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Top 7 AWS ETL Tools in 2024

Zoha Shakoor

Content Strategist

March 18th, 2024

Amazon Web Services (AWS) ETL refers to a cloud-based set of tools and services that help extract data from different sources, make it usable, and store it in a way that makes it easy to analyze and make decisions based on it. AWS ETL tools offer a unique advantage for businesses seeking to streamline their data processes. These tools are efficient, scalable, and adaptable, making them ideal for a wide range of industries, from healthcare and finance to retail and beyond.  

According to a recent report by Gartner, more than half of businesses plan to move towards industry cloud platforms by 2028. This shift reflects a new approach to utilizing cloud technologies as a strategic asset, and a utility for innovation and competitive differentiation.  AWS is helping businesses prepare for the future by leveraging cloud technology. It is at the forefront of this transformation, particularly in optimizing data workflows through AWS ETL tools.  

In this article, we’ll discuss all about AWS ETL tools, exploring the best options available in 2024 to help you make an informed decision.  

What are AWS ETL Tools?  

AWS offers a range of ETL tools to help businesses integrate and manage their data. These tools make it easy for businesses to collect, process, and analyze data, turning it into useful insights.  

There are four key AWS ETL tools with unique benefits that can help businesses improve their data management. 

1. AWS Data Pipeline  

AWS Data Pipeline is a web service that helps make it easier to move and transform data across different AWS storage, compute services, and on-premises sources. It has a simple interface that lets the user drag and drop data sources and destinations onto a visual canvas. With AWS Data Pipeline, a user can create ETL data pipelines by defining specific connection attributes. 

AWS Data Pipeline offers an automation feature, which lets the user schedule the data workflows to run automatically. This means that the data is always up-to-date and ready for analysis. This level of automation streamlines operations and makes data processing more efficient. 

The tool is fault-tolerant, meaning it’s built to handle failures and it automatically retries tasks that encounter errors. AWS Data Pipeline also provides customizable notifications to keep the user informed of their data workflows’ status. 

Finally, AWS Data Pipeline can manage resources automatically and provide pre-built templates for complex data handling scenarios, freeing businesses from the technical complexities of data processing. 

2. AWS Glue 

This serverless ETL platform is designed to automate the data preparation work, making it a hassle-free process. By pointing AWS Glue to the data stored within AWS, it automatically identifies data, infers its structure, and catalogs the metadata in the AWS Glue Data Catalog. The automatic discovery and organization of data make it instantly ready for ETL processes, and accessible for querying, making sure that analytics pipelines are insight driven. 

AWS Glue has a user-friendly GUI that allows users to create data transformation pipelines with minimal disruption, even if they don’t have deep coding knowledge. As users visually assemble the ETL job, AWS Glue generates the necessary PySpark code behind the scenes, making it easy to use. 

The Glue Data Catalog and automated schema discovery streamline the ETL process by organizing data metadata centrally, making it easy to find, access, and transform. 

3. AWS Lambda 

AWS Lambda is a serverless computing service offered by Amazon Web Services (AWS) that executes code in response to various events, automatically handling the underlying computing resources. It enables users to extend AWS services with custom logic or develop backend services without server management. 

Lambda supports multiple programming languages, including Python, Node.js, Java, Ruby, Go, and Power Shell, providing flexibility based on users’ preferences. 

As an ETL tool, AWS Lambda can process and transform data seamlessly. By leveraging Lambda functions, users can design ETL pipelines to extract data from different sources, transform it according to specific requirements, and load it into target destinations. 

Lambda responds to various events, such as HTTP requests via Amazon API Gateway, object modifications in Amazon S3 buckets, and table updates in Amazon DynamoDB, triggering the execution of user-defined code to perform desired actions.  

4. AWS Glue DataBrew  

AWS Glue DataBrew is a tool that simplifies and speeds up the data preparation process for analysis and machine learning. It is like having a skilled assistant who takes care of all the data preparation tasks. 

With DataBrew, users can easily pull in data from different sources and clean it up, fix errors, and transform it into a more useful format using an intuitive interface. This tool saves time and makes data preparation accessible to everyone, regardless of coding skills.  

After refining the data, DataBrew can automate these steps. This means that when similar data is received next time, DataBrew will remember the process and repeat it automatically, without any user intervention. It is a tool for businesses that want to use their data for making decisions or building machine learning models but don’t want to get slowed down in the technicalities of getting the data ready. 

AWS Data Pipeline Vs AWS Glue

AWS Glue is a modern, serverless service that focuses on automation and integration within the AWS ecosystem. It offers an easy and efficient way to process and transform data. On the other hand, AWS Data Pipeline is a more traditional workflow service that automates the movement and transformation of data. 

