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

6.5

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.

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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

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

"AWS Glue is a good solution for developers, they have the ability to write code in different languages and other software."

"I like its integration and ability to handle all data-related tasks."

"The AWS Glue Data Catalog provides metadata management and schema discovery. AWS Glue simplifies data transformation with automatic schema detection, incremental data updates, and integration with other AWS services."

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

"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 product has a valuable feature for data catalog."

"AWS Glue has reduced efforts by 60%, which is the main benefit."

More AWS Glue pros

"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."

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

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

"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."

"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."

"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."

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

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

Cons

"The solution's visual ETL tool is of no use for actual implementation."

"The monitoring is not that good."

"It is quite clunky and code-heavy, which is my biggest problem."

"There is a learning curve to this tool."

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

"The start-up time is really high right now. For instance, when you start up a new job, you have to wait for five or eight minutes before it starts. If the start-up time is reduced to one or two minutes, it will be great. It will be better to have a direct linkage to Redshift in AWS. If we can use data catalogs from Redshift, it will be so easy to create some data catalogs. Currently, we can only use data catalogs from S3."

"The point for improvement in AWS Glue would be the dynamic allocation of resources while utilizing Lambda functions."

"I am quite technical, yet when it comes to data pipelines, I prefer using GUIs over code-based calls. It might be easier with a different skill set."

More AWS Glue cons

"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."

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

"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."

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

"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

"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 would rate the solution a six or seven on a scale of one to ten, with ten being very expensive. Specifically, I rate its pricing a six 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."

"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."

"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."

"I rate pricing an eight out of ten."

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

"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."

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 39% of the time

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Compared 5% of the time

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Compared 5% of the time

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

Alteryx Designer 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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