AWS Data Pipeline [EOL] vs AWS Glue comparison

AWS Data Pipeline [EOL] and AWS Glue are both solutions in the Cloud Data Integration category. Additionally, 100% of AWS Data Pipeline [EOL] users are willing to recommend the solution, compared to 90% of AWS Glue users who would recommend it.

AWS Data Pipeline [EOL]

Read 2 AWS Data Pipeline [EOL] reviews

100% willing to recommend

AWS Glue

Read 50 AWS Glue reviews

7,749 Views
5,424 Comparison Views

90% willing to recommend

AWS Data Pipeline [EOL]

AWS Glue

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Executive Summary

We performed a comparison between AWS Data Pipeline [EOL] and AWS Glue based on real PeerSpot user reviews.

Find out what your peers are saying about Amazon Web Services (AWS), Informatica, Salesforce and others in Cloud Data Integration.

To learn more, read our detailed Cloud Data Integration Report (Updated: May 2026).

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Cloud Data Integration

May 2026

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Review summaries and opinions

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Categories and Ranking

AWS Data Pipeline [EOL]

Average Rating

8.0

Number of Reviews

Ranking in other categories

No ranking in other categories

AWS Glue

Average Rating

7.8

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

Cloud Data Integration (1st)

Featured Reviews

BarathwajRamamoorthy

Senior Director Data Architecture at Managed Markets Insight & Technology, LLC

A tool with great orchestration and development capabilities but needs to improve its user-defined functions

In the tool, parallel processing is an area that is contingent, in the sense that you have to be watchful for the cap that you have in terms of computing behind AWS Data Pipeline. You need to always watch for some reason. I am capped with 200 nodes, and if I get to use more than 200 nodes, the AWS Data Pipeline will fail. AWS doesn't state that I have almost gone beyond my limits, and it is allowing me now to go beyond the set limits if I talk to a representative and figure it out. Such aforementioned warnings are not let out by AWS, and they end up failing the nodes if I go beyond the set cap limits.

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Sridivya Chillappa

application security engineer at Hyperspace IT India

Efficient data integration reduces operational time and enhances metadata management

For the initial setup with AWS Glue, I find it easy to set up the data catalog and create Glue jobs using the visual editor or the visual code. Setting permission sets via IAM rules can be a bit tricky at the start, but we ensure Glue has access to AWS S3, Redshift, and other services. Once the role is configured, it runs smoothly. For advanced configurations, connecting to VPCs and setting up connections with JDBC sources takes more time compared to my cloud experience, but overall, for someone with cloud and ETL experience, the setup is manageable and well done.

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Quotes from Members

We asked business professionals to review the solutions they use. Here are some excerpts of what they said:

Pros

"It is a stable solution...It is a scalable solution."

"The most valuable feature of the solution is that orchestration and development capabilities are easier with the tool."

"I like that it's flexible, powerful, and allows you to write your own queries and scripts to get the needed transformations."

"You do not need many frameworks to run Glue."

"It is AWS-integrated, there is end-to-end integration with the other AWS services, and it is also user-friendly."

"The solution helps organizations gain flexibility in defining the structure of the data."

"It is a stable and scalable solution."

"In my opinion, it is one of the best tools in the market for ETL processes because of the fact that you pay as you use, which separates it from other big tools such as PowerCenter, Pentaho Data Integration, and Talend."

"The most valuable feature for me is the visual interface of AWS Glue."

"It's fairly straightforward as a product; it's not very complicated."

More AWS Glue pros

Cons

"The user-defined functions have shortcomings in AWS Data Pipeline."

"It's almost semi-automatic because you must review and approve code push, which works well. Still, we had many problems getting there during the deployment process, but we got there."

"There could be an enhanced way of managing pure metadata management or data cataloging."

"The setup and installation is a bit complex without advanced knowledge or training."

"It is not clear how the partition discovery would have been affected by more data coming in."

"If there's a cluster-related configuration, we have to make worker notes, which is quite a headache when processing a large amount of data."

"I would like to see stable libraries at the moment they are not there."

"It would be better if it were more user-friendly. The interesting thing we found is that it was a little strange at the beginning. The way Glue works is not very straightforward. After trying different things, for example, we used just the console to create jobs. Then we realized that things were not working as expected. After researching and learning more, we realized that even though the console creates the script for the ETL processes, you need to modify or write your own script in Spark to do everything you want it to do. For example, we are pulling data from our source database and our application database, which is in Aurora. From there, we are doing the ETL to transform the data and write the results into Redshift. But what was surprising is that it's almost like whatever you want to do, you can do it with Glue because you have the option to put together your own script. Even though there are many functionalities and many connections, you have the opportunity to write your own queries to do whatever transformations you need to do. It's a little deceiving that some options are supposed to work in a certain way when you set them up in the console, but then they are not exactly working the right way or not as expected. It would be better if they provided more examples and more documentation on options."

"The solution’s technical support could be improved."

"Improvements in the UI are needed, as it is challenging to understand some functionalities."

More AWS Glue cons

Pricing and Cost Advice

"The way we use it, I think it is fair as we're getting a good value for money compared to having a server or some other data pipeline."

"I rate the pricing between six to eight on a scale from one to ten, where one is low price, and ten is high price."

"Its price is good. We pay as we go or based on the usage, which is a good thing for us because it is simple to forecast for the tool. It is good in terms of the financial planning of the company, and it is a good way to estimate the cost. It is also simple for our clients. In my opinion, it is one of the best tools in the market for ETL processes because of the fact that you pay as you use, which separates it from other big tools such as PowerCenter, Pentaho Data Integration, and Talend."

"If you are using the solution for an enterprise business, it will be expensive."

"I rate the tool's pricing a four out of ten."

"The solution's pricing is based on DPUs so it is a good idea to optimize use or it can get expensive."

"I rate the product's pricing a five on a scale of one to ten, where one is a high price, and ten is a low price."

"It is not expensive. AWS Glue works on the serverless architecture. We get charged for the time the server is up. For our use case, we have to use it once in a day, and it is not expensive for us."

"The pricing is a bit higher than other solutions like Athena and EC2. If the pricing becomes more scaled or flexible, it will be good because you have to pay 44 cents just for one DPU for an hour. If you increase DPUs to 5 or 10, the pricing gets multiplied. There are also some time limits like 0 to 10 minutes or 10 to 20 minutes. If the pricing is according to the minutes, it would be better because you have to limit your job to 10 minutes or 20 minutes."

"The overall cost of AWS Glue could be better. It cost approximately $1,000 a month. There is paid support available from AWS Glue."

More AWS Glue pricing and cost advice

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

19%

Computer Software Company

Manufacturing Company

Comms Service Provider

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

By reviewers
Company Size	Count
Small Business	11
Midsize Enterprise	6
Large Enterprise	34

Questions from the Community

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What are the most common use cases for AWS Glue?

AWS Glue's main use case is for allowing users to discover, prepare, move, and integrate data from multiple sources. The product lets you use this data for analytics, application development, or ma...

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Comparisons

Perspectium DataSync vs AWS Data Pipeline [EOL]

Compared 34% of the time

IBM InfoSphere DataStage vs AWS Data Pipeline [EOL]

Compared 27% of the time

More AWS Data Pipeline [EOL] Competitors

AWS Database Migration Service vs AWS Glue

Compared 38% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs AWS Glue

Compared 5% of the time

SSIS vs AWS Glue

Compared 5% of the time

Palantir Foundry vs AWS Glue

Compared 4% of the time

Amazon Data Firehose vs AWS Glue

Compared 4% of the time

More AWS Glue Competitors

Product Reports

Buyer's Guide

Cloud Data Integration

May 2026

Download AWS Data Pipeline [EOL] product report

Buyer's Guide

AWS Glue

May 2026

Download AWS Glue product report

Overview

AWS Data Pipeline is a web service that helps you reliably process and move data between different AWS compute and storage services, as well as on-premises data sources, at specified intervals. ... AWS Data Pipeline also allows you to move and process data that was previously locked up in on-premises data silos.

Amazon Web Services (AWS)

AWS Glue is a serverless data integration service offering seamless integration with AWS services like S3, Redshift, and Athena. Known for its flexibility with data formats and automation of ETL tasks, AWS Glue enhances data management and transformation.

AWS Glue facilitates seamless data extraction, transformation, and loading for businesses, integrating with key AWS services, allowing efficient data pipeline automation. It's valued for a user-friendly GUI, scalability, and cost-effectiveness, supporting PySpark for complex datasets and includes a robust data catalog, real-time backup capabilities, and code generation. Despite its strengths, improvements are needed in documentation, training, and broader programming language support. Users face challenges with its complex interface and integration with non-AWS products, driving demand for enhancements in its usability and performance.

What are AWS Glue's most important features?

Seamless AWS Integration: Connects effortlessly with services like S3, Redshift, and Athena.
Serverless Operation: Removes the need for infrastructure management.
Flexible Data Format Handling: Supports various data formats for comprehensive data management.
User-Friendly GUI: Simplifies the creation and management of ETL jobs.
Data Catalog: Provides an organized repository for metadata management.
Real-Time Data Backup: Ensures data integrity and availability.
Code Generation: Facilitates easy script generation for data processes.

What benefits or ROI should users look for in reviews?

Cost Efficiency: Reduced operational costs through serverless architecture.
High Performance: Efficient data processing and transformation capabilities.
Scalability: Adapts to businesses' evolving data demands.
Ease of Use: Intuitive interface allowing streamlined ETL workflows.
Automation: Simplified data pipeline tasks saving time and effort.

Businesses leverage AWS Glue in industries for ETL processes, data integration, and transformation. It is used to optimize data lakes or warehouses integration, enhancing data cataloging and real-time integration. Its serverless feature enables efficient data processing in sectors like finance and healthcare, where handling complex data-intensive tasks is crucial.

Amazon Web Services (AWS)

Sample Customers

bp, Cerner, Expedia, Finra, HESS, intuit, Kellog's, Philips, TIME, workday

Buyer's Guide

Cloud Data Integration

May 2026

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