AWS Glue Reviews

We use AWS Glue for data analytics.

AWS Glue is a stable and easy-to-use solution.

The solution’s stability could be improved.

I have been using AWS Glue for the last three years.

I rate AWS Glue a seven out of ten for stability.

AWS Glue is a very scalable solution, and you can connect multiple databases.

AWS Glue's technical support is very good.

AWS Glue is not a licensed solution. AWS Glue follows a pay-as-you-go model, wherein the cost of the data you use will be counted as a monthly bill.

Currently, there are many ETL tools in the marketplace. Compared to other ETL tools, AWS Glue is a low-cost and serverless solution.

Overall, I rate AWS Glue a nine out of ten.

We're using GPU 0.2 in ten verticals and wanted to use AWS Glue only for one purpose: to optimize Amazon Redshift. 

We have millions of data that we have to back up. Previously, we did it once every six months, but the client data have been very interactive, and we need spontaneous back and forth of data communication in real-time. In one second, we have almost one million records that come and go continuously. The client wanted to keep all data because they're using it for analytics and wanted to back up the data every second without delay. We tried to optimize Amazon Redshift and found out about AWS Glue, which comes with massive costs, but the client is willing to pay.

What I like best about AWS Glue is its real-time data backup feature. Last week, there was a production push, and what used to take almost ten days even to send out around fifty-six thousand emails now takes only two hours.

I also like that the data backup in AWS Glue is spontaneous, and data is recorded and backed up every single moment.

AWS Glue had some issues, which required optimization, particularly in terms of the number of workers you deploy, and that's where costing comes in. Cost-wise, AWS Glue is expensive, so that's an area for improvement. My company did some modifications, which turned out to be successful, so overall, the solution works fine.

Even though there is a backup, you need to know what's happening. You need to understand why there's a failure. AWS Glue doesn't provide the information, so my company uses its logs. The development team also doesn't have specific answers because the team is still playing around with the process, which means the company is still trying to figure out other areas for improvement in AWS Glue.

The process for setting up the solution was also complex, which is another area for improvement.

AWS should provide help during migration and assist its users. Otherwise, it's a nightmare.

I've been using AWS Glue for one and a half months.

AWS Glue is stable, but stability depends on how many workers you deploy and the work that you do.

AWS Glue is highly scalable. It can scale to almost one billion data per second.

We did make some good friends in AWS, so they gave us technical support for AWS Glue for free. They were also new and were trying to evolve, so they provided us with free support, but they'll be charging other clients for the support moving forward.

The setup for AWS Glue is highly complex. The company started with R&D four months ago and only completed the deployment last week.

My company used one and a half FTE resources for the deployment.

The deployment process for AWS Glue was normal and involved CI/CD, but it was mainly the backend dev ops engineers who did it. I'm more of a project manager, so I'm not involved in technical items. It's more of me helping the engineers with the R&D.

AWS Glue is a high-priced solution that bills the client $150,000 to $250,000 annually. That's just the starting price because it's a small data sample, but if it hits over three hundred million users, the cost will probably go up almost thirty times more.

I'm using the latest version of AWS Glue.

I'm not the end-user, as I work for a company that implements AWS Glue for clients.

My company has one client using AWS Glue, but that client has three hundred million users.

I recommend AWS Glue to others because it's an excellent solution. However, it lacks documentation. There's only a little documentation available. Even certified AWS practitioners struggle with the lack of documentation for AWS Glue. You'll find complicated processes or features, such as time series tables. Even if there's documentation, implementing the solution requires many trial and error methods, and revamping becomes a nightmare if you're using the old infrastructure.

My rating for AWS Glue is seven out of ten because of the complexity of the deployment, and the lack of information and documentation, that my company had to do some R&D. If AWS had complete documentation, or sent more than one person to assist my company, then it could have saved more time.

We use the solution to do the usual type of transformations that before required ETL. It's mostly transformation-type purposes that we have, including transforming data from source to target. Also, we are replacing the usual ETLs with Glue, for example.

The solution has helped the organization mostly with migration. When we migrate from mostly on-premises to the cloud, Glue replaces a more expensive item that's also available in the cloud. Lots of programmers can understand Glue because they can write scripts in Python and PySpark, and there are quite a few programmers that know how to program in those languages. With the previous components that did similar things on-premise, you needed specialized knowledge.

The fact that we can use PySpark to program them is great. 

It's fairly straightforward as a product; it's not very complicated. 

