Top 7 AWS ETL Tools Ranked by Popularity in 2025

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:

Top 7 AWS ETL Tools to Choose from in 2025

Choosing the right AWS ETL tool is imperative to ensure you have the most suitable tool for your use case.

Here are the top 7 AWS ETL tools in the market 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.

Key Features

• Automatic schema detection: Map schema changes, like added or renamed columns, without manual input.
• Real-time sync with S3 and RDS: Keep data updated across AWS sources and destinations with minimal lag.
• Built-in error handling: Ensure automatic retries of failed loads and flag data issues in your pipeline.
• 150+ prebuilt connectors: Move data from SaaS apps, databases, and AWS sources without building custom scripts.
• Visual pipeline monitoring: Track data flow, status, and throughput using real-time dashboards and alerts.

Pros

• No-code setup for fast ETL processes, even with non-engineers.
• Supports key AWS services like Redshift, RDS, and S3.
• Strong customer support and clear documentation.
• Transparent pricing plans based on event volume.
• Real-time data loading with minimal latency.

Cons

• Fine-tuning data sync frequency is not always available.
• Editing existing pipelines is limited.
• Limited support for less common data sources.

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.

Key Features

• Automatic ETL code generation: Specify the data source and destination for AWS Glue to create the code for the entire ETL pipeline.
• Automatic schema recognition: Automatically recognize the schema of the data without manual intervention using crawlers.
• Data cleaning and deduplication: Find duplicates of data using a machine learning algorithm and clean them.
• Streaming support: Use Glue with streaming data sources, such as Apache Kafka or Amazon Kinesis.

Pros

• Pay only when jobs run and no idle infrastructure costs.
• Deep integration with AWS stack (S3, Redshift, Athena, etc.).
• Automate metadata management to reduce manual efforts.

Cons

• Cold start latency for jobs that run infrequently.
• Limited support for non‑AWS or hybrid environments.
• Requires Spark, Python, and Scala skills to tune data tasks.

Learn More: Top AWS Glue Alternatives

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.

Key Features

• Scheduled workflow orchestration: Define when the pipeline tasks must run and in what order for more data transformation control.
• Automatic data transformation: Automate data movement and transformation across Amazon S3, RDS, DynamoDB, and more.
• Support for heterogeneous tasks: Execute heterogenous activities, such as SQL, shell commands, EMR jobs, etc.

Pros

• Reliable fault‑tolerance is built into pipeline orchestration.
• Low-cost and pay‑for‑use model for pipeline data operations.
• Define conditional preconditions, branching, and retries with flexibility.

Cons

• Limited connections for SaaS products.
• Not suited for real-time or streaming workloads.
• Debugging UI is not intuitive and takes time.

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. 

Key Features

• Automated schema migration: Track and apply changes in source data schemas automatically to the destination.
• Lightweight, SaaS-based ingestion: Fast pipeline setup with minimal overhead for efficient ETL processes with AWS.
• Integrations for popular tools: Integrates natively with diverse SaaS, DB, and AWS products without external connectors.

Pros

• Fast to deploy with minimal configuration.
• Stable and reliable for standard data ingestion processes.
• Predictable and transparent subscriptions, ideal for cost-conscious brands.

Cons

• Does not support complex, custom data ingestion.
• Reliability issues with some connectors.
• Limited transformation capabilities.

Learn More: Top Stitch Alternatives and Competitors in 2025

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. 

Key Features

• Efficient metadata management: Store source, transformation, and destination data on an enterprise-grade metadata repository.
• Data quality checks: Identify data quality issues and address them within the pipeline to ensure accurate and reliable analytics.
• Visual workflows: Build ETL logic visually with drag-and-drop components that can be used even by non-tech team members.

Pros

• Robust and reliable data quality options to ensure accurate analysis.
• A rich ecosystem with a mature platform and reusable components.
• Excellent AWS and non-AWS integrations for businesses.

