AWS Glue Reviews

We are collecting some TV audience data and analyzing it.

Data catalog and triggers are the two best features for me. 

AWS Glue has its own data catalog, which makes it great and really easy to use. Triggers are also really good for scheduling the ETL process. 

The start-up time is really high right now. For instance, when you start up a new job, you have to wait for five or eight minutes before it starts. If the start-up time is reduced to one or two minutes, it will be great.

It will be better to have a direct linkage to Redshift in AWS. If we can use data catalogs from Redshift, it will be so easy to create some data catalogs. Currently, we can only use data catalogs from S3.

We have been using the AWS Glue for approximately one and a half years.

There is no problem related to stability.

Scalability is good. I can reduce or increase the number of DPUs, which I find very useful.

We are trying to increase the usage of AWS Glue because of customer needs. When the data increases, our application needs some more analyzers and user interfaces. We will increase our data analyzer and user interfaces.

I didn't take any technical support because I didn't have a big problem or issue. I just used some information from various communities and forums about the maintenance. 

The pricing is a bit higher than other solutions like Athena and EC2. If the pricing becomes more scaled or flexible, it will be good because you have to pay 44 cents just for one DPU for an hour. If you increase DPUs to 5 or 10, the pricing gets multiplied. 

There are also some time limits like 0 to 10 minutes or 10 to 20 minutes. If the pricing is according to the minutes, it would be better because you have to limit your job to 10 minutes or 20 minutes.

I would recommend AWS Glue. It is a great choice. 

I would rate this solution a nine out of ten.

AWS Glue is a versatile tool and we mostly use it for "lift and shift" server migrations.

I appreciate AWS Glue for its cost-effectiveness. The service provides a good balance between its capabilities and the cost associated with using it.

In terms of improvement, the performance of AWS Glue could be faster.

I have been using AWS Glue for five years.

It is a stable product.

It is fairly scalable.

The partner program support is very good.

The initial setup is not too complex. To deploy and maintain a data platform, a general data team of around four to five skilled individuals is typically required.

For AWS Glue, there is no separate license fee. It is part of the AWS service, and you pay for its usage as part of your overall AWS bill.

Overall, I would rate AWS Glue as an eight out of ten.

Currently, we are utilizing AWS Glue for various ETL workloads, specifically in the life sciences domain. Our primary objective is to acquire data from various sources. Then, we store it in Redshift. This is where the complete use case of AWS Glue comes into the picture.

The solution is highly user-friendly, and its features are easy to use. The new addition of AWS Glue Data Catalog is also very beneficial, making the tool even more helpful for its users.

The solution could be cheaper. The price of the solution is an area that needs improvement.

I have been using AWS Glue in my organization for a year. I am an end-user and a customer of the solution.

It is a stable solution. We have not faced any issues in the past year, so it's pretty stable. Stability-wise, I rate it a ten out of ten.

The solution has proven to be scalable, and from my experience in the data engineering domain, I rate it an eight out of ten. It is worth noting that I may not be the most qualified person to provide a rating since I mostly manage and work on data-related tasks. Currently, approximately 20-25 people in our company use the solution.

I had no experience with the technical support team of AWS Glue.

Previously, I used Azure Data Factory. But I did not find it really helpful. And it was a bit complex. It was not that user-friendly. And I am much more comfortable with the AWS services as compared to Azure services.

The initial setup of the solution is straightforward, and I find it easy to implement. I rate the setup process a nine on a scale of one to ten, where ten is the easiest. As for the deployment process, we usually request our platform team to handle it, and they are quite efficient in deploying and managing the infrastructure. Although I am not directly involved in the deployment process, my understanding is that it can be completed in just a few hours with the help of two to three team members. Our platform team consists of data engineers, architects, and platform engineers who cater to the needs of various projects and products within the AWS ecosystem. Fortunately, the solution does not require any maintenance.

Price-wise, the solution is adequate, and we have no issues with it. We believe that the cost is justified given the number of users and the features it provides. Overall, it can be considered an average-priced tool. I would rate the solution a six or seven on a scale of one to ten, with ten being very expensive. Specifically, I rate its pricing a six out of ten.

Before choosing AWS Glue, I evaluated Azure Data Factory.

I would tell those planning to use AWS Glue to try it. I rate the overall solution a ten out of ten.

The primary use cases of AWS Glue in our organization are for implementing ETL processes and for data flow.

The most valuable feature for me is the visual interface of AWS Glue. It is user-friendly and it is not complicated. Moreover, the coding part of AWS Glue allows users to upload their scripts after dropping some components. The product has flexibility and scalability, which is common in most cloud tools.

The product has only a few built-in transformations; additional custom-building transformations could be improved in the next release.

For additional features, I would like documentation on the equivalent of legacy ETL tools and their equivalent in AWS to make it easier for users to migrate their ETL processing to the cloud. It would save time and help users find the best transformation or solution to satisfy their new business needs.

I have been using this solution for three months, and I am using the latest version.

The stability is good; I have not faced any crashes so far.

I would rate its scalability a seven out of ten.

I used a product called SysTrack. For me, it was just a switch from SysTrack to AWS Glue.

The pricing depends on the usage, such as the number of users, computers, and the time jobs run.

Overall, I would rate this product a seven out of ten. It is a good product, but I have not experienced all the additional features.

We are using it for day-to-day ETL jobs. It is being used to transfer data from Teradata to the cloud.

We are using its latest version.

It is AWS-integrated. There is end-to-end integration with the other AWS services. It is also user-friendly.

There should be more connectors for different databases.

I have been using this solution for almost a year.

It is stable.

It is scalable. We have almost 40 users.

Their support is very good. I would rate them a five out of five.

Positive

We were not using any other solution previously.

It was straightforward. Within a couple of hours, it was done.

Before you start using it, you need to know PySpark.

I would rate it a nine out of ten. It is good for what we are using it for.

I mainly use AWS Glue for ETL purposes and batch processing of data.

AWS Glue's most valuable features are the data catalog, including crawlers and tables, and Glue Studio, which means you don't have to use custom code.

There are a couple of issues with AWS Glue. First, AWS Control randomly logs off, which disturbs coding. Second, if there's a cluster-related configuration, we have to make worker notes, which is quite a headache when processing a large amount of data. In the next release, AWS Glue should include more transformations with AWS Studio.

I've been using AWS Glue for around eight months.

AWS Glue is stable.

AWS' technical support responds within an hour on email.

The initial setup was very easy, with only some minimal configuration. However, there is a drawback that once we file the name of a user, it can't be changed.

AWS Glue is quite costly, especially for small organizations. The licensing fee is around $200 per year.

Glue supports Spark, so if you have a team that's good with Spark, definitely go with Glue. I would rate AWS Glue as eight out of ten.

The key role of Glue is that it hosts our metadata before rolling out our actual data. This is the major advantage of using this solution and our clients client have been very satisfied with it.

The most valuable aspect of this solution is its automation and ability to sync data from the source to the solution phase. 

The technical support for this solution could be improved. In future, we would like to connect more services like Athena or Kinesis to help control more loads of data.

I have been using this solution for three years. 

This is a stable solution. We have isolated the environment using containerization so that if anything goes wrong, we have higher levels of scalability and availability. To achieve this, we have configured multiple servers for testing, UAT and development.

This is a scalable solution which is supported in our organization by Docker and Kubernetes. We have 2,000 users.

We used a vendor with an internal IT team who provided us with architecture so that we could leverage those services and reach a solution. They have 50 people in the IT team, who continuously help us and monitor the things that we are working on.

Positive

The initial setup was straightforward and took approximately one month. For deployment, we worked in two teams. One person handled all the scripting which we are developing for automation. Two other members handled the database and servers.

This solution is affordable and there is an option to pay for the solution based on your usage. 

I would rate this solution a seven out of ten. 

We use the solution as a level of loading data from the source systems.

One of the best features of the solution is its ability to easily integrate with other AWS services. So, it's like we are using AWS as a main cloud provider. It's easy to put everything together. it is very flexible when it comes to compute features. We find the solution very useful when we make use of certain scripts. In some cases, it allows us to get rid of duplicates.

When there is a need to configure connections to different database sources in respect of the target, it would be good if it were easier to deal with roles. I am referring to the need to configure connections in a different target process, something which would require a certain time outlay for configuring VPC and checking that everything is okay, in respect of the creation of required roles. It would save time were this process to be made easier and more user friendly. 

The technical support depends on the type of question, whether there is a need to understand additional inter-related information on multiple levels. Overall, I consider the technical support to be fine, although the response time could be faster in certain cases. 

I have been using AWS Glue for about two years.

The solution is stable. 

While the technical support can vary with the type of question, I feel that, overall, it is okay, although receipt of information could be faster in certain cases. 

We previously had experience with Database Migration Service at AWS. I recommend it over AWS Glue if one needs to do full database migration from on-premises deployment or in cases involving large volumes of data.

I handled the installation on my own. 

I consider the the price to be standard-plus when it comes to optimal usage. 

I rate AWS Glue as an eight out of ten. 

We are using it for file ingestion. Its primary role is to ingest a file from a vendor to a database.

Its user interface is quite good. You just need to choose some options to create a job in AWS Glue. 

The code-generation feature is also useful. If you don't want to customize it and simply want to read a file and store the data in the database, it can generate the code for you.

Currently, it supports only two languages in the background: Python and Scala. From our customization point of view, it would be helpful if it can also support Java in the background.

I have been using AWS Glue for three months. We have just started using these services.

We have not been using AWS Glue for a long time. Till now, we haven't found any issues.

Their technical support is good. We faced an issue with AWS Glue where we had to read a flat file. In a flat file, you only have spaces. You don't have commas or anything else. AWS Glue does not directly support flat files. You need to provide it with an expression to read the file, and that expression itself has some limitation of characters. We contacted the AWS support team. They had a call with us and first tried to understand our problem and then our use case. We gave them some sample files for our use case, and they come up with a solution for this limitation. There are some custom patterns in AWS Glue that can be used. Even though they took some time, they provided the solution. If you give a file today, they will take three to four days to get back.

It was straightforward. A lot of documentation is available on the AWS website, which can guide you through the simple steps to set it up. Its setup was easy for me.

It is not expensive. AWS Glue works on the serverless architecture. We get charged for the time the server is up. For our use case, we have to use it once in a day, and it is not expensive for us.

We have just recently started to use this solution. We haven't used all features properly. It is good for the features we are using. We did not find any drawbacks or limitations so far. We are already getting whatever we want from it. 

I would rate AWS Glue a seven out of ten. It needs improvements in terms of Java support and the turnaround time for our problems. 

AWS Glue is essentially used for data engineering ETL jobs to extract, transform, and load data. We use it to clean data. You have multiple data sources from your application that are not so clean. You have this data and may want to delete certain columns or fill in certain data in an Excel sheet. That's where the extract part comes in. Then, you transform, drop, or make the data uniform and load it to your destination like a data warehouse.

The solution’s most valuable feature is the ETL job. AWS Glue is an easy-to-use solution. AWS Glue integrates seamlessly with other AWS services like Athena, Redshift, and S3.

The solution’s technical support could be improved.

I have been using AWS Glue for a few months.

AWS Glue is a stable solution.

I rate the solution’s stability eight and a half out of ten.

In the future, our data sets are going to increase. For now, the solution's scalability is fine.

I previously used Data Pipeline, and I tried using Lambda.

The solution’s initial setup is easy.

AWS Glue is built for large datasets, and it does the job perfectly. I would recommend the solution to other users.

Overall, I rate the solution eight and a half out of ten.