AWS Glue vs Amazon Data Firehose comparison

Amazon Data Firehose and AWS Glue are both solutions in the Cloud Data Integration category. Amazon Data Firehose is ranked #20 with an average rating of 9.0, while AWS Glue is ranked #1 with an average rating of 7.6. Amazon Data Firehose holds a 0.9% mindshare in CDI, compared to AWS Glue’s 21.3% mindshare. Additionally, 100% of Amazon Data Firehose users are willing to recommend the solution, compared to 89% of AWS Glue users who would recommend it.

Amazon Data Firehose

Read 1 Amazon Data Firehose review

106 Views
85 Comparison Views

100% willing to recommend

AWS Glue

Read 48 AWS Glue reviews

7,339 Views
5,504 Comparison Views

89% willing to recommend

Amazon Data Firehose

AWS Glue

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Executive Summary

We performed a comparison between Amazon Data Firehose 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: March 2025).

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Cloud Data Integration

March 2025

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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

Amazon Data Firehose

Ranking in Cloud Data Integration

20th

Average Rating

9.0

Reviews Sentiment

8.1

Number of Reviews

Ranking in other categories

No ranking in other categories

AWS Glue

Ranking in Cloud Data Integration

1st

Average Rating

7.8

Reviews Sentiment

7.0

Number of Reviews

Ranking in other categories

No ranking in other categories

Mindshare comparison

As of April 2025, in the Cloud Data Integration category, the mindshare of Amazon Data Firehose is 0.9%. The mindshare of AWS Glue is 21.3%, down from 21.7% compared to the previous year. It is calculated based on PeerSpot user engagement data.

Cloud Data Integration

Featured Reviews

Johnny Suleiman

MS AWS expert at Bespin Global MEA, an e& enterprise Company

Enhances our AI-driven analytics projects by providing a means to manage data streaming and delivery at any scale

The primary use case of Amazon Data Firehose is for real-time streaming data, specifically for data analysis and collection purposes. It is used to extract useful data and export it for machine learning algorithms to analyze, providing real-time data streaming Amazon Data Firehose enhances our…

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Muthuvel Sivaraman

AVP at a manufacturing company with 10,001+ employees

Handles a huge volume of data and is serverless, but it can be considered costly by some users

We use Amazon's services to provide technical support for the product. If you want to have support, Oracle and others offer a single support, and other tools have a direct support window. For Amazon, we need to pay 10 percent of my billing amount for the tool to get support services. Whether to raise a support ticket or not is an issue since ten percent is a huge amount. My company ends up using all the options without help from support. It is very difficult for any common man to understand why there is a need to pay ten percent for support. If I find an issue in the product, and I need to get support from AWS to fix it, then I need to pay ten percent of the tool's bill amount to Amazon. AWS is a very tricky tool because everything is evolving nowadays. AWS engineers are getting hired from other places, and even after that, if I am not getting any technical support, then things will be very nasty. There are some good engineers who help users outside the normal support cycle, but it doesn't meet their needs. I rate the technical support a four out of ten.

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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

"The most valuable feature is its capability for real-time data streaming."

"The solution is stable and reliable."

"The facility to integrate with S3 and the possibility to use Jupyter Notebook inside the pipeline are the most valuable features."

"The solution is serverless so it allows us to transform data while optimizing the cost and performance of Spark jobs."

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

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

"It is AWS-integrated. There is end-to-end integration with the other AWS services. It is also user-friendly."

"AWS Glue is quite better than other tools, but you have to learn it properly before you start using it."

"AWS Glue's most valuable features are the data catalog, including crawlers and tables, and Glue Studio, which means you don't have to use custom code."

More AWS Glue pros

Cons

"Amazon Data Firehose enhances our AI-driven analytics projects by providing a means to manage data streaming and delivery at any scale."

"Beginners need additional support as it currently lacks some features required for complex transformations, often necessitating custom Python coding."

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

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

"The crucial problem with AWS Glue is that it only works with AWS. It is not an agnostic tool like Pentaho. In PowerCenter, we can install the forms from Google and other vendors, but in the case of AWS Glue, we can only use AWS."

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

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

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

"Only people who can code, either in Java or Python, can use the product freely. Those who don't know Java or Python might find using AWS Glue difficult."

More AWS Glue cons

Pricing and Cost Advice

Information not available

"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 pricing an eight out of ten."

"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."

"AWS Glue uses a pay-as-you-go approach which is helpful. The price of the overall solution is low and is a great advantage."

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

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

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

More AWS Glue pricing and cost advice

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Top Industries

By visitors reading reviews

No data available

Financial Services Firm

22%

Computer Software Company

13%

Manufacturing Company

Government

Company Size

By reviewers

Large Enterprise

Midsize Enterprise

Small Business

No data available

Questions from the Community

What is your experience regarding pricing and costs for Amazon Data Firehose?

The pricing is fair and balanced for the capabilities provided by Amazon Data Firehose.

See all answers

What needs improvement with Amazon Data Firehose?

There is no specific improvement mentioned for Amazon Data Firehose itself. However, it was noted that there could be room for a better understanding of real-time data streaming concepts for junior...

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What is your primary use case for Amazon Data Firehose?

See all answers

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

Comparisons

AWS Database Migration Service vs Amazon Data Firehose

Compared 41% of the time

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AWS Database Migration Service vs AWS Glue

Compared 37% of the time

SSIS vs AWS Glue

Compared 5% of the time

Informatica Intelligent Data Management Cloud (IDMC) vs AWS Glue

Compared 5% of the time

Oracle Integration Cloud Service vs AWS Glue

Compared 5% of the time

SnapLogic vs AWS Glue

Compared 4% of the time

More AWS Glue Competitors

Product Reports

Buyer's Guide

Cloud Data Integration

March 2025

Download Amazon Data Firehose product report

Buyer's Guide

AWS Glue

March 2025

Download AWS Glue product report

Overview

Amazon Data Firehose offers a robust data streaming service that simplifies integration and data capture, enabling real-time data processing and analysis efficiently.

Amazon Data Firehose is a cloud-based service providing real-time data streaming into analytics tools like Amazon S3 and Redshift. It offers seamless scalability, enabling consistent delivery, transformation, and enriching capabilities of data streams. Users value its reliability for handling streaming data with minimal administration. It excels in performance for those seeking efficient data management. Still, there is room for enhancement in its pricing model to better cater to businesses with rapid fluctuations in data volume.

What are the key features of Amazon Data Firehose?

Data Transformation: Convert incoming data before delivering to destinations.
Automatic Scaling: Dynamically adjust to handle up to gigabytes of streaming data per second.
Destination Flexibility: Directly integrate with Amazon S3, Redshift, and Elasticsearch.
Data Formatting: Supports compression and format conversions.

What benefits should users expect from Amazon Data Firehose?

Efficient Data Handling: Secure and efficient real-time data processing.
Cost-Effective: Reduces infrastructure and management costs.
Simplified Integration: Easily integrates with storage and analytics services.
Consistent Performance: Reliable delivery even under high data loads.

Amazon Data Firehose finds diverse applications across industries like finance, healthcare, and e-commerce. In finance, it supports fraud detection via real-time data ingestion. Healthcare facilities use it for efficient patient monitoring systems. E-commerce platforms leverage its capabilities for real-time analytics, enhancing customer experiences with tailored offerings.

Amazon Web Services (AWS)

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)

Sample Customers

Information Not Available

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

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Cloud Data Integration

March 2025

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