AWS Glue Reviews

I use AWS Glue for data processing. Some of my colleagues have data for software, and I use AWS Glue to transform and inspect this data.

It's very good to manage. It is easy to integrate other products with AWS. 

Glue integrates with other AWS processes and networks. So, it's quite easy to integrate.

I've worked with AI integration but I haven't gone into much depth on that topic.

AWS Glue's error handling is difficult. 

The errors in AWS are very hard to handle. The screen is very hard to understand. 

I have to use CloudWatch, but whatever our error was, the new ones, and so on. I would test this with someone. It's not so easy for me, and there are more things related to this.

I have been using it for a year and a half. 

I would rate the stability a nine out of ten. 

I would rate the scalability a seven out of ten. 

My data is small, so we need to consider more days. We need to deal with what we have, but I understand the documentation. 

Some people find it hard, but I rated it a seven. In my company, TechOps uses AWS with about 1,200 users.

I worked with Databricks. In my opinion, Databricks is improving and is easier to use. It's more user-friendly, and I think it's better overall.

I work with a big company, and most of it is already quickly done, like using something that is a blueprint. This configuration stuff is already working in another place. The only thing I have to do with the cloud is the remote configuration.

AWS can be expensive.

Overall, I would rate it a seven out of ten. I would recommend it.

I use AWS Glue mostly for data ingestion or data extraction from multiple sources.

AWS Glue plays a central role in AI-based solutions and machine learning workflows by efficiently handling and moving data around, contributing to streamlined operations.

One aspect that I would like to highlight is the Glue Crawler, which we utilize when working with large datasets to ensure the schema updates seamlessly without requiring end-team knowledge. 

Additionally, the pipeline orchestration and scheduling of ETL pipelines in AWS Glue are also highly effective. AWS Glue supports AI-driven projects and DAG transformations by facilitating efficient data handling required for machine learning workflows.

The point for improvement in AWS Glue would be the dynamic allocation of resources while utilizing Lambda functions. Currently, Lambda functions encounter a time-out error after fifteen minutes, necessitating an improvement in this area. Moreover, more practical examples and resources for advanced features would be beneficial for users.

With AWS Glue, I have one year of experience.

In terms of stability, there are areas that could be improved, particularly with Lambda functions and step functions. There should be better scaling for parallel computation. Despite this, AWS's overall stability is quite good.

AWS Glue receives a nine out of ten for scalability, although we feel certain functions should scale better for enhanced parallel computation.

I would rate the technical support for AWS Glue as ten out of ten. The support is high-quality.

Positive

The initial setup of AWS Glue could be straightforward for new users of AWS services, but there should be more practical examples and guides for advanced features to assist newcomers in learning complex concepts.

Currently, I work on the implementation within our development team, focusing on cloud services.

I find the pricing for AWS Glue quite affordable. For students or new users, AWS offers free credits, and as usage increases, the pay-as-you-go model provides flexibility without being expensive.

I would recommend AWS Glue to others and rate the overall solution nine out of ten.

We use AWS Glue for handling data-intensive tasks such as data lake creation, log analysis, machine learning pipelines, data warehouse population for analytics, and real-time data integration with AWS Lambda.

AWS Glue has increased efficiency and time saving. It simplifies and automates data pipeline processes, enabling faster data processing and analysis.

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.

It enables us to analyze data stored in Amazon S3 using SQL, which is manageable and cost-effective.

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

I have been using AWS Glue for more than one year.

AWS Glue is generally considered stable and reliable for data integration, especially for larger scale production environments. Its serverless architecture and integration with other AWS services contribute to its stability.

AWS Glue is scalable because of its serverless nature, which allows for easy scaling without needing to manage any infrastructure.

The technical support from AWS is very reliable. I would rate it nine out of ten.

Positive

I did not use any other cloud data integration solutions before AWS Glue.

The initial setup process for AWS Glue involved setting up Glue resources, creating roles and permissions, and developing ETL scripts and jobs. It took about half an hour.

The deployment process required three developers.

While specific data on time and cost savings was not provided, AWS Glue's benefits include increased efficiency and time-saving.

The approximate cost for ETL jobs is about 0.44 USD, which is mostly covered by the company. Employees do not purchase AWS Glue solutions individually.

AWS Glue is highly recommended for data engineers due to its ability to build and maintain data pipelines, ensure data quality and integrity, and its integration with UI tools. It offers data preparation, machine learning integration, and governance.

I would rate it a ten out of ten.

In my role as the global lead for AWS solutions and offerings, we work with various clients, including large-scale clients, to adopt and implement AWS cloud offerings. 

Our primary focus revolves around cloud lift-and-shift migration, modernization, re-platforming, rehosting, data architecture, design strategy, and implementing generative AI-specific solutions across different industries such as banking, capital insurance, energy utilities, manufacturing, automotive, semiconductor, and aerospace and defense. 

