Amazon Web Services (AWS) Reviews, Ratings & Features 2026 | Gartner Peer Insights

The market for data integration tools consists of stand-alone software products that enable organizations to combine data from multiple sources and perform tasks related to data access, transformation, enrichment and delivery. They enable use cases such as data engineering, delivering modern data architectures, self-service data integration, operational data integration and supporting AI projects. Data management leaders procure data integration tools for their teams, including data engineers and data architects, or for other users, such as business analysts or data scientists. These products are primarily consumed as SaaS or deployed on-premises, in public or private cloud, or in hybrid configurations.

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Data Integration Tools

AWS Glue

Amazon SageMaker AI

Amazon SageMaker

Amazon Web Services (AWS)

Data Analyst

Overall. My experience with AWS Glue has been very positive. It is a feature-rich, serverless data integration platform that simplifies building and managing ETL pipelines at scale.

The platform is particularly strong in metadata management, data transformation and seamless integration with AWS native services.

AWS Glue stands out due to its wide range of connectors, automated schema delivery, and flexible transformation capabilities using spark. 

While it requires some expertise to fully leverage advanced features like optimization and data modelling, it provides a comprehensive and scalable solution for modern data engineering use cases.

50M - 250M USD

<50M USD

500M - 1B USD

250M - 500M USD

1B - 3B USD

3B - 10B USD

30B + USD

Gov't/PS/ED <5,000 Employees

10B - 30B USD

Gov't/PS/ED 5,000 - 50,000 Employees

Gov't/PS/ED 50,000 + Employees

Director of IT Services

Este serverless de integracion de datos de Amazon Web Services a partir de mi experiencia personal me parece un buen producto porque permite extraer, transformar y cargar datos eligiendo para ellos diferentes fuentes, automatizando de una forma muy eficaz pipelines de datos.
La parte que nos genero o sembró mas dudas es que si no optimizas tus jobs tus costes van a crecer.

Based on my personal experience, this serverless data integration tool from Amazon Web Services seems like a good product because it allows you to extract, transform, and load data from different sources, automating data pipelines very effectively.

The part that raised the most questions for us is that if you don't optimize your jobs, your costs will increase.

Lead Cloud Infrastructure Specialist

From my perspective as a Principal Cloud Infrastructure Analyst, AWS Glue is a strong and exceptional fit for managed ETL in our Data Mesh context, but it requires disciplined engineering around governance, cost control, and operational runbooks.  Reliability has been good when we enforce network connection standards, IAM role standards, and repeatable aggregation/deployment patterns.  Where complexity shows up is not in basic ETL, but in scale behavior: schema drift, crawler edge cases, cross-account catalog sync, and differences between small dev datasets and real production distributions.

Reliability & Uptime: Glue jobs and notebooks have been stable for batch pipelines (Landing Parquet to Raw Iceberg), especially after standardizing session/job settings, VPC connections, and naming conventions.  I also found that explicitly using Glue 5.0 for Lake Formation fine-grained access reduced friction compared to Glue 4.0-era workarounds.

Support Quality: Support has been effective when issues are concrete (permissions, crawler configurations, Iceberg parameters, job retries), but root-cause analysis still depends heavily on in-house expertise and structured troubleshooting.  The turn-around in these scenarios, or especially when the solution requires a feature enhancement, can be a blocker and something to consider.

Integration Success: I have seen Glue integrations work well with Lake Formation, Athena, Data Catalog/Data Mesh patterns, and Snowflake interoperability designs.  In practice, data integration quality improves when ownership boundaries are clear (single writer principle, explicit account/environment segregation, and defined IAM trust patterns)

Business Impact: Glue has allowed us to move faster on governed data ingestion, data modeling and data transformation, while maintaining security guardrails and metadata consistency as first-class concerns.

Scenario 1: I worked through Glue 4.0/earlier limitations where column-level filtering was problematic for some Lake Formation-tagged datasets, and I used Athena-based workarounds (direct queries/JDBC) where needed.  Moving to Glue 5.0 with proper session configurations and Lake Formation fine-grained settings materially simplified operations and reduced workaround overhead.

Scenario 2: I dealt with crawler quality issues in Data Mesh onboarding (table over-consolidation, schema migration, partition/schema inconsistencies, and KMS-key-related read problems).  We have improved reliability by tightening the S3 layout conventions, exclusion patterns, crawl scope, and schema update controls, plus targeted re-crawls and manual DDL fallback where necessary.

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Amazon Web Services (AWS), established in 2006, is focused on providing essential infrastructure services to businesses globally in the form of cloud computing. The key advantage offered through cloud computing, particularly via AWS, is its capacity to shift fixed infrastructure expenses into flexible costs. Businesses have been able to forgo extensive planning and procurement of servers and other Information Technology (IT) resources, owing to AWS. AWS seeks to provide businesses with prompt and cost-effective access to resources using Amazon's expertise and economies of scale, as and when their business requires. Currently, AWS offers a robust, scalable, economic infrastructure platform on the cloud powering an extensive array of businesses worldwide. It operates across numerous industries with data center locations in various parts of the globe including U.S., Europe, Singapore, and Japan.

Launched in 2006, Amazon Web Services (AWS) began exposing key infrastructure services to businesses in the form of web services -- now widely known as cloud computing. The ultimate benefit of cloud computing, and AWS, is the ability to leverage a new business model and turn capital infrastructure expenses into variable costs. Businesses no longer need to plan and procure servers and other IT resources weeks or months in advance. Using AWS, businesses can take advantage of Amazon's expertise and economies of scale to access resources when their business needs them, delivering results faster and at a lower cost. Today, Amazon Web Services provides a highly reliable, scalable, low-cost infrastructure platform in the cloud that powers hundreds of thousands of businesses in 190 countries around the world. With data center locations in the U.S., Europe, Singapore, and Japan, customers across all industries are taking advantage of our low cost, elastic, open and flexible, secure platform.

Reviews

Ratings Overview

Overall experience with

Favorable Review

AWS Glue is a Strong Enterprise ETL Platform if you Engineer the Operating Model Around It

Critical Review

Integrating serverless data into AWS facilitates synergies but requires extra resources

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