AWS Glue Reviews

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. 

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.

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.  

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.

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.

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.

Our primary use case is ETL.

We have found it beneficial when moving data from one source to another.

The most valuable feature In terms of convenience, the drag-and-drop is really nice. The no-code interface, is really nice, being able to drag in my connectors. And then the nice thing, as well, is that it generates the framework, the wireframe of your code, so then you can just input whatever Spark or Python you want to input to make any further transformations.

I would like to see in general, documentation, on the limitations on which loads you can actually pull in when you are running Python. The additional Python Jupyter Notebook now has been nice. But yeah, generally speaking, you can not import every LOB. You can import branders now and you can use photos, but you can not import a lot of the other sorts of statistical-based loads. That is an issue currently. I would like to see a more robust interface on the no-code side. This would be nice to be able to split cells.

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

The stability is excellent.

There is good scalability you can set up your minimum and maximum users and you are ready to implement.

The initial setup is straightforward If you are just doing a file format conversion, then it is very simple, but if you want to do a little bit more robust sort of transformations, like inserting transformations or you want to do transformations on multiple delimiters, then there is a bit of learning curve. The deployment time is literally minutes.

I would rate AWS Glue a seven on a scale of one to ten.