Name: AWS Glue
Price: 0.44 USD
Rating: 7.9 (38 reviews)
Author: Amazon AWS

Digital Adoption

Help Desk

Web and Video Conferencing

Customer Support

Augmented Reality Development

Bug  Tracking

Development

Business Intelligence (BI)

Collaboration

Environmental Health and Safety (EHS)

Visual Collaboration

Enterprise

Accounting

Anti-Money Laundering (AML)

Investor Relations

Revenue Management

Spend Management

Finance and Accounting

Applicant Tracking

Corporate Learning Management

HR Management

On-Demand Payment

Payroll

Resume Parsing

Succession Planning

Talent Intelligence

Talent Management

Workforce Analytics

Workforce Management

Human Resources

Cloud Storage

Component Content Management

Data Pipeline

Electronic Data Interchange (EDI)

Fraud Detection

Hosted Virtual Desktop

Identity Governance and Administration

Industrial IoT

Integration Platform as a Service (iPaaS)

Interoperability

Managed File Transfer (MFT)

Managed Hosting

Monolithic Frame-based Disk Arrays

Network Performance Monitoring

Relational Database Management Systems

Security Awareness Training

Vulnerability Management

Information Technology

A/B Testing

Ad Serving & Retargeting

All-in-One Marketing

Content Management

Customer Journey Mapping

Digital Analytics

Email Marketing

Intent Data

Marketing Automation

Marketing Resource Management

Search Marketing Intelligence

Social Media Management

Survey & Forms Building

Tag Management

Web Analytics

Webinar

Marketing

Business Caller ID

Project Management

Prototyping

Professional Services

Call Recording

Customer Relationship Management (CRM)

Sales Email Tracking

Sales Intelligence

eCommerce

Sales

Operational Technology (OT) Security

Security

Computer-Aided Design (CAD)

Construction Accounting

Loan Servicing

Nonprofit Fundraising

Patient Engagement

Revenue Cycle Management (RCM)

Utilities

Vertical-Specific

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### Data Transformation
Users generally praise AWS Glue for its data transformation capabilities. Reviewers highlight the efficiency of AWS Glue in handling data with diverse formats, schemas, and volumes, particularly in scenarios where data normalization and structuring are essential for analytics. The platform's ability to automate ETL processes, generate and transform data in its data catalog, and integrate smoothly with other AWS services is well-received. However, some users express concerns about the complexity of transformations, debugging and monitoring challenges, and limitations when integrating with systems outside the AWS environment. Despite these drawbacks, AWS Glue is recognized for its role in simplifying data transformation tasks and enhancing data processing workflows. (Source Reviews: [1](https://www.trustradius.com/reviews/aws-glue-2023-07-25-07-04-51), [2](https://www.trustradius.com/reviews/aws-glue-2023-10-17-21-07-30), [3](https://www.trustradius.com/reviews/aws-glue-2022-09-08-10-08-33), [4](https://www.trustradius.com/reviews/aws-glue-2022-09-14-06-55-45), [5](https://www.trustradius.com/reviews/aws-glue-2022-09-15-23-58-43), [6](https://www.trustradius.com/reviews/aws-glue-2022-07-01-08-29-29), [7](https://www.trustradius.com/reviews/aws-glue-2020-01-13-07-57-36))

### Data Cataloging
Users generally praise AWS Glue's robust data cataloging capabilities. The data catalog feature of AWS Glue is highlighted for its ability to efficiently organize schemas, tables, and views, providing a centralized repository for data structure. Additionally, users appreciate the ease with which AWS Glue's data catalog allows for the synchronization of external and internal data sources, streamlining the process of data exploration and analysis. The automatic schema recognition and generation of SQL-based ETL pipelines based on catalog objects are also lauded for simplifying data management tasks. While some users note minor limitations such as integration challenges with systems outside the AWS environment, overall, AWS Glue's data cataloging functionality is viewed favorably for its role in enhancing data organization and accessibility. (Source Reviews: [1](https://www.trustradius.com/reviews/aws-glue-2022-09-14-06-55-45), [2](https://www.trustradius.com/reviews/aws-glue-2022-09-08-10-08-33), [3](https://www.trustradius.com/reviews/aws-glue-2022-09-15-23-58-43), [4](https://www.trustradius.com/reviews/aws-glue-2023-10-17-21-07-30), [5](https://www.trustradius.com/reviews/aws-glue-2022-07-01-08-29-29), [6](https://www.trustradius.com/reviews/aws-glue-2020-01-13-07-57-36))

