AWS Glue Reviews

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. 

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.  

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.

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.

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.