Cons

• Expensive platform compared to others.
• Basic plans lack advanced features.
• Complex setup and configuration.

Learn More: Top Talend Alternatives in 2025

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.

Key Features

• Real-time ingestion and processing: Stream high-volume data with minimal latency for real-time ETL and analytics workflows.
• Multiple subservices: Choose the right Kinesis tool depending on your use case, like raw streams or auto-delivery
• Seamless AWS integration: Connect easily with Lambda, S3, Redshift, and other AWS services to build scalable pipelines.

Pros

• Excellent for event-driven ETL and real-time analytics.
• Support SQL streaming via Kinesis Data Analytics.
• Fully managed and automatically scalable tool.

Cons

• Does not support batch or large‑scale transform logic.
• Requires careful partitioning and throughput planning.
• The tool can get costly with high throughput.

Learn More: AWS Kinesis Lambda Functions: Easy Steps & Best Practices 101

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.

Key Features

• Visual designer with a drag‑and‑drop UI: Build complex ETL workflows without writing code using a visual interface.
• Broad connector library: Hundreds of prebuilt connectors and transformation tools to integrate data from diverse sources.
• Data governance and lineage tracking: Track data flow end-to-end with built-in metadata, version control, and audit logs.

Pros

• Highly suitable for governance, compliance, and large-scale usage.
• Quick and efficient data management with minimal complexity.
• Responsive, timely, and knowledgeable customer support.

Cons

• Higher cost, especially for enterprises.
• Complex deployment and maintenance.
• Steeper learning curve for advanced features.

Learn More: Key Differences Between Hevo Data and Informatica Cloud Data Integration 

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.

What Are the Use Cases of AWS ETL Tools?

AWS ETL tools go beyond basic data movement. They help teams automate, prepare, and manage data across different AWS services with minimal setup.

Here are the most common use cases of AWS ETL tools.

1. Build Event-driven ETL Pipelines

Many AWS ETL tools support triggers that launch data processing jobs when new data arrives.

For example, when new files are added to Amazon S3, the ETL process can begin immediately. This means that while you will load new data in your Amazon S3 account, the ETL process will start working in the background.

As a result, you can save your team’s effort and time, which can be used for more strategic tasks.

2. Create a Unified Catalog

AWS-native ETL tools offer cataloging features that help teams organize, search, and query datasets without moving data.

The cataloging features offer you several benefits:

1. Facilitate data lineage in ETL to improve auditability.
2. Integrate with services like Athena and Redshift.
3. Enable searchable, query-ready data access.

As a result, you can store and manage metadata in one place and ensure easier data governance and consistency.

3. Create and Monitor ETL Jobs Without Coding

Many modern ETL tools on AWS provide visual interfaces for building and monitoring data pipelines. They also come with built-in dashboards for tracking job progress and performance in real-time.

They help you eliminate the need to create custom codes by bringing:

• Design pipelines using drag-and-drop tools.
• Auto-generate backend ETL logic.

This means you can identify process lapses faster and address them to ensure the jobs stay on course.

4. Explore Data with Self-Service Visual Data Preparation

AWS ETL tools with built-in visual data preparation let you clean and transform data without writing code. They also let you connect to common AWS sources such as S3, Redshift, RDS, and Aurora.

After connecting, users can apply over 250+ built-in transformations to filter out anomalies, invalid values, and more.

And it offers the following benefits:

• Non-tech users can explore and prep data without SQL or Python.
• Make data ready for analytics or reports without manual rework.
• Format columns, remove nulls, or detect outliers without dev involvement.

5. Build Materialized Views to Combine and Replicate Data

Some AWS ETL tools, like AWS Glue Elastic Views, allow you to create materialized views using SQL. These views pull and combine data from multiple sources and keep the result updated in near real time.

For example, you can merge data from DynamoDB and S3, then deliver a consistent, query-ready dataset to downstream systems.

