Companies of all sizes and industries have access to an ever-increasing amount of data that is too large for anyone. If you cannot effectively process and analyze all this information, and reveal valuable information based on the data hidden in the noise, then all this information is actually useless. The ETL process extracts information from sources such as Databases, converts files or tables according to Data Warehouse standards, and loads them into the data warehouse.

Are you confused about which tool to use for ETL from your AWS account? Don’t worry this blog will discuss the Amazon ETL process in detail and also tell you about some of the best AWS ETL tools that make the entire process easy and efficient.

What is AWS ETL?

AWS provides a Glue for performing extract, transform, and load (ETL). The product consists of a serverless platform and necessary tools for carrying out data integration. It also helps to modify your data based on your use case.

AWS Glue provides the flexibility to build pipelines either through custom coding or their intuitive UI. You can store data flows and resulting datasets using their data catalog. Administrators can leverage Glue Studio to run and monitor ETL data flows.

Glue Studio also provides a visual job editor and a data flow-style UI. It enables high-level graphical definitions of flows, but the supported transformations are limited. You will need to write code or use SQL for complex modifications like filters, and joins. However, the limitation is that you can use the connectors only for data sources and destinations hosted on AWS. 

You can prepare your data using DataBrew, a very similar but different product from AWS Glue. It offers a bigger library of modifications than Glue. Again, DataBrew connectors are limited, but they can also support conventional databases like Oracle or MySQL that operate on AWS.

To benefit from the combination of both, you should first carry out extraction and loading using Glue must perform to a destination like Redshift. After that,  you can transform your data using DataBrew.

Let’s sum up the limitations of AWS ETL now.

  • The sources supported are only AWS-owned sources, databases operating on AWS, and files from S3 buckets. 
  • Connecting to on-premises data sources is not secure.
  • The tool is not effective when you need to carry out complex transformations that include logic and job execution separated between the two tools. 
  • As both are separate tools, inconsistencies in security policies and vulnerabilities between them could arise.
  • The available features for data governance are limited.

Now, let’s dive deep into the top AWS ETL tools.

Top 8 AWS ETL Tools

Choosing an ETL Tool that your business needs can be daunting, especially when many AWS ETL Tools are available on the market. To make your search easier, here is a complete list of the 5 best AWS ETL Tools for you to choose from AWS marketplace ETL tools and easily start setting up your ETL pipeline:

1. Hevo Data

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Hevo allows you to replicate data in near real-time from 150+ sources to the destination of your choice including Snowflake, BigQuery, Redshift, Databricks, and Firebolt. Hevo is the only real-time ELT No-code Data Pipeline platform that cost-effectively automates data pipelines that are flexible to your needs. For the rare times things do go wrong, Hevo ensures zero data loss.

To find the root cause of an issue, Hevo also lets you monitor your workflow so that you can address the issue before it derails the entire workflow. Add 24*7 customer support to the list, and you get a reliable tool that puts you at the wheel with greater visibility. Check Hevo’s in-depth documentation to learn more. Hevo offers a simple and transparent pricing model.


Hevo was the most mature Extract and Load solution available, along with Fivetran and Stitch but it had better customer service and attractive pricing. Switching to a Modern Data Stack with Hevo as our go-to pipeline solution has allowed us to boost team collaboration and improve data reliability, and with that, the trust of our stakeholders on the data we serve.

– Juan Ramos, Analytics Engineer, Ebury

Check out how Hevo empowered Ebury to build reliable data products here.

Sign up here for a 14-Day Free Trial!

2. AWS Glue

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AWS Glue is one of the most popular ETL Tools in AWS in the current market. It is a completely managed ETL platform that simplifies the process of preparing your data for analysis.

It is very easy to use, all you have to do is create and run an ETL job with just a few clicks in the AWS Management Console. You just have to configure AWS Glue to point to your data stored in AWS.

It automatically discovers your data and stores the related metadata in the AWS Glue Data Catalog. Once this is done, your data can be searched and queried immediately and is also available for ETL.

To learn more about AWS Glue, visit here.

Use Case

AWS Glue is to be used when your use case is primarily ETL and when you want your jobs to run on a serverless Apache Spark-based platform. It can also be used when your data is semi-structured or has an evolving schema. 


You pay hourly when you use AWS Glue, billed by the second, for crawlers and ETL jobs. For this Amazon ETL Tool, you only have to pay a simple monthly fee, according to the AWS Glue Data Catalog. Also, the first million objects and accesses are stored for free.

3. AWS Data Pipeline

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AWS Data Pipeline is among the most reliable AWS ETL Tools. It helps to move data between different AWS compute and storage services and on-premises data sources at specified intervals.

It allows you to access your data where it’s stored regularly, transform and process it as needed, and efficiently transfer the resultant data to AWS services such as Amazon RDS, Amazon S3, Amazon DynamoDB, and Amazon EMR.

AWS Data Pipeline is fault-tolerant, repeatable, and highly available and lets you easily develop complex data processing workloads. AWS Data Pipeline eliminates the need to take care of resource availability, management of inter-task dependencies, resolve transient failures or timeouts in individual tasks, or even develop a failure notification system.

This Amazon ETL Tool helps you claim and process data that was previously locked up in on-premises data silos.

To learn more about AWS Pipeline, visit here.