Feature   AWS Data Pipeline   AWS Glue  
User Interface  Drag-and-drop; Web-based management console; CLI  Visual and code-based GUI 
Data Processing  Batch  Batch and streaming 
Scaling  Manually scalable to handle large datasets  Automatically scales on demand 
Execution Environment  Can be used on AWS cloud or on-premises  Serverless; fully managed by AWS 
Integration with AWS Services  Integrates with AWS storage and database services  Extensive integration with AWS services, including direct support for data stores and analytics services 
Orchestration  Supports scheduling, dependency tracking, and error handling  Advanced job management, including triggers, and workflow orchestration with AWS Glue DataBrew 
Schema Management  No native schema discovery or management  Automated schema discovery, cataloging, and management with AWS Glue Data Catalog 
Transformation Capabilities  Custom scripts for data transformation  Supports both built-in transformations and custom scripts.  
Pricing  Charged based on the frequency of activities and where they run  Hourly rate for ETL jobs and crawlers, with a minimum charge; additional charges for Data Catalog and DataBrew usage 

Top 8 Tools to ETL Data in AWS 

Choosing an ETL Tool for AWS that meets your business requirements can be a difficult task, especially if we consider the range of AWS ETL tools available in the market. To simplify this process, we have compiled a list of the best AWS ETL Tools. 

1. Astera  

Astera is a comprehensive data integration platform that simplifies and accelerates your data processes with a focus on automation and user-friendliness. It empowers both business and technical users to manage complex data workflows without extensive IT dependency 

  • Seamlessly integrate with AWS, enhancing your data quality and ETL processes using Astera’s cloud capabilities. 
  • Ensure the integrity and cleanliness of your data before storing it in AWS services with advanced data quality management features. 
  • Efficiently transform data using pre-built transformations and functions, eliminating the need for extensive coding expertise. 
  • Create and manage ETL pipelines on AWS effortlessly with an intuitive drag-and-drop interface. 
  • Extract valuable information from unstructured data sources directly into AWS with AI-powered data extraction capabilities. 
  • Easily ingest data from various sources and formats into AWS using built-in connectors. 
  • Optimize your data processing tasks on AWS by automating and orchestrating data workflows, ensuring timely data availability. 
  • Enhance data mapping from source to destination with an AI-powered auto mapper, crucial for maintaining data quality and consistency in AWS environments.  

2. AWS Glue  

AWS Glue is a fully managed ETL service that automates the preparation of your data for analytics, offering a seamless way to organize, cleanse, and ultimately transform your data.  

Pros 

  • AWS Glue’s serverless architecture eliminates the need to manage infrastructure, allowing user to focus on analyzing the data. 
  • It offers cost-effectiveness, billing based on the resources utilized.  
  • AWS Glue simplifies ETL job creation through a user-friendly interface and automatic code generation.  

Cons

  • The service offers limited control over compute resources, which might not align with all project requirements or specific compute needs. 
  • AWS Glue supports only Python and Scala for scripting, potentially complicating the integration of existing ETL scripts written in other languages. 

3. AWS Data Pipeline

AWS Data Pipeline is a web service for data processing and transfer among AWS, on-premises, and storage sources.  

Pros

  • The AWS Data Pipeline offers support for a range of AWS databases and provides an intuitive control interface  
  • It creates resources and clusters only when necessary.  
  • Data is protected both at rest and in motion through AWS’s access control mechanisms.  

Cons

  • Managing multiple installations and configurations on compute resources within data pipelines can be challenging  
  • Beginners may find the representation of preconditions and branching logic complex.  
  • For users requiring data from external services, AWS Data Pipeline may not be the most suitable choice.  

4. Stitch (acquired by Talend) 

Stitch simplifies data replication from various sources to your data warehouse, enabling efficient and secure data analytics. 

Pros

  • The platform’s setup and user-friendly interface makes your data pipeline creation quick. 
  • With support for over multiple data sources, you can integrate a diverse range of data into your analysis workflows. 
  • Stitch’s adherence to SOC 2, HIPAA, and GDPR ensures your data processes meet security and privacy standards. 

Cons

  • The platform’s limited customization may restrict the ability to tailor data pipelines to complex or specific business needs. 
  • Its transformation capabilities are limited, potentially necessitating additional tools for complex data manipulation. 
  • Stitch provides limited monitoring options, posing challenges for businesses that need real-time monitoring and alerts. 

5. Integrate.io 

Integrate.io is a cloud-based platform that simplifies data integration from multiple sources. It streamlines the process of aggregating and transforming data for your analytics needs. 