This offers you the following benefits:

• Combine records from multiple AWS services into a single, structured view without complex joins or batch jobs.
• Up-to-date outputs without manual effort, since materialized views auto-refresh as the source data changes.
• Handle changes in source structure over time with its flexible ETL data modeling and built-in support for schema evolution.

Learn More: 10 Best AWS Migration Tools to Consider in 2025

What Is the Importance of AWS ETL Tools?

ETL tools are essential for managing data at scale in AWS. They help move, transform, and organize data efficiently. Without them, teams face delays, errors, and complex manual workflows across cloud services.

Here are 7 ways AWS ETL tools are important for your business:

1. Speeds up large-scale data loading: Loading large-scale data manually takes a lot of time and makes real-time analysis nearly impossible. AWS ETL tools automate this process and enable faster insights and quicker decisions.
2. Reduce training and labor costs: Manual data migration often requires extensive training, leading to higher time and cost for every project. AWS ETL tools lower the cost and time by providing user-friendly interfaces and built-in automation.
3. Ensure data consistency: AWS ETL tools ensure consistency through standardized workflows, automated checks, and schema validation. This helps make your data accurate and reliable.
4. Handle big data at scale: Whether you are processing logs, user activity, or transactional records, AWS ETL tools offer the scalability and performance needed for enterprise-level data operations.
5. Support broad data integration: AWS ETL tools can connect easily with databases, cloud apps, and storage systems. This flexibility makes it easier to centralize and analyze your data in one place.
6. Enable powerful data transformation: AWS ETL tools offer a wide range of built-in data transformations for cleaning, filtering, enriching, or reshaping data without starting from scratch, with support for custom scripting in SQL, Python, or Scala.

Learn More: Exploring AWS Integration: Tools and Services for Seamless Connectivity

How to choose the right AWS ETL tool?

Businesses often find it hard to choose the right AWS ETL tool for diverse reasons, like a lack of transformation features, poor customer support, pricing, and even the number of connectors.

However, the choice needs to be made based on three key factors that affect your business:

1. Check the data volume and complexity

The size and complexity of your data are important factors in choosing the ETL tool.

• If you are handling large and complex data, AWS Glue is the right choice due to its flexibility and scalability.
• If you handle simple and less volume data, AWS Lambda or Hevo Data are the best options.

2. Consider real-time vs batch processing needs

How you want to process your data, such as in real-time or in batches, also affects the choice of the AWS ETL tool, as different tools offer different options.

• If you want to process data that enters the system, you can utilize tools like Hevo for real-time processing.
• AWS Glue or Talend are the best options if you prefer to have a classic batch processing approach.

3. Check the cost and scalability features

Cost and scalability must also be considered when picking the most suitable AWS ETL tool for your business.

• If you want to ensure scalability and high performance with large workloads, options like AWS Glue or Talend are great.
• If you need performance and real-time data processing with predictable pricing, Hevo Data is the best choice.

Learn More: ETL Without the Headache: Why No-Code and Low-Code Tools Are the Future?

Ensure Reliable and Real-time AWS ETL Process with Hevo Data

The AWS ecosystem offers a wide range of ETL tools, each with unique strengths, pricing models, and ideal use cases. However, the right choice depends on your data volume, processing needs, and scalability goals. 

When evaluating tools, consider three essentials:

1. How fast can they process your data?
2. How well do they integrate with AWS services?
3. How cost-effective are they for long-term workloads?

By aligning these factors with your business priorities, you can find the right tool that meets your specific needs.

This is where Hevo Data becomes the best AWS ETL tool for your business by offering:

• No-code UI for non-data professionals and users to set up and manage their own data pipelines.
• Rapid access to SaaS apps for extracting data from sources, like Salesforce, Shopify, and ad platforms.
• A built-in mechanism for automatically handling schema changes, data load retries, and checkpointing.
• CDC to efficiently sync high-volume databases in near real-time for faster decision-making.

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