### ETL Processes
Users generally praise AWS Glue for its robust ETL capabilities. Reviewers highlight the ease of creating ETL pipelines for transforming and moving data between various sources like S3, Snowflake, and Redshift. The auto-generated code simplifies the execution of Spark jobs, ensuring secure connections and data cleansing. Additionally, the serverless nature of AWS Glue allows for automatic scaling of resources, making ETL operations efficient and scalable. The integration with other AWS services and the ability to schedule crawlers or run jobs on demand further enhance the overall ETL experience for users. (Source Reviews: [1](https://www.trustradius.com/reviews/aws-glue-2023-10-17-21-07-30), [2](https://www.trustradius.com/reviews/aws-glue-2023-07-25-07-04-51), [3](https://www.trustradius.com/reviews/aws-glue-2022-09-15-23-58-43), [4](https://www.trustradius.com/reviews/aws-glue-2022-07-01-08-29-29), [5](https://www.trustradius.com/reviews/aws-glue-2022-09-14-06-55-45), [6](https://www.trustradius.com/reviews/aws-glue-2020-01-13-07-57-36))

### Schema Management
Users generally praise AWS Glue's data structuring capabilities. The sentiment regarding AWS Glue's schema management features is mostly positive, with users highlighting the ease of creating and transforming data schemas. The automation of schema recognition and the support for scheduling tasks are particularly appreciated. However, some users express concerns about the platform's limitation of only working within the AWS environment, which restricts its compatibility with systems outside of this ecosystem. Despite this drawback, AWS Glue's ability to efficiently manage schemas and facilitate data structuring tasks is widely acknowledged by users. (Source Reviews: [1](https://www.trustradius.com/reviews/aws-glue-2023-10-17-21-07-30), [2](https://www.trustradius.com/reviews/aws-glue-2022-09-08-10-08-33), [3](https://www.trustradius.com/reviews/aws-glue-2022-09-14-06-55-45), [4](https://www.trustradius.com/reviews/aws-glue-2022-09-15-23-58-43), [5](https://www.trustradius.com/reviews/aws-glue-2022-07-01-08-29-29), [6](https://www.trustradius.com/reviews/aws-glue-2020-01-13-07-57-36))

### Apache Spark Integration
The reviews suggest that while AWS Glue's integration with Apache Spark offers benefits such as easy execution of Spark jobs, automatic scaling of memory resources, and efficient schema crawling, there are concerns about its compatibility with systems outside the AWS environment. Users appreciate the ability to perform ETL processes and run Spark or Python shell jobs with AWS Glue, but some find limitations in its integration capabilities with non-AWS services and the requirement for engineers to be proficient in Spark. Despite some reservations, users generally recognize the value of Apache Spark integration within the AWS Glue ecosystem. (Source Reviews: [1](https://www.trustradius.com/reviews/aws-glue-2022-07-01-08-29-29), [2](https://www.trustradius.com/reviews/aws-glue-2023-07-25-07-04-51), [3](https://www.trustradius.com/reviews/aws-glue-2022-09-08-10-08-33), [4](https://www.trustradius.com/reviews/aws-glue-2022-09-15-23-58-43))