For example, we have implemented AWS Glue at several client locations, utilizing its serverless data integration capabilities during the data discovery process, enterprise transformation, cleansing, transforming, and centralizing data.

AWS Glue has significantly improved our data quality, enhancing the data by removing duplicates and providing timely and efficient insights. 

It also aids in real-time data processing, reducing effort and cost due to its serverless architecture. These features ensure we maintain the highest level of scalability, reliability, and security compliance.

AWS Glue is fully managed, providing an easy-to-use integration environment to create, run, and monitor ETL jobs. It's broadly compatible and seamlessly integrates with other AWS services like Amazon S3, Redshift, and Athena. It's flexible with data integration, manages various data formats (JSON, ORC, CSV, etc.), and is serverless, eliminating the need for infrastructure management.

Its ease of use, cost-effectiveness, and highly secure architecture are some of the most valuable features.

There could be an enhanced way of managing pure metadata management or data cataloging. 

Additionally, while it covers a wide range of integrations with AWS services, integrating with certain additional or legacy products is not seamless and can be complex. 

Increasing support for more programming languages and improving advanced analytics capabilities could also be beneficial.

We have been working with AWS Glue for almost three-plus years now.

We haven't faced any stability issues with AWS Glue. It is a scalable solution, provided that the right design principles and workload management are implemented.

AWS Glue is a scalable solution due to its serverless architecture and efficient design.

My team handles interactions with AWS for technical support, ensuring our design architectures are scalable, flexible, and well-integrated. We often reach out to the AWS team to double-check our implementation mechanisms and guidelines.

Positive

The initial setup of AWS Glue is straightforward due to its serverless architecture and fully managed nature. Specific prerequisites need to be followed, such as setting up data sources, configuring IAM permissions, creating crawlers, and running ETL jobs.

My team escalates technical questions to AWS support, ensuring our design architectures are optimal. We have a partnership with AWS, and the technical team frequently reaches out to AWS for guidance on scalability, flexibility, and integration mechanisms.

We have seen an efficient process with AWS Glue, providing the right return on investment at the right time. It ensures efficiency for our clients, giving them the desired ROI within their expected timelines.

Follow the right design principles and involve AWS at the right time to leverage the most current features and offerings from AWS Glue. Ensuring the right architecture will mitigate any issues. I'd rate the solution eight out of ten.

I had the source data, which was unstructured and non-fixable, and my responsibility was to convert it into structured data. For this task, I used PySpark as the programming language. With Python, I implemented the creation of a data frame using Glue jobs. Since Glue jobs are a serverless mechanism, I deployed my code into the Glue job, and that's how I got the job done.

We no longer had to worry much about infrastructure management because AWS Glue is serverless, and Amazon takes care of the underlying infrastructure. This allowed us to focus on the code and application logic without concerns about scaling, CPU management, or handling fluctuations in flow. The serverless nature of Glue jobs relieved us from these infrastructure-related worries.

The most valuable feature of AWS Glue for me is the ability to write PySpark code and execute it within Glue. I use Glue functions to publish parameters and interact with Glue. 

In terms of performance, if they can further optimize the execution time for serverless jobs, it would be a welcome improvement. Faster code execution would be beneficial. If AWS could enhance the serverless execution capabilities, like increasing CPU, RAM, and processing speed, that would be great.

I have been using AWS Glue for a year. 

In terms of stability, I haven't experienced any issues with AWS Glue. It has been quite reliable in my usage, and I would rate it around nine on a scale of ten.

AWS Glue is highly scalable and reliable. The serverless nature ensures that scaling is automatically managed by AWS. It's one of the strengths of Glue, and there is no doubt about its scalability.

The customer service and support were responsive and helpful in resolving the issues I had at that time. The response time was quick. So, overall, the support was okay.

Positive

Our admin team was responsible for setting up the solution. 

The pricing is a bit higher than EMR. EMR is a managed Hadoop and Spark platform. AWS Glue has a higher price. 

I found that AWS Lambda could be a possible alternative, but since AWS Lambda has a 15-minute execution limit, we opted for AWS Glue, which does not have any execution limits. I don't see any other service that can do what AWS Glue does. 

Other services that could potentially do the same job as AWS Glue would require your application to be deployed in Elastic Beanstalk, EC2, or Container Services, which is another task altogether. Since AWS Glue is already doing its job perfectly, I don't think any other service can do the same job as AWS Glue.

I would recommend that new users refer to the AWS documentation. The documentation is very well-written and easy to understand. Even new users with no prior experience with AWS should be able to get up and running quickly. I would also recommend that new users learn Python.

Overall, I would rate the solution a nine out of ten.

