long-standing leader in the data integration space, offering powerful ETL capabilities tailored for enterprise-grade environments. Known for its robust performance, Informatica comes with an intuitive visual interface and broad connector support, making it easy to pull data from any source and transform it for AWS destinations like Redshift and S3. 

Client Testimonial:

MDM is highly efficient even the data is different or incomplete .smart matching techniques are so useful by setting our own rules for the matching of records.

Naga Sandhya

M.

Use Cases of AWS ETL Tools

1) Build Event-driven ETL Pipelines

• AWS Glue enables you to prevent any processing delays. It allows you to start your ETL tasks as soon as any new data arrives.
• So, while you will load new data in your Amazon S3 account, the ETL process will start working in the background.

2) Create a Unified Catalog

• The AWS Glue provides a Data Catalog using which you can discover multiple AWS datasets quickly without even shifting any data. Furthermore, it also facilitates data lineage in ETL to improve auditability.
• Once you have successfully cataloged the data, you can access it for searching and querying using Amazon Athena, Redshift Spectrum, etc.

3) Create and Monitor ETL Jobs Without Coding

• You can seamlessly create and track ETL jobs using AWS Glue Studio.
• These ETL jobs use a drag-and-drop editor to perform data transformations, while AWS Glue automatically builds an ETL code for the same. Moreover, you can monitor the progress of the ETL jobs with the AWS Glue Studio Task Execution Dashboard.

4) Explore Data with Self-Service Visual Data Preparation

• Using the AWS Glue DataBrew, you can experiment with data by directly accessing it from Data Warehouses or Databases, such as Amazon S3, Amazon Redshift, Amazon Aurora, Amazon RDS, etc.
• It also allows you to choose from 250+ in-built transformations in AWS Glue DataBrew.
• This way, you can automate various data preparation processes such as anomaly filtering, building formats, and invalid value correction.
• Once prepared, the data can be used for analytics and machine learning purposes.

5) Build Materialized Views to Combine and Replicate Data

• You can create Views using SQL in AWS Glue Elastic Views. These views are beneficial if you wish to combine data stored in multiple data sources and update it regularly. AWS Glue Elastic Views currently supports Amazon DynamoDB as a primary data source, but can also integrate with other Amazon products. It supports flexible ETL data modeling, allowing schema evolution and transformation logic.

Significance of AWS ETL Tools

• When you manually migrate your data, the chances of committing an error increase due to human nature’s dynamism. However, using AWS ETL Tools will ensure that you migrate your data with zero data loss.
• Manually loading your data can be time-consuming, especially when you are dealing with petabytes of data and want to perform real-time analysis. AWS ETL Tools help load data in real time within minutes.
• The manual data migration process involves a high cost of training personnel to meet the basic standard requirements. AWS ETL Tools provide data migration at a low cost without any help from an expert.
• AWS ETL Tools ensure data consistency whereas manual methods may lead to inconsistency which can’t be avoided.
• AWS data integration tools are designed to handle large volumes of data. This makes them efficient for organizations with big data requirements.
• It is possible to integrate these tools with a wide range of data sources and destinations. Data integration across different environments becomes easier with this. 
• There are many ETL tools in AWS for data transformation. They contain pre-built transformations and you can also write custom scripts.

How to choose the right AWS ETL tool?

Data Volume and Complexity

The size and complexity of your data are important factors in choosing the ETL tool. In the case of large and complex data, it would be AWS Glue because of its flexibility and scalability. For simple and less volume data, it could use AWS Data Pipeline or even Lambda.

Real-Time vs. Batch Processing Needs

Your processing criteria will drive the choice between real-time and batch ETL tools. For instance, if you want to process data that enters the system, you can utilize tools like Hevo for real-time processing. AWS Glue and AWS Data Pipeline are more realistic for classic batch processing.

Cost and Scalability Considerations

AWS is also highly affordable for mainstream options. If cost has to be considered, then it depends on options like AWS Lambda; it is costed by the time of computation. Amazon EMR and AWS Glue deliver high-powered performances with large workloads and scalability, but the cost is high. For another cost-effective option, you can go for Hevo.

Conclusion

In this blog post, you have learned about ETL and the top 5 best AWS ETL Tools.

Looking for a more user-friendly ETL solution than AWS Glue? Hevo offers seamless real-time data integration. If your use case involves pushing data back into SaaS tools from your data warehouse, reverse ETL tools may complement your ETL setup.

Explore the best AWS Glue alternatives to optimize your ETL workflows. Check out the details of alternatives to AWS Glue.

Sign up for a 14-day free trial with Hevo and experience fuss-free ETL with AWS.

FAQ on AWS ETL Tools

Shruti Garg Technical Content Writer, Hevo Data

Shruti brings a wealth of experience to the data industry, specializing in solving critical business challenges for data teams. With a keen analytical perspective and a strong problem-solving approach, she delivers meticulously researched content that is indispensable for data practitioners. Her work is instrumental in driving innovation and operational efficiency within the data-driven landscape, making her a valuable asset in today's competitive market.