AWS Glue Reviews

Once you get the data and you don't know about the structure of the data, then Glue is very helpful to estimate the structure, including where is the structure, and it'll identify everything for you. It has one component that is called Glue Crawler that is quite useful for this task. It will go through segments of your data and try to guess their structure. It pops out the structure, and you can modify it according to your convenience.

It is good to basically perform the ETL when your files are stored in the S3 bucket. Glue supports other external sources also. That said, most of the time, we have basically given our proposal to clients if the data is available in S3.

The solution helps organizations gain flexibility in defining the structure of the data.

You can define and then include the original data structure and decide what the required fills are or what other ones you can omit. You can perform certain processing tasks also, and you can basically apply the multiplying factor; you can do the cleanup, et cetera, on the fly with the Glue.

The Glue Crawler can have a set of connectors, so you can utilize those connectors to connect with the external databases, which may be on-premise in different networks or maybe locally on AWS. Basically, you can use the connector to fetch the data. 

Once you have a data schema, you can start streaming or fetching the data in the particular format conversion. For example, suppose you have the text file, and you have Word in place or maybe in SQL, and you can use the connector on the fly to convert the database.

For batch processing, batch genetics, it is helpful for the ETL process.

The setup is easy. 

The solution offers a free trial. 

The solution can scale. 

It's stable.

Users only pay for what they use once they have a license. 

I haven't looked into Glue in terms of seeking out flaws. I've not come across missing features. 

I've been dealing with the solution for two or three years. I have given a lot of proposals based on customer demand.

The solution is quite stable and reliable. There are no bugs or glitches. It doesn't crash or freeze. It is reliable. 

Typically, data analytics individuals use the solution. It's not for an entire organization.

It's a scalable solution. I'd rate it ten out of ten. 

We do have plans to increase usage. We are in the process of moving many things to the cloud, and if they move onto AWS, they'll need Glue.

I've never been in touch with technical support. I can't speak to how helpful or responsive they are. 

The solution is very straightforward to set up and implement. 

I'd rate the ease of deployment at an eight or nine out of ten. However, it all depends on the circumstances. 

The deployment only takes two to three minutes. It's very fast.

Using the console, you have different sections of AWS Glue You can go and specify the input data source and output target data place. Then you need to specify the transformation. If you want to do the filtering, et cetera, you have to specify. You have the blueprint of transformation functions available also, and you can select from there and then just run it. 

I've only just explored the solution. It has not been deployed yet. 

When you are just learning and testing the solution, it is free. I cannot speak to the full cost beyond that, as I am just experimenting with the product. They do offer it to users for a limited time to try for free, however.

My understanding is you only pay for what you use, so pricing would vary based on that. You don't need to maintain a cluster and it is serverless. 

There are no extra costs beyond a standard license fee.

We are using the latest version of the solution. The solution runs on the cloud and is serverless. 

It's a good solution to use when people are not exporting analytics. If you want to perform some ETL on your data and the data is complex, then you should go for Glue. It is easy to set up.

I'd rate the solution ten out of 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. 

We are primarily using it for batch crossing and transformations.

We have a large set of data and we are doing some transformations and identification. We are cleaning the data and transformations. Then we are putting the data into the destination table. So it is very comfortable.

The most valuable features currently are glue studio, jobs, and triggers.

I would like to see stable libraries at the moment they are not there.

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

The stability I would consider to be an extensible Apache Spark.

The scalability is good and we have three hundred projects we are working with.

Previously, we used EMR, Informatica, Data Pipeline, and Azure Data Factory.

The initial setup is straightforward.

We did our deployment in-house with the CI/CD integrations like GitHub and deployed the code on Glue. 

We are seeing a very good return on our investment.

The current cost is around forty to fifty thousand a month.

I would definitely recommend using AWS Glue for batching procedures. I would rate AWS Glue an eight out of ten.

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. 

We are using AWS Glue for transforming firewalls synced to the Data Lake in the bronze zone. The ATL uses the solution to transform fields in the silver layer and later we will produce the gold zone. We are using the Delta Lake Architecture.

The most valuable feature of AWS Glue is its ease of use and good documentation. Additionally, we can do all the transformations that we need.

The price of the solution could improve.

I have been using AWS Glue for approximately one month.

The stability of AWS Glue is good.

AWS Glue is highly scalable.

There are dozens of customers using this solution.

I have not used the support from AWS Glue but I know their support is good.

I have previously used Azure and Spark for testing.

The initial setup of AWS Glue is simple. In other solutions, such as Spark, the configuration would take a lot longer.

I did the deployment of AWS Glue myself with the AMS console. I am a data engineer.

The overall cost of AWS Glue could be better. It cost approximately $1,000 a month. There is paid support available from AWS Glue.

If the cost of AWS Glue was 50 percent less then we would not move to another solution.

I am moving to the EMR serverless or GCP solution.

I rate AWS Glue a nine 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.

The best feature is the price point. It's pretty cheap as compared to other tools like Informatica, et cetera. That's why major companies are moving to the cloud and using Glue. At least, that's what I found.

The solution is stable and reliable.

You can scale the product if you need to. 

The monitoring is not that good. We'd like to see job progress be more clear. Right now, how we can view that is not that good. The is that mostly it is Python or Scala code based. The UX is lacking.

There is a bit of a learning curve, particularly during the setup process. 

More connectors should be included.

I've been using the solution for three years. 

The solution is very reliable. It's stable. There are no bugs or glitches It works just fine. 

The solution can scale very well. It's not a problem.

Technical support is okay. We tend to go to the partner if we have issues, and they'll go to WS if they need to.

I'm also familiar with Informatica. However, Glue is less expensive. 

In terms of the initial setup, the learning part was a little bit stiff. After that, it is okay. We didn't have any issues once we understood the process. 

We didn't require any outside assistance such as integrators or consultants. We were able to handle it ourselves. 

The price is very good. It's enticing people to move to the cloud. 

That said, I do not have exact information on pricing. 

I'm an AWS engineer. My company is a gold partner.

I'd rate the product eight out of ten. So far, it's quite good. I don't have any complaints.  

We used AWS Glue to build our data warehouse. We built prototypes to go all the way all across their warehouse platforms. From AWS Glue to Spreadsheets and then QuickSight, that's how we're building their warehouse.

I like that it's flexible, powerful, and allows you to write your own queries and scripts to get the needed transformations.

It would be better if it were more user-friendly. The interesting thing we found is that it was a little strange at the beginning. The way Glue works is not very straightforward. After trying different things, for example, we used just the console to create jobs. Then we realized that things were not working as expected. After researching and learning more, we realized that even though the console creates the script for the ETL processes, you need to modify or write your own script in Spark to do everything you want it to do.

For example, we are pulling data from our source database and our application database, which is in Aurora. From there, we are doing the ETL to transform the data and write the results into Redshift. But what was surprising is that it's almost like whatever you want to do, you can do it with Glue because you have the option to put together your own script. Even though there are many functionalities and many connections, you have the opportunity to write your own queries to do whatever transformations you need to do.

It's a little deceiving that some options are supposed to work in a certain way when you set them up in the console, but then they are not exactly working the right way or not as expected. It would be better if they provided more examples and more documentation on options.

I have been using AWS Glue since last year.

