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AWS Data Pipeline [EOL] vs AWS Glue comparison

 

Comparison Buyer's Guide

Executive Summary

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
2
Ranking in other categories
No ranking in other categories
AWS Glue
Average Rating
7.8
Reviews Sentiment
7.0
Number of Reviews
49
Ranking in other categories
Cloud Data Integration (1st)
 

Featured Reviews

Geoffrey Leigh - PeerSpot reviewer
A stable, scalable, and reliable solution for moving and processing data
We're only considering enhancing the presentation layer to give a more multidimensional OLAP view that AWS seems to have decided on. Redshift with the data mart structure is like an OLAP cube. Oracle Analytics Cloud is an over-code killer and is not what we need. I was looking at Mondrian, which used to be part of the open-source stack from another vendor that works. Still, I am also looking at some of the other OLAP environments like Kaiser and perhaps decided to go to Azure with Microsoft Azure analysis cloud, but that's not multidimensional either as SSAS used to be. We tried the Mondrian, and that didn't perform how we expected. So, we are looking at resetting something to perform as an OLAP in the cloud, particularly AWS, so that we might consider an Azure solution.
Saurabh Jaiswal - PeerSpot reviewer
Enables seamless integration and data preparation with robust transformation capabilities
AWS Glue's most valuable features include its transformation capabilities, which provide data quality and shape for processing in ML or AI models. It offers transformation options on canvas or through ETL pipelines, notebooks, and code. Additionally, it supports data preparation, cleaning, and filtering seamlessly. AWS Glue also enhances job scheduling and orchestration capabilities, integrating with AWS Glue Studio for comprehensive data workflow management.

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."
"Its ease of use, cost-effectiveness, and highly secure architecture are some of the most valuable features."
"AWS Glue is a good solution for developers, they have the ability to write code in different languages and other software."
"The solution is serverless so it allows us to transform data while optimizing the cost and performance of Spark jobs."
"We no longer had to worry much about infrastructure management because AWS Glue is serverless, and Amazon takes care of the underlying infrastructure."
"One aspect that I would like to highlight is the Glue Crawler, which we utilize when working with large datasets to ensure the schema updates seamlessly without requiring end-team knowledge."
"I like its integration and ability to handle all data-related tasks."
"The solution integrates well with other AWS products or services."
"What I like best about AWS Glue is its real-time data backup feature. Last week, there was a production push, and what used to take almost ten days to send out around fifty-six thousand emails now takes only two hours."
 

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 is a learning curve to this tool."
"The solution’s technical support could be improved."
"Overall, I consider the technical support to be fine, although the response time could be faster in certain cases."
"The drawbacks associated with the product stem from the fact that, based on the data volume, it can become very costly."
"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."
"Beginners need additional support as it currently lacks some features required for complex transformations, often necessitating custom Python coding."
"In terms of performance, if they can further optimize the execution time for serverless jobs, it would be a welcome improvement."
"The mapping area and the use of the data catalog from Glue could be better."
 

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."
"AWS Glue is a paid service that doesn't come under the free trial of AWS."
"The solution's pricing is based on DPUs so it is a good idea to optimize use or it can get expensive."
"Technical support is a paid service, and which subscription you have is dependent on that. You must pay one of them, and it ranges from $15,000 to $25,000 per year."
"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."
"AWS Glue follows a pay-as-you-go model, wherein the cost of the data you use will be counted as a monthly bill."
"I rate pricing an eight out of ten."
"It is an expensive product. I rate its pricing a nine out of ten."
"I rate the tool an eight on a scale of one to ten, where one is expensive, and ten is expensive."
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Top Industries

By visitors reading reviews
Computer Software Company
27%
Financial Services Firm
19%
Educational Organization
7%
Government
6%
Financial Services Firm
21%
Computer Software Company
13%
Manufacturing Company
8%
Government
6%
 

Company Size

By reviewers
Large Enterprise
Midsize Enterprise
Small Business
No data available
 

Questions from the Community

What do you like most about AWS Data Pipeline?
The most valuable feature of the solution is that orchestration and development capabilities are easier with the tool.
What is your experience regarding pricing and costs for AWS 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.
What needs improvement with AWS Data Pipeline?
The user-defined functions have shortcomings in AWS Data Pipeline. The user-defined functions could be one of the areas where I can write a custom function and embed it as a part of AWS Data Pipeli...
How do you select the right cloud ETL tool?
AWS Glue and Azure Data factory for ELT best performance cloud services.
How does Talend Open Studio compare with AWS Glue?
We reviewed AWS Glue before choosing Talend Open Studio. AWS Glue is the managed ETL (extract, transform, and load) from Amazon Web Services. AWS Glue enables AWS users to create and manage jobs in...
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...
 

Comparisons

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