I use AWS Glue primarily for ETL jobs. In my organization, it's just me using it as we are a small company. The IT team consists of four people, and I am the data engineering specialist.

The best thing about AWS Glue is its scalability and how easy it is to process a large amount of data. It integrates well with Redshift, S3, and AWS Glue catalog. 

For processing extensive data, having a managed Spark service fulfills that role. If you're already working on AWS and you need to process a lot of data that can't be handled on a single node or server, AWS Glue will serve you well. While it's quite expensive, it's valuable for large data processing needs.

Setting up pipelines is challenging, especially with version control and testing requirements. While the initial setup is easy, it doesn't accommodate more complex development needs. You might feel hesitant about changing pipelines that are already running and processing business-critical data due to limited versioning and testing capabilities.

I've been using AWS Glue since 2022, so for two years.

The stability of AWS Glue is fine. I haven't had any problems with it.

The scalability of AWS Glue is commendable.

Previously, in different jobs, I have worked with Databricks for ETL processes. I've also utilized Lambda functions for handling smaller data. I didn’t switch to AWS Glue, but used it in a different context.

The initial setup of AWS Glue is easy, yet not adequate for more complex requirements. If you need to do something robust, like creating a notebook, it is straightforward. 

However, when dealing with complex pipelines handling critical business data, it's hard to set up versioning and testing.

AWS Glue receives a hesitant five out of ten from me. I recommend it if you're already on AWS and need to process large data sets. However, for smaller data volumes, I would suggest Airflow because AWS Glue can be quite expensive.

Our primary use cases include pulling data from multiple sources and loading it into the central capacity for data transformation, integration, and processing.

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

We face performance issues when using AWS Glue for data transformation and integration. It takes almost three to four hours to execute single transformations, which is a lot. We want to improve the performance to meet customer requirements.

Mainly, I am focused on improving the performance aspect because the customer is keen on this improvement.

I have been using AWS Glue for five years. I am using the latest version. 

I would rate the stability of AWS Glue a seven out of ten. There are some performance issues. 

I would rate the scalability of AWS Glue a six out of ten. There are over 25 users in my company. 

The customer service and support team is okay. 

Neutral

It is a cloud-based solution; there is no such installation procedure required. One DevOps is required for the maintenance of AWS Glue.  

Based on the customer scenario, I have previously recommended AWS Glue. Sometimes, customers directly request either Azure RapidAPI or AWS Glue. It depends on the specific business use case. Both tools have limitations, so it's hard to say which is best. If a customer already uses Microsoft products, I suggest going with Azure. As for a general rating, I would give AWS Glue a seven out of ten.

Overall, I would rate AWS Glue a seven out of ten because it's not about performance. It's because of how the tool is used. 

We have a lot of microservices written in Glue, which are responsible for triggering based on certain events. The solution will be responsible for another container to containerize them and run over the cloud. We use the solution for different purposes, including data computing.

You do not need many frameworks to run Glue. It's an interpreted language that does not require to be compiled at all.

It is very difficult to learn the tool and remember the syntaxes comparatively. Sometimes, I face issues integrating the solution with some third-party services or services that are not a part of Glue. Such integrations take a lot of time, and not much content is available over the internet for the same.

I have been using the solution for three to four years.

We have eight developers on our team. My team works on almost four to five Glue services. We have four team members working on Glue, including me.

I once faced an issue with Glue. There was a scenario where I wanted Glue to pick certain images, containerize them, and run over the code. That containerization integration wasn't happening successfully. I dropped a couple of messages in the community channel. I got good support from them, which helped me resolve my issue as quickly as possible. The community is very small, but the people are very helpful.

I previously worked in Java, .NET, and Python. I have extensive experience with Python and .NET. Since my organization is language-independent, we have microservices written in almost all the languages, including Glue, Python, Java, and .NET.

I'm not handling the solution's end-to-end deployment, but we have a CI/CD pipeline set up for that. The CI/CD pipeline will remain the same. It's all about how you containerize your Glue application. That is the only challenge we have faced while setting up the deployment. The rest of the configuration was pretty smooth.

Glue is not a must-have tool. You can choose Glue if you have the capability to learn Glue as quickly as possible. There are other alternatives where you will find a lot of articles, study material, and certificates over the internet apart from Glue. If you do not have any other option, go for Glue.

If Glue is not mandatory for you, go for something else because it is difficult to learn Glue and remember the syntaxes. You will need support whenever you have a bigger integration or connectivity with third-party libraries or services. You will not receive many articles or help over the internet. Although the community is available, you need to spend some time with them to make them understand the issue.

It is not easy for a beginner to learn to use the solution for the first time. There are a few videos and courses available, but it's difficult. It's not as easy as other languages in terms of content. It's hard, but you can use it once you understand the concept.

Overall, I rate the solution seven and a half out of ten.