AWS Glue Reviews

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.

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 uses the solution for ETL data movement for our customers such as on-premises to cloud, cloud to cloud, and cloud to Snowflake. We also data catalog and schedule ETL jobs. We are able to monitor all jobs through AWS services. 

Transformations are valuable because you can modify or override complex data logic from an open source or Spark to solve issues. 

For example, it is easy to solve issues where volume is good but performance is degrading because you can split jobs into small chunks to more quickly handle data loads. 

The setup and installation is a bit complex without advanced knowledge or training. It would be easier for an AWS expert or someone in DevOps.

Transformations need improvements to be more user friendly and rely less on coding like Matillion. 

I have been using the solution for three years. 

The solution's stability is decent and rates higher than other products. It works well with Snowflake, Azure, GCP, and AWS-supported products. 

A hybrid situation may cause delays in performance. 

The solution is scalable. 

One of our customers used technical support and found them to be helpful. 

The setup and installation is a bit complex. Training or advance knowledge is required. Someone with AWS experience or a DevOps perspective would have fewer issues. 

We install the solution for customers and the timeline depends on the job. 

A complete project will take a few days to a week for deployment. The number of jobs and components determines how many technicians are required for setup, installation, and deployment. Technician requirements can range from two to fifteen. 

Deployment will take a couple of hours for a few announcement jobs that deploy from the CI/CD pipeline.

The solution is my second choice because I prefer Snowflake's capabilities. 

Our company is creating data warehousing in the cloud. Our team includes four data engineers, two data ops, and two data administrators. 

We use S3 to data lake or prepare data from two databases that are contained in MySQL and Oracle. For the migration, we use DMS.

Then, we use the solution to perform data transformation. For Oracle, we use Data Catalog and Data Crawler to create our catalog. Dev Endpoint is used to develop complex data transformations. We then migrate to Studio Notebook where we develop and schedule a complex Spark job. 

Finally, we load the transformed data to Redshift so our data analyst team can visualize it with QuickSight. 

The solution is serverless so it allows us to transform data while optimizing the cost and performance of Spark jobs. 

The solution works with many data sources and services in the cloud. 

Glue Watch monitors our Spark jobs and immediately alerts us to issues so we are able to resolve them quickly. 

The solution does not work with Spark DataFrame. We can use the solution's DynamicFrame for this function but transformations are expensive. 

Not enough resources or services are available to run managed Spark jobs within the solution. We have reached out to Amazon many times regarding this issue. 

The solution should offer features for streaming data in addition to batching data. We can use other products such as Scala or Python but prefer the features be available in the solution. 

I have been using the solution for one year. 

The solution is stable with no issues. 

The solution is scalable. 

Technical support has been good and has handled any issues. 

I rate technical support an eight out of ten. 

Positive

The solution is the best service in its category at this time. Based on project budget and use case, we use either the solution or EMR.

EMR is used for projects that require the latest version of Spark. 

We use the solution for any other versions of Spark. 

I was not involved in the initial setup.

The solution's pricing is based on DPUs so it is a good idea to optimize use or it can get expensive. 

I use Studio Notebook because it is less expensive and jobs can be deleted or clustered to run in one day. 

I rate pricing a four out of ten. 

Our company only uses Amazon cloud because other cloud environments do not offer the same features. 

The solution's Studio uses GCP which is easier than coding in Python Spark or Scala Spark. 

Azure Data Factory's features do not compare to what the solution can do in the cloud. 

The solution is good for teams who do not want to worry about DevOps or who want to optimize cost by using the cloud. 

I rate the solution a seven 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.

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.

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.

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.