AWS Glue Reviews

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. 

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.  

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.

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'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.

Our company has five data engineers who use the solution for metadata catalogs and ETL pipelines that are built in S3 or EC2.  

The solution integrates well with other AWS products or services. 

The solution needs to expand its 30-minute query or runtime. Sometimes it fails with certain data types such as Athena due to the limited runtime. Some large data sets run overtime during busy hours so we try to avoid failures by running data at idle times or at night. 

On occasion, the solution's dashboard reports that a project failed due to runtime but it actually succeeded. This can be quite confusing. 

I have been using the solution for one year. 

The solution is stable 99% of the time but is somewhat dependent on workloads. Heavy workloads or bigger data sets sometimes fail. Improvements are needed for stability to be consistent. 

I have not needed technical support but my colleagues have used it. I do not have relevant feedback to report. 

The setup requires some knowledge and understanding of how the solution works. 

We implemented the solution in-house. 

I rate pricing an eight out of ten. 

There are other options that offer equivalent service such as SRA and GCP but the solution is fully integrated with AWS where our data resides so we take full advantage of that. 

I rate the solution a five out of ten. 

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.

We used AWS Glue to build our data warehouse. We built prototypes to go all the way all across their warehouse platforms. From AWS Glue to Spreadsheets and then QuickSight, that's how we're building their warehouse.

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

It would be better if it were more user-friendly. The interesting thing we found is that it was a little strange at the beginning. The way Glue works is not very straightforward. After trying different things, for example, we used just the console to create jobs. Then we realized that things were not working as expected. After researching and learning more, we realized that even though the console creates the script for the ETL processes, you need to modify or write your own script in Spark to do everything you want it to do.

For example, we are pulling data from our source database and our application database, which is in Aurora. From there, we are doing the ETL to transform the data and write the results into Redshift. But what was surprising is that it's almost like whatever you want to do, you can do it with Glue because you have the option to put together your own script. Even though there are many functionalities and many connections, you have the opportunity to write your own queries to do whatever transformations you need to do.

It's a little deceiving that some options are supposed to work in a certain way when you set them up in the console, but then they are not exactly working the right way or not as expected. It would be better if they provided more examples and more documentation on options.

I have been using AWS Glue since last year.

On a scale from one to ten, I would give AWS Glue a nine.

We are implementing a solution in AWS for one of our customers. It is more of a data analytics solution. We wanted to process data from different sources and put it into a central repository that can be used for any analysis or predictive modeling.

The most valuable feature of AWS Glue is scalability.

Since AWS Glue is not like an enterprise ETL tool, we need to put quite a lot of effort into customization. The solution has a visual editor, but most ETL transformations cannot be implemented or constructed using that. We always have to do a script. The solution's visual ETL tool is of no use for actual implementation.

I have been using AWS Glue for two years.

I rate AWS Glue an eight out of ten for stability.

Most of our clients for AWS Glue are enterprise businesses.

I rate AWS Glue ten out of ten for scalability.

Nobody from AWS technical support has implementation experience.

Neutral

On a scale from one to ten, where one is difficult and ten is easy, I rate the solution's configuration a five and its implementation a two or three out of ten.

The solution's pricing is pay-as-you-go. If you are using the solution for an enterprise business, it will be expensive.

AWS Glue is a cloud-based solution.

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

I constructed a straightforward ETL job using AWS Glue, wherein I had to load a couple of files in the Teradata database.

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

While working on AWS Glue, I could not find any training material for it. Although it's not a problem with the product, the solution could include better documentation.

I have been using AWS Glue for about two months.

AWS Glue is a stable solution.

AWS Glue's initial setup is quite straightforward.

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

We use the solution to collect customers' data containing multiple files and convert it into a common database. Later, we send the database for SQL injection.

The solution's most valuable feature is its ability to efficiently collect and catalog the data in the warehouse.

They should improve the solution's performance in case of large amounts of data. Currently, AWS fails to handle massive databases acquired from various sources. Also, it is challenging to queue the data or use a standard code in AWS environment. We need to install a third-party tool to tackle the issue. We need to use another tool to convert the data as well. Thus, we are using multiple tools to handle the database. They should work on this particular area.

We have been using the solution for one year.

It is a stable solution. I rate its stability as an eight.

I rate the solution's scalability as a six.

The initial setup is a bit complex, and I rate the process as a six. We have to install multiple third-party tools whenever we update the security patches or renew the solution. Thus, the deployment process is complicated.

If you already have AWS environment, you can opt for AWS Glue for its ETL operations feature; if you want to process multiple operations, such as creating a table or catalog, or for machine learning purposes better to go for other database tools.

I rate the solution as a seven.