AWS Glue Reviews

In my company, we use AWS Glue to build data engineering pipelines, so we ingest data from either S3 or other sources and put it back into Redshift, where we have a data lake or data warehouse.

With AWS Glue, there have been a lot of updates over the past couple of years. The feature which I like the most is that AWS Glue has now integrated Jupyter Notebook into it, causing AWS Glue to become interactive and develop new use cases. The fact that we don't have to worry about the resources required for any particular job since it gets taken care of by AWS Glue adds to the valuable feature set of the product.

AWS Glue Studio has undergone a lot of enhancements in the last couple of months. An improvement that can help the solution is if the user interface can become more user-friendly and allow for features like drag and drop, allowing it to build transformations. There can be a good improvement if the product itself supports different kinds of transformations so that the pipeline, which we want to create, can be done easily since right now, we have to write a code to do so in our company. Only people who can code, either in Java or Python, can use the product freely. Those who don't know Java or Python might find using AWS Glue difficult.

AWS has pricing for spot instances that reduces the cost substantially, but that is not available for AWS Glue AWS pricing for spot instances comes for products like EC2, and if the same gets introduced for AWS Glue, then the pricing can substantially reduce.

I have been using AWS Glue for five years. I am an end user of the product.

Stability-wise, I rate the solution an eight out of ten.

It is a very scalable solution. In our company, we don't have to bother about the latest features implemented by the solution since the solution itself allocates resources, causing the product to scale up or down as the job progresses.

The solution's technical support is good. Whenever we raise a use case where we face an issue in our company, we get a response from the solution's technical team.

AWS Glue is a platform as a service from AWS, so there is no setup required. It's very simple since you can just go into the UI and start using it.

The solution is deployed on a private cloud.

I am a part of the development team, which doesn't have access to all the different sets of features and has a different backup. There is a team that does the initial setup, user access, and all those things. Once the product is ready, my team takes it over, so the setup is done by someone else.

I rate the product's pricing a five on a scale of one to ten, where one is a high price, and ten is a low price.

AWS Glue can be compared to the database provided by Azure which has a lot of extra features or capabilities compared to AWS Glue which is only useful for processing.

I rate the overall product an eight 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. 

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 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.