AWS Glue Reviews

The best feature is the price point. It's pretty cheap as compared to other tools like Informatica, et cetera. That's why major companies are moving to the cloud and using Glue. At least, that's what I found.

The solution is stable and reliable.

You can scale the product if you need to. 

The monitoring is not that good. We'd like to see job progress be more clear. Right now, how we can view that is not that good. The is that mostly it is Python or Scala code based. The UX is lacking.

There is a bit of a learning curve, particularly during the setup process. 

More connectors should be included.

I've been using the solution for three years. 

The solution is very reliable. It's stable. There are no bugs or glitches It works just fine. 

The solution can scale very well. It's not a problem.

Technical support is okay. We tend to go to the partner if we have issues, and they'll go to WS if they need to.

I'm also familiar with Informatica. However, Glue is less expensive. 

In terms of the initial setup, the learning part was a little bit stiff. After that, it is okay. We didn't have any issues once we understood the process. 

We didn't require any outside assistance such as integrators or consultants. We were able to handle it ourselves. 

The price is very good. It's enticing people to move to the cloud. 

That said, I do not have exact information on pricing. 

I'm an AWS engineer. My company is a gold partner.

I'd rate the product eight out of ten. So far, it's quite good. I don't have any complaints.  

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.

It is a good tool for us. All the implementation in our company is done with AWS Glue. We use it to execute all the ETL processes. We have collected more or less five terabytes of information from the internet by now. We process all this data in our cloud platform and normalize the information. We first put it on a data lake that we have here on the AWS tool. After that, we use AWS Glue to transform all the information collected around the internet and put the normalized information into a data warehouse.

It has improved the time to implement a new ETL process by 30%. We have also seen a big improvement in the data science area.

The facility to integrate with S3 and the possibility to use Jupyter Notebook inside the pipeline are the most valuable features.

The crucial problem with AWS Glue is that it only works with AWS. It is not an agnostic tool like Pentaho. In PowerCenter, we can install the forms from Google and other vendors, but in the case of AWS Glue, we can only use AWS.

I have been using this solution for two years.

In terms of stability, we had some problems in the past, but now, it is okay. AWS provides SLA, and the integration of the tools is good.

Scalability is a very strong point of this solution as compared to other solutions like PowerCenter and Pentaho. In Pentaho, you need to install a lot of machines, but in AWS Glue, you just need to find out how many instances do you need. You just put this information in a form and click okay. Magically, you have the scaled processes. 

We have 35 users of this solution, and they are engineers, DevOps, and data scientists. We have a lot of plans to increase the usage of AWS Glue in 2021.

In the first year of using it, we had a lot of problems with the solution. Our team found more or less five bugs if I remember correctly. Our experience with AWS support was very good. The team in the US helped us to resolve the problems and fix the bugs. We are AWS partners.

Before AWS Glue, we worked with Talend, PowerCenter, and Pentaho. In the case of PowerCenter, the biggest problem for us was the plugins because they were too expensive. That was the negative point of PowerCenter. 

In the case of Talend, the problem was that in Brazil, we didn't have professionals with the skills to work with Talend. In addition, we had to use the command-line interface, which was a terrible thing because it took more time as compared to other solutions.

In the case of Pentaho, we had the same problem as Talend. We didn't have a lot of professionals. Of course, we have some courses to train people in Pentaho. We work with the biggest companies in Brazil, and we need professionals every day, but we don't have professionals with experience in Pentaho.

The initial setup process is totally easy. You just need to put some information in the forms, and then you just need to click some buttons, and it is complete. The process to provide a new infrastructure with AWS Glue takes from 10 minutes to an hour.

We have all the professionals inside the company.

Its price is good. We pay as we go or based on the usage, which is a good thing for us because it is simple to forecast for the tool. It is also good in terms of the financial planning of the company, and it is a good way to estimate the cost. It is also simple for our clients.

In my opinion, it is one of the best tools in the market for ETL processes because of the fact that you pay as you use, which separates it from other big tools such as PowerCenter, Pentaho Data Integration, and Talend.

I would rate AWS Glue a seven out of ten.

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.

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.

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.  

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.

I primarily use AWS Glue as a lightweight ETL to migrate our existing on-prem workloads to a cloud environment without looking at a lot of migration paths. 

AWS Glue served the purpose of migrating our on-prem workloads to a cloud environment without involving a heavy load. It ensured that we were able to test every migrated component independently.

AWS Glue's best features are scalability and cloud-based features.

AWS Glue would be improved by making it easier to switch from single to multi-cloud.

I've been using AWS Glue for eighteen months.

I would rate AWS Glue's scalability eight out of ten, as it still has some room to improve.

AWS Glue is straightforward to implement, and you won't see any technical complexity if you're from a development background.

We also considered Airflow, but Glue was a better fit for our engineering-heavy workloads because we wanted a serverless option.

I would rate AWS Glue eight out of ten.