The fact that Amazon offers it and it's a quick way of getting things done, and the fact that it doesn't require a lot of training is very positive attributes.

The initial setup is straightforward.  

It can scale.

The mapping area and the use of the data catalog from Glue could be better. I would say those two are the main things we'd like to see improvements on.

The solution needs support for big data.

As I understand it, Glue is based on Lambdas and Lambdas have some limitations as far as running them continuously. Sometimes they get dropped, and they have to be reinitialized.

I've been using the solution for about two years. 

So far, the solution has been stable. For everything that we've done, we didn't run into any problems, so it was fairly stable for us.

The solution can scale. I don't know exactly at what scale. I was using it for a medium-scale type solution. We never tested it with extremely large scales, so I don't know how expansive it can be. That said, since it's in the cloud, if you need more scale, it would be easy enough to add additional Glue components. At least, in theory, it should be fairly scalable.

Indirectly, we have hundreds of people on the solution. 

At this time, I'm not sure if any clients plan to increase usage. 

Since the solution is so easy to use, we have not needed the help of technical support. 

We've previously used many different solutions, including Informatica, Data Storage from IBM, and SSIS.

The solution is easy to set up. It's not overly complex.

At a minimum, a company would need one to two people to handle the deployment. If it is a larger scale of deployment, they might need more personnel. 

As a consultant, I assist with the implementation. 

Likely, a company would receive an ROI as it's cheaper than the alternatives.

I was not involved in the cost negotiation process. 

Our clients typically have chosen Glue from the start. I was not involved in the evaluation process. 

We are using one of the latest versions of the solution. It's about two years old. 

Depending on the number of data sources, the variety of data sources, and the variety of targets they will have, I might recommend the solution. What they have and plan to do will dictate whether Glue is a good solution or whether they would require something more sophisticated - such as Databricks. For example, if you have big data, then Databricks is probably a better solution to do ETL.

I'd rate the solution seven out of ten. 

Currently, we are utilizing AWS Glue for various ETL workloads, specifically in the life sciences domain. Our primary objective is to acquire data from various sources. Then, we store it in Redshift. This is where the complete use case of AWS Glue comes into the picture.

The solution is highly user-friendly, and its features are easy to use. The new addition of AWS Glue Data Catalog is also very beneficial, making the tool even more helpful for its users.

The solution could be cheaper. The price of the solution is an area that needs improvement.

I have been using AWS Glue in my organization for a year. I am an end-user and a customer of the solution.

It is a stable solution. We have not faced any issues in the past year, so it's pretty stable. Stability-wise, I rate it a ten out of ten.

The solution has proven to be scalable, and from my experience in the data engineering domain, I rate it an eight out of ten. It is worth noting that I may not be the most qualified person to provide a rating since I mostly manage and work on data-related tasks. Currently, approximately 20-25 people in our company use the solution.

I had no experience with the technical support team of AWS Glue.

Previously, I used Azure Data Factory. But I did not find it really helpful. And it was a bit complex. It was not that user-friendly. And I am much more comfortable with the AWS services as compared to Azure services.

The initial setup of the solution is straightforward, and I find it easy to implement. I rate the setup process a nine on a scale of one to ten, where ten is the easiest. As for the deployment process, we usually request our platform team to handle it, and they are quite efficient in deploying and managing the infrastructure. Although I am not directly involved in the deployment process, my understanding is that it can be completed in just a few hours with the help of two to three team members. Our platform team consists of data engineers, architects, and platform engineers who cater to the needs of various projects and products within the AWS ecosystem. Fortunately, the solution does not require any maintenance.

Price-wise, the solution is adequate, and we have no issues with it. We believe that the cost is justified given the number of users and the features it provides. Overall, it can be considered an average-priced tool. 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.

Before choosing AWS Glue, I evaluated Azure Data Factory.

I would tell those planning to use AWS Glue to try it. I rate the overall solution a ten out of ten.

I have been working as a data engineer, where dealing with the ETL process is essential. We are using AWS Glue as a primary ETL tool to serve our organization's needs. I have implemented several Glue jobs still in production.

AWS Glue has enabled us to perform ETL processes efficiently, with ease of use for AWS cloud users, providing a serverless service that eliminates the need for infrastructure maintenance.

The two features I find most valuable in AWS Glue are its user interface and ease of use. The user interface is intuitive, and navigating through the Glue console is seamless. 

Additionally, its ability to integrate with other AWS services is excellent, providing flawless coordination with services such as SNS, S3, and Lambda.

I see scope for improvement in the drag-and-drop feature of AWS Glue. Beginners need additional support as it currently lacks some features required for complex transformations, often necessitating custom Python coding.

I have been using Glue for more than five years now.

Overall, the stability of AWS Glue is excellent. I would rate it a nine out of ten. Some network-related issues may arise. That said, they are rare and do not affect its functionality significantly.

Regarding scalability, AWS Glue is nearly perfect. I would rate it a nine out of ten, although there is always room for improvement.

AWS customer service is great, but there is room for improvement. The issue I face is the inconsistency in dealing with different customer service representatives for the same issue, which disrupts personal touch.

Neutral

On an organizational level, the pricing of AWS Glue does not pose a concern. It is in line with other ETL tools in the market. However, AWS Glue's cost to free-tier users is an issue because it is not entirely free, even for trial purposes.

I advise potential users to adopt AWS Glue primarily due to its user-friendly interface, extensive documentation, and seamless integration with other AWS services, making it ideal for data engineers.

I'd rate the solution nine out of ten.

We use AWS Glue for ETL batch processing purposes.

The product has a valuable feature for data catalog.

The product is expensive for data streaming compared to EMR. This area needs improvement.

We have been using AWS Glue for one and a half years.

I rate the product's stability a ten out of ten.

We have five to six AWS Glue users. I rate its scalability a nine out of ten.

We have used Cloudera before. We switched to AWS Glue for better pricing, scalability, and innovation.

The initial setup is easy. I rate the process an eight or nine out of ten. It could be deployed on-premises and on the cloud as well. We have a team of five executives to carry out the implementation.

It is an expensive product. I rate its pricing a nine out of ten.

I rate AWS Glue a nine out of ten.

One common use case is migrating data from one system to another.  So, mostly migrating data and data engineering, getting real-time or near-real-time data using Lambda functions and migrating big data from on-prem to the cloud for historical data before starting a project.

If you have the Fund Manager, you could use a fast processing engine, which is crucial for performance. 

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.

One area that could be improved is the ETL view. The drag-and-drop interface is not as user-friendly as some other ETL tools. 

Additionally, AWS Glue can sometimes be slow, especially when processing large datasets. It was sometimes a bit slow. Also, I couldn't directly use bucketed data. With Elastic Glue, you had to convert your data frames into the correct format before connecting them using the drag-and-drop interface. So that's something I didn't like because the conversion process wasn't straightforward. 

In future releases, I would like to see a feature that could trigger Glue pipeline using an API or something. 

I have experience with AWS Glue. I have about one year of experience in a professional setting, but I have also done some personal work with this solution.

Support was good, but I was working with a big client, so that might have influenced the experience. The response time was fast, we heard back from them within a day. 

Positive

I would rate my experience with the initial setup an eight out of ten, where one is difficult and ten is easy. 

The initial setup is not very complex. You can customize parameters like minimum and maximum for your needs. For me, it wasn't complex to deploy the solution. 

I'd rate it around six out of ten compared to other tools like Databricks.  

My colleagues work with Spark, PySpark, and Scala as programming languages for writing complex aggregations. They have a repository in order to have a general view of all the sources and jobs on the platform and AWS Glue is very helpful.

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

The interface for AWS Glue could improve, they do not put a lot of details. You can write the code, in PySpark or in Scala, which is a big advantage, it is only easy to use for a developer. It will be difficult for new users to enter the cloud environment.

If business users want to run their own graphs they will not have the opportunity to use such features, such as running code inside AWS Glue in Spark, which will be complex for them.

 I have been using AWS Glue for approximately four years.

AWS Glue is a highly stable solution. We didn't have bugs in production. 

The solution works well with Spark, which is a good framework for large volumes of data. It operates very well.

I rate the stability of AWS Glue a ten out of ten.

The scalability of AWS Glue is great. It was used for enterprise customers. We worked a lot with AWS Glue for International companies.

We have approximately 10 people using AWS Glue in my company.

I have to use the support from AWS Glue. The response time could improve.

I rate the support from AWS Glue a nine out of ten.

Positive

The initial setup of AWS Glue is very simple.

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.

If I can compare AWS Glue to other solutions, it has the advantage of the cloud, which assures availability and scalability, and the pay-as-you-go is beneficial. This is why many companies are moving from their traditional ETL tools to the cloud because the costs will be reduced dramatically.

I would recommend this solution to others.

I rate AWS Glue a nine out of ten.