AWS Glue vs Rivery comparison

Amazon Web Services (AWS) and Rivery are both solutions in the Cloud Data Integration category. Amazon Web Services (AWS) is ranked #1 with an average rating of 7.5, while Rivery is ranked #15 with an average rating of 7.8. Amazon Web Services (AWS) holds a 9.2% mindshare in CDI, compared to Rivery’s 1.1% mindshare. Additionally, 90% of Amazon Web Services (AWS) users are willing to recommend the solution, compared to 100% of Rivery users who would recommend it.

AWS Glue

Read 50 AWS Glue reviews

6,730 Views
4,509 Comparison Views

90% willing to recommend

Rivery

Read 8 Rivery reviews

1,292 Views
362 Comparison Views

100% willing to recommend

AWS Glue

Rivery

Comparison Buyer's Guide

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Executive SummaryUpdated on Dec 3, 2024

AWS Glue and Rivery are competing platforms in the data integration and ETL space. Rivery seems to outperform in user satisfaction, especially in terms of usability and scalability.

Features: AWS Glue provides automated ETL tasks, a serverless architecture, and seamless integration with other AWS services, enhancing data processing capabilities. Rivery offers intuitive data pipelines, pre-built data connectors, and real-time data processing, suitable for rapid deployment and agile operations. The difference lies in Glue's focus on automation versus Rivery's emphasis on ease of use and flexibility.

Ease of Deployment and Customer Service:AWS Glue requires deep integration within the AWS ecosystem, which can be complex for those not fully engaged with AWS infrastructure. Rivery promotes easier deployment with simplified data workflows and robust support, favoring businesses seeking quick implementation without exhaustive technical overhead. Customer support is more praised in Rivery, reflecting a more responsive service experience.

Pricing and ROI: AWS Glue offers cost efficiency as part of the AWS pay-as-you-go model, encouraging scalable cost management. However, Rivery's pricing is often viewed as more transparent, providing clear ROI through comprehensive plans that align with diverse business scales. Despite Glue's pricing flexibility, Rivery is seen as delivering high value, perceived as worth the setup costs by facilitating higher operational efficiency.

To learn more, read our detailed AWS Glue vs. Rivery Report (Updated: December 2025).

Buyer's Guide

AWS Glue vs. Rivery

December 2025

Download the complete report

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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 Glue

Ranking in Cloud Data Integration

1st

Average Rating

7.8

Reviews Sentiment

6.9

Number of Reviews

Ranking in other categories

No ranking in other categories

Rivery

Ranking in Cloud Data Integration

15th

Average Rating

8.0

Reviews Sentiment

6.4

Number of Reviews

Ranking in other categories

Data Integration (22nd), Migration Tools (3rd), Cloud Migration (11th)

Mindshare comparison

As of February 2026, in the Cloud Data Integration category, the mindshare of AWS Glue is 9.2%, down from 20.0% compared to the previous year. The mindshare of Rivery is 1.1%, up from 0.5% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Integration Market Share Distribution
Product	Market Share (%)
AWS Glue	9.2%
Rivery	1.1%
Other	89.7%

Cloud Data Integration

Featured Reviews

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.

Read full review

Anup Dhungel

CDL at Ycotek

Training has boosted custom ETL scripting and now debugging complex incremental loads needs work

The best feature Rivery offers is the ability to build custom or user-defined functions. You can even develop Python scripts to perform transformations on your data frames. This flexibility allows you to implement custom requirements, making Rivery more versatile than relying solely on in-built functions. Regarding features such as the interface, scheduling, or connectors, I found that as of 2022 when I last used it, the monitoring was good, although the debugging process for custom scripts was somewhat challenging. If we encountered issues with custom-built scripts, debugging was difficult since it used to send standard errors rather than specific ones. From what I recall, monitoring worked well, and we could connect to multiple relational and other sources, which was advantageous. A few of my colleagues and I were able to earn certifications on Rivery, which motivated us, even though we could not pursue or implement Rivery project for clients. The learning experience was very valuable as we had around seven or eight resources participating in those trainings, and they were all excited to learn about this new tool for us at the time.

Read full review

Quotes from Members

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

Pros

"AWS Glue is fast and managed by AWS. Hence, you don't have to worry about capacity and the performance of Glue jobs. It has integrations with other data stores of AWS. The product offers metadata management, logging, and ETL processing capabilities. It comes with a powerful feature, Glue Studio, which helps to do queries interactively within the community. It is a managed service and very secure. Another popular and mature service is S3."

"The key role for Glue is that it hosts our metadata before rolling out our actual data. This is the major advantage of using this solution and our clients client have been very satisfied with it."

"Our entire use case was very easily handled or solved using this solution."

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

"I also like that you can add custom libraries like JAR files and use them. So, the ability to use a fast processing engine and embed basic jobs easily are significant advantages."

"The best thing about AWS Glue is its scalability and how easy it is to process a large amount of data."

"I appreciate AWS Glue for its cost-effectiveness."

"The solution's technical support is good. Whenever we raise a use case where we face an issue in our company, we get a response from the solution's technical team."

More AWS Glue pros

"The main benefit Rivery brought to my organization was the time we were able to save on development, and by using all these automation features provided by Rivery together with integration to Snowflake, we were able to solve problems."

"Connects to many APIs in the market and new ones are being added all the time."

"The process of using Rivery has worked very smoothly for me, and I found the tool very easy to use, allowing me to gain a lot of insights."

"Rivery has positively impacted my organization by enabling us to create a stable pipeline, and we can expand the variety of the data sources as we go very easily, with no worries, and with a lot of confidence in the fact that it can be done."

"Rivery has positively impacted my organization by reducing the need for a big team of data engineers and speeding up the work when we need to connect to a new data source; this can happen really fast."

"Anyone looking into using Rivery should know it is a great product."

"Rivery has positively impacted my organization by helping me streamline my data ingestion and transformation processes, especially in combination with Snowflake."

"The solution's most valuable features are that it is quick to connect and simple to use."

Cons

"The solution’s stability could be improved."

"Overall, I consider the technical support to be fine, although the response time could be faster in certain cases."

"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."

"Setting up pipelines is challenging, especially with version control and testing requirements."

"In terms of improvement, the performance of AWS Glue could be faster."

"AWS Glue would be improved by making it easier to switch from single to multi-cloud."

"The process of entering environment variables in AWS Glue requires navigating to a different page, which could be streamlined."

"AWS Glue should be more reliable and faster in processing. Enhancing the speed of data processing would be beneficial."

More AWS Glue cons

"My experience with pricing, setup cost, and licensing was unfortunately not so good in my recent data project."

"Lineage and an impact analysis or logic dependency are lacking."

"I think Rivery could be improved by having more analytical features inside."

"Pricing is a little steep for smaller organizations, I would say. The product's pricing model could be a little bit better."

"The key challenges I recall were related to the incremental merging logic, which was not functioning properly."

"As an end user of Rivery, I would like to see it be less commercial for users."

"Rivery can be improved by generally investing a bit more depth into the product, mainly in orchestration and specific features comparable to mature products."

Pricing and Cost Advice

"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 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."

"I rate the tool an eight on a scale of one to ten, where one is expensive, and ten is expensive."

"This solution is affordable and there is an option to pay for the solution based on your usage."

"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 tool's pricing a four out of ten."

"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."

More AWS Glue pricing and cost advice

"I rate the tool's price as six out of ten if I consider the lowest price to be one and the highest price to be ten."

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Top Industries

By visitors reading reviews

Financial Services Firm

20%

Computer Software Company

12%

Manufacturing Company

Insurance Company

Manufacturing Company

12%

Comms Service Provider

10%

Computer Software Company

Financial Services Firm

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

By reviewers
Company Size	Count
Small Business	11
Midsize Enterprise	6
Large Enterprise	32

By reviewers
Company Size	Count
Small Business	4
Midsize Enterprise	1
Large Enterprise	3

Questions from the Community

How do you select the right cloud ETL tool?

AWS Glue and Azure Data factory for ELT best performance cloud services.

See all answers

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...

See all answers

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...

See all answers

What is your experience regarding pricing and costs for Rivery?

The tool's price can be a little steep for a small organization. I rate the tool's price as six out of ten if I consider the lowest price to be one and the highest price to be ten.

See all answers

What needs improvement with Rivery?

I don't know what could be improved in terms of what my company was used to previously or after moving over to Rivery. I have not had much experience with platforms other than Rivery. For me, Rival...

See all answers

What is your primary use case for Rivery?

My company has started to use the Rivery extract data from Hive. It is like a project management sort of program, and we started to use Rivery to get the data from there over into Mavenlink, so we ...

See all answers

Comparisons

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Compared 40% of the time

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Compared 5% of the time

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Informatica Intelligent Data Management Cloud (IDMC) vs AWS Glue

Compared 4% of the time

Oracle Integration Cloud Service vs AWS Glue

Compared 4% of the time

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Matillion Data Productivity Cloud vs Rivery

Compared 15% of the time

dbt vs Rivery

Compared 15% of the time

Azure Data Factory vs Rivery

Compared 11% of the time

OpenText Migrate vs Rivery

Compared 9% of the time

Fivetran vs Rivery

Compared 7% of the time

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Product Reports

Buyer's Guide

AWS Glue

January 2026

Download AWS Glue product report

Buyer's Guide

Data Integration

January 2026

Download Rivery product report

Overview

AWS Glue is a serverless cloud data integration tool that facilitates the discovery, preparation, movement, and integration of data from multiple sources for machine learning (ML), analytics, and application development. The solution includes additional productivity and data ops tooling for running jobs, implementing business workflows, and authoring.

AWS Glue allows users to connect to more than 70 diverse data sources and manage data in a centralized data catalog. The solution facilitates visual creation, running, and monitoring of extract, transform, and load (ETL) pipelines to load data into users' data lakes. This Amazon product seamlessly integrates with other native applications of the brand and allows users to search and query cataloged data using Amazon EMR, Amazon Athena, and Amazon Redshift Spectrum.

The solution also utilizes application programming interface (API) operations to transform users' data, create runtime logs, store job logic, and create notifications for monitoring job runs. The console of AWS Glue connects all of these services into a managed application, facilitating the monitoring and operational processes. The solution also performs provisioning and management of the resources required to run users' workloads in order to minimize manual work time for organizations.

AWS Glue Features

AWS Glue groups its features into four categories - discover, prepare, integrate, and transform. Within those groups are the following features:

Automatic schema discovery: AWS Glue crawlers connect to the organization's source or target data source through a prioritized list of classifiers to determine the schema for users' data. This feature creates metadata in companies' AWS Glue Data Catalog.
Schemas for data stream management: The AWS Glue Schema Registry enables users to validate and control the evolution of streaming data through registered Apache Avro schemas for no additional charge.
Automatic scaling based on workload: This feature dynamically scales resources up and down based on workload. The feature controls job resources, removing them depending on how much the workload can be split up.
FindMatches: This feature is for machine learning-based data deduplication and cleansing, and works by finding records that are imperfect matches of each other to remove useless data copies.
Edit, debug, and test ETL code: This feature helps users who have chosen to interactively develop their ETL code by providing development endpoints for editing, debugging, and testing the code it generates for them.
AWS Glue DataBrew: An interactive, point-and-click visual interface for specialists to clean and normalize data without the need to write any code.
AWS Glue Interactive Sessions: This feature simplifies the development of data integration jobs by enabling data engineers to interactively prepare and explore data.
AWS Glue Studio Job Notebooks: This AWS Glue feature provides serverless notebooks with minimal setup, allowing developers to start working in a timely manner.
Complex ETL pipeline building: This feature allows the product to be invoked on a schedule, on demand, or based on an event, allowing users to start multiple jobs in parallel or specify dependencies to build complex ETL pipelines.
AWS Glue Studio: This AWS Glue feature allows users to visually transform data through a drag-and-drop interface. The product automatically generates the code for ETL processes for users' data.

AWS Glue Benefits

AWS Glue offers a wide range of benefits for its users. These benefits include:

Users of other AWS products can easily onboard with AWS Glue, as it is integrated across a wide range of the company's services.
The solution is serverless, which allows for a lower total cost of ownership.
AWS Glue offers more power for users, as it automates much of the effort in building, maintaining, and running ETL jobs.
The product allows customers to easily discover and search across all their AWS datasets through AWS Glue Data Catalog.
AWS Glue does not require additional payment for managing and enforcing schemas for data streams.
The solution facilitates the authority of scalable ETL jobs for beginners and non-coding experts through a drag-and-drop interface.

Reviews from Real Users

Mustapha A., a cloud data engineer at Jems Groupe, likes AWS Glue because it is a product that is great for serverless data transformations.

Liana I., CEO at Quark Technologies SRL, describes AWS Glue as a highly scalable, reliable, and beneficial pay-as-you-go pricing model.

Amazon Web Services (AWS)

Rivery is a serverless, SaaS DataOps platform that empowers companies of all sizes around the world to consolidate, orchestrate, and manage internal and external data sources with ease and efficiency.

By offering comprehensive data solutions and partnering with complementary technology providers, including Google, Snowflake, Tableau, and Looker, Rivery enables data-driven companies to build the perfect ecosystems for all their data processes.

Rivery

Sample Customers

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

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Buyer's Guide

AWS Glue vs. Rivery

December 2025

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