Use Case

Using AWS Data Pipeline is best suited for daily data operations as it does not require maintenance effort. It also allows you to run and monitor your processing activities on a highly reliable, fault-tolerant infrastructure. It is suitable for copying data between Amazon Amazon S3 and Amazon RDS or running a query against Amazon S3 log data.


As part of AWS’s Free Usage Tier, you can get started with AWS Data Pipeline for free. For further pricing information, check out this page.

Download the Guide to Evaluate ETL Tools
Download the Guide to Evaluate ETL Tools
Download the Guide to Evaluate ETL Tools
Learn the 10 key parameters while selecting the right ETL tool for your use case.

4. Stitch Data

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Stitch provides support for Amazon Redshift and S3 destinations. It also integrates with over 90 data sources. It maintains SOC 2, HIPAA, and GDPR compliance while providing businesses the power to replicate data easily and cost-effectively. Stitch, being a Cloud-first, extensible platform, lets you scale your ecosystem reliably and integrate with new data sources 

To learn more about Stitch Data, visit here.

Use Case

Stitch Data is recommended when you want better insight into data analytics. It lets you move your AWS data to your data warehouse within minutes. It does not require API maintenance, scripting, cron jobs, or JSON and is very easy to use. It can be used by someone without a technical background. It allows quick connections to first-party data sources, including MySQL, MongoDB, Salesforce, and Zendesk.


You can see the entire pricing plan of this Amazon ETL Tool here.

5. Talend

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AWS integration strategies may be daunting for many, but it doesn’t have to be. It is important to choose the right integration tool, to simplify the entire process and carry it out quickly and reliably.

Talend Cloud Integration Platform supports different types of integrations, including on-premise, cloud, and hybrid, with AWS. You will find graphical tools, an integration template, and more than 900 components at your disposal to ensure your integration is successful.

To learn more about Talend, visit here.

Use Case

Talend is suitable for data preparation, data quality, data integration, application integration, data management, and big data among other things. Talend provides separate products for each solution.


Talend provides a free trial. You can view the entire pricing plan here.

6. AWS Kinesis

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Amazon Kinesis Data Streams is one of the important AWS Redshift Amazon ETL tools. It enables you to analyze huge amounts of data in real time. You have to add data to your Redshift cluster using AWS Kinesis. After reading a stream of events from Kinesis, it applies processing or transformations to these data. Then, in Amazon SCTS,  it writes the results into a destination table. This makes Amazon Kinesis one of the popular AWS data transformation tools.

Use Cases

You can create real-time data solutions and feed live data into your data warehouse using Kinesis Streams. It can be used in IOT sensors, financial markets, and websites for data analysis. 


AWS Kinesis offers short-term free trials and further pricing can be checked here.

Other AWS ETL Tools are available such as:

  • Amazon EMR (Elastic MapReduce)
  • AWS Step Functions
  • Amazon Redshift
  • Amazon Athena
  • AWS DMS (Database Migration Service)
  • Amazon Managed Workflows for Apache Airflow (MWAA)
  • AWS Glue DataBrew

7.  Informatica    

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Regarded as one of the best ETL tools for AWS, Informatica offers built-in features that allow it to easily connect with various source systems. It offers a versatile graphical user interface that can design ETL processes, debug, and monitor sessions.

Use Cases

It can be used in healthcare, pharma, finances, and fraud detection for real-time data analysis. It is also used in data warehousing, business intelligence, and data integration among business applications.


Informatica offers a free trial, and a detailed pricing plan is available here


Image Source is a cloud-based data integration platform offering ETL (Extract, Transform, Load) capabilities. It enables you to connect to various data sources, transform data through code or pre-built functions, and load it into target destinations. Its features such as extensive connectivity, visual interface, and security make it one of the most sought-after AWS ETL tools. 

Use Cases is a suitable ETL tool for use in finance, banking, and e-commerce for data warehousing, real-time data processing, and automation. 

Pricing offers a free trial and you can check out its detailed pricing plans here

Comparing the 5 Best ETL Tools

The following table provides a comparative summary of the 5 best ETL tools:

5 Best ETL tools comparison table

Use Cases of AWS ETL Tools

The following are the 5 major uses 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, Once you successfully catalog 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 a 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.

Before wrapping up, let’s look into the significance of the tools.

Significance of AWS ETL Tools

AWS ETL Tools refer to the ETL Tools offered by AWS. The following reasons signify the importance of AWS ETL Tools in data migration:

  • When you manually migrate your data, the chances of committing an error due to the dynamism of human nature increase. With the usage of AWS ETL Tools, you will 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 have 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.


In this blog post, you have learned about what ETL is and the top 5 best AWS ETL Tools out there. You have also seen the factors like use cases, pricing, Services etc., to consider when choosing among these AWS ETL Tools.

Nowadays, it is very common to use Amazon Redshift as the backbone Data Warehouse for highly reliable ETL or ELT systems.

You can also learn how to set up a robust Amazon Redshift ETL.

Visit our Website to Explore Hevo

Hevo is a No-code Data Pipeline. It supports prebuilt integration from 150+ data sources. You can easily migrate your data from any source in real-time.

Want to take Hevo for a spin? Sign Up for a 14-day free trial and experience the feature-rich Hevo suite first hand.

Share of experience with AWS ETL Tools in the comments section below!

Shruti Garg
Freelance Technical Content Writer, Hevo Data

With an analytical perspective and a problem-solving approach, Shruti has extensive experience in solving business problems of data teams by delivering thoroughly researched content pivotal for data practitioners.

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