### Data Source Integration
Reviewers consistently highlight AWS Glue's adeptness in integrating with a wide array of data sources, such as S3, Redshift, and RDS, enabling smooth data movement and transformation processes. Users appreciate the ease of establishing connections with both internal and external data sources, underscoring the platform's versatility in handling diverse data formats and structures. Despite some concerns regarding performance optimization and cost efficiency, AWS Glue's seamless integration with various AWS services like Athena and QuickSight remains a key strength for organizations seeking efficient data analysis and processing solutions. (Source Reviews: [1](https://www.trustradius.com/reviews/aws-glue-2023-07-25-07-04-51), [2](https://www.trustradius.com/reviews/aws-glue-2022-09-14-06-55-45), [3](https://www.trustradius.com/reviews/aws-glue-2023-10-17-21-07-30), [4](https://www.trustradius.com/reviews/aws-glue-2022-09-15-23-58-43), [5](https://www.trustradius.com/reviews/aws-glue-2020-01-13-07-57-36))

AWS Glue is a managed extract, transform, and load (ETL) service designed to make it easy for customers to prepare and load data for analytics. With it, users can create and run an ETL job in the AWS Management Console. Users point AWS Glue to data stored on AWS, and AWS Glue discovers data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, data is immediately searchable, queryable, and available for ETL.

per DPU-Hour

IBM Cognos is a full-featured business intelligence suite by IBM, designed for larger deployments. It comprises Query Studio, Reporting Studio, Analysis Studio and Event Studio, and Cognos Administration along with tools for Microsoft Office integration, full-text search, and dashboards.

On Demand - Standard

On Demand - Premium

IBM Cognos Analytics

AWS Glue

Data preparation tools are a new class of software products designed to enable business analysts and data scientists to bypass data warehouses to perform some data integration and data preparation themselves before analysis.

Data Preparation

Information Technology & Services

Likelihood to Recommend

Usability

Support Rating

Amazon strives to be Earth's most customer-centric company where people can find and discover virtually anything they want to buy online. By giving customers more of what they want - low prices, vast selection, and convenience - Amazon continues to grow and evolve as a world-class e-commerce platform.

Founded by Jeff Bezos, the Amazon.com website started in 1995 as a place to buy books because of the unique customer experience the Web could offer book lovers. Bezos believed that only the Internet could offer customers the convenience of browsing a selection of millions of book titles in a single sitting. During the first 30 days of business, Amazon fulfilled orders for customers in 50 states and 45 countries - all shipped from his Seattle-area garage.

Amazon's evolution from Web site to e-commerce partner to development platform is driven by the spirit of innovation that is part of the company's DNA. The world's brightest technology minds come to Amazon.com to research and develop technology that improves the lives of shoppers and sellers around the world.

Apache Hadoop

Informatica Intelligent Cloud Integration Services

Informatica PowerCenter

Amazon EMR (Elastic MapReduce)

Amazon Relational Database Service (RDS)

Amazon RDS on VMware

AWS Glue 2024-12-26 07:28:14

Apache Spark

AWS Cloud9

Azure App Service

Aspire AWS Cloud Services and Solutions

Azure Analysis Services

Aspire, duplicate

AWS Glue 2023-10-17 21:07:30

IBM Netezza Performance Server

Talend Open Studio

Informatica Data Prep

AWS Glue 2023-07-25 07:04:51

Alexa Internet (discontinued)

Amazon SageMaker

Amazon Lex

AWS Data Pipeline

Practice Head - Labs (Innovation and R&D)

AWS Glue 2022-07-01 08:29:29

Alteryx Platform

Denodo

Qlik Replicate

AWS Glue 2022-09-08 10:08:33

TrustRadius is a technology and business research firm based in Austin, Texas. The company is known as a review platform for verified B2B technology and software reviews through a proprietary algorithm and human verification.

TrustRadius

Non developers

Development tools

Command line interface

Available online

Online resources

Extensive data

Data set

Easy to use

Command line

Home

Data Preparation Tools

We have certain transformations needs, we use Glue to fulfil them. As its integrated service from AWS itself, the connectivity to other AWS service is pretty seamless. As it’s fully serverless, we don’t have to worry about the infrastructure as well. It can crawl the things for us so we don’t have worry about the updation in our source, we got to know itself. So it’s powerful ETL FOR US.

Scale up and scale down easily
Seamless connectivity with other AWS services
Cost effective as you need to pay what you are using.

It’s integration with other cloud vendors is bit difficult
If it can support non SQL based databases as well, it would be powerful.
Real time data synchronisation in data source is missing

We are using GLUE for our ETL purpose. it’s ease with other our AWS services makes our ROI, 100% ROI.
One missing piece was compatibility with other data source for which we found a work around and made our data source as S3 only, so our dependencies on other data source is also reducing

Informatica Intelligent Cloud Integration Services and Informatica PowerCenter

Apache Hadoop, Amazon EMR (Elastic MapReduce), Amazon RDS on VMware

The main concern in AWS Glue is to so much costing of Glue jobs and I was worked with 5000 dataset I was facing some performance issue as compare to cost they need to work on performance aand also reduced our time utilisation to save time using this method. AWS Glue integrates with services like Amazon Redshift, Amazon Athena, and Amazon QuickSight, enabling organizations to analyze data with their preferred analytics tools.

Data integration
Data transformation
Job scheduling

Complexity transformation
Debugging and monitoring
Custom connectors

Etl automation
Data transformation
Job scheduling

Reduced the time and effort
Improving data quality
Job scheduling

AWS Cloud9, Aspire AWS Cloud Services and Solutions, Azure Analysis Services, Azure App Service

We use AWS Glue to creat Etl pipelines for transforming and moving of data from different data sources like S3, snowflakes, postgres to Redshift and vice versa. Execution of spark jobs is really easy as it has auto generated code which establishes connections with source and target data bases securedly and helps in the cleansing of data like deduplication and performing validations on data. As it is Serverless it will automatically scale up and scale down the memory resources required to run the spark glue job.

Execution of spark jobs
Scaling of memory resources
Crawling the schemas

Incremental data sync
Real time data triggers
Grouping of small files

Extract
Transform
Load
Spark jobs
Crawling
Connections

Easy to use
Lesser training required
Detailed documentation available hence implementation is easy

Talend Open Studio, Informatica Data Prep, IBM Netezza Performance Server

We use AWS Glue for ETL of the healthcare data. The input data come from different source systems and so with different formats. With help of the AWS Glue jobs, we translate the data into a common format. With help of python scripts and the scheduled job feature, the data is fetched in a periodic manner, processed with help of the python script, converted to the parquet format, and stored in the S3 bucket. The glue catalog generates the schema of the stored data and allows AWS Athena to query the same for analytics purposes.

It is extremely fast, easy, and self-intuitive. Though it is a suite of services, it requires pretty less time to get control over it.
As it is a managed service, one need not take care of a lot of underlying details. The identification of data schema, code generation, customization, and orchestration of the different job components allows the developers to focus on the core business problem without worrying about infrastructure issues.
It is a pay-as-you-go service. So, there is no need to provide any capacity in advance. So, it makes scheduling much easier.

The sample code should cover more scenarios. They are quite basic. However, you can find good pointers from the internet and AWS community and tickets.
AWS Glue runs on Apache Spark. So, to take the best of the AWS Glue service, the developer should have a good idea of Apache Spark.

AWS Glue Data catalog to write the efficient queries.
AWS Glue Crawler for the automatic schema recognition.
AWS Glue schedule job to perform certain ETL tasks on the defined interval.

We were transforming the data using a simple python script and were facing a lot of orchestration issues. The failure of the script was quite prominent as the nature of the data was a bit more dynamic. With help of AWS glue, we could fix ~80% of orchestration issues. With help of automatic schema generation, dynamism is also addressed very well. So, we have started realising the ROI from day 1.

The automation of numerous tasks, including logging, alerting, monitoring, etc., is made possible by AWS Glue. Additionally, it is economical because you only pay for the resources you actually use. One of AWS Glue's most notable features that are helpful is that it aids in the generation and transformation of data in its data catalog.

Helps in data creation and transformations.
Automation of the data schema recognition.
Support and scheduling of the data schema.

Integration with systems outside of the AWS environment.
Glue runs on spark so the engineer should be aware of the language.

It is costly.
Implementation is time consuming.
Data creation transformation is smooth.

AWS Glue

Overview

What is AWS Glue?