Pros

  • It enhances data transformation processes, facilitating quick and efficient workflows for handling data. 
  • The tool simplifies integrations, particularly with Salesforce environments, improving the data consolidation tasks.  
  • Its security features, including encryption and SOC 2 compliance, safeguards the data throughout the ETL process. 

Cons 

  • The platform’s basic error logging can make troubleshooting complex issues more difficult.  
  • The interface might present challenges as you navigate more sophisticated data pipeline configurations. 
  • A few aspects of platform features are not fully integrated. 

6. Talend (acquired by Qlik) 

Talend offers a solution for your ETL needs, facilitating data extraction, transformation, and loading with its open-source platform.  

Pros

  • It offers cost savings and customization possibilities with its open-source nature, making it an economical choice for the data integration tasks. 
  • The connectors allow easy linking to various data sources and applications, improving integration capabilities. 
  • It offers drag-and-drop functionality that simplifies ETL process creation and management. 

Cons

  • Talend presents a steep learning curve for newcomers to ETL or data integration. 
  • While Talend boasts a large user community, its support options are more limited than traditional ETL tools. 
  • Talend can face performance challenges with very large data sets or complex transformations.  

7. Informatica 

Informatica streamlines data integration and governance across various source systems, enhancing data warehousing and business intelligence efforts. 

Pros 

  • Its graphical interface facilitates the ETL process design, session monitoring, and overall task management with ease. 
  • The platform can manage multiple data types, including queued messages and unstructured data, ensuring complete data integration. 
  • With Informatica’s centralized web-based platform, organizations can achieve data governance and reductions in administrative costs 

Cons

  • The Workflow Monitor lacks a sorting feature, making it difficult to distinguish between essential and non-essential folders. 
  • The Repository Manager does not support moving items between folders or importing XML export files. 
  • Developing mappings and workflows is not possible with the current toolset.  

How to Select the Right AWS ETL Tool 

When it comes to AWS ETL tools, there are many options out there, all claiming to be the best for the data processing needs. It can be overwhelming to choose the right one. To help make an informed decision, let’s explore the most important factors to consider 

Data Sources It Supports 

The tool should offer broad support for various data sources. This includes traditional databases like MySQL or PostgreSQL, cloud services, and even streaming data platforms. 

For example, a company using a mix of on-premises databases and cloud-based services will benefit from an ETL tool that can seamlessly integrate data from all these sources. 

Data Replication Features 

The ability to replicate data efficiently is essential. The tool should provide real-time data replication capabilities, ensuring that data is continuously synchronized between source and destination.  

This is important for businesses that rely on up-to-date data for real-time analytics and decision-making. For instance, an e-commerce platform may need to constantly replicate data to analyze customer behavior and inventory levels in real-time. 

Zero Coding 

Additionally, for businesses aiming to minimize coding effort or eliminate it entirely, no-code AWS ETL tools are a valuable option. These tools provide a user-friendly interface where users can create, manage, and monitor ETL jobs without writing a single line of code.  

Drag-and-drop interfaces and pre-built connectors for common data sources and destinations make these tools accessible to users regardless of their technical expertise. An example would be a marketing team without programming skills needing to integrate customer data from various platforms for analysis; a no-code ETL tool would enable them to set up and manage these integrations effortlessly. 

Customer Support Service 

Good customer support is vital. The ETL tool should come with comprehensive support options, including 24/7 live support, extensive documentation, and community forums where users can exchange tips and solutions.  

This is particularly important for businesses that may encounter complex integration scenarios or need guidance on optimizing their ETL processes. A company might prefer a tool with a reputation for responsive and helpful support to quickly resolve any issues that arise. 

Pricing 

The cost of the ETL tool is a significant consideration. Pricing structures vary widely, from subscription-based models to pay-as-you-go plans based on the volume of data processed. Businesses should look for transparent pricing that aligns with their data usage patterns and budget constraints.  

For small startups or companies with fluctuating data processing needs, a tool offering scalable pricing or a free tier for lower volumes of data could be most beneficial.

Factors to consider while selecting the AWS ETL tool

Concluding Thoughts  

Picking the right AWS ETL tool is key for companies looking to use their data efficiently. These tools not only streamline data processes but also empower organizations to make informed decisions based on accurate insights. As you explore the best options for your data management needs, consider how features like automation, scalability, and data quality align with your goals.  

Astera stands out as the leading AWS ETL tool, offering unparalleled ease of use, automation capabilities, and comprehensive data quality features. Experience Astera’s transformative power for yourself with a free 14-day trial or schedule a demo to see how it can streamline your data processes and drive your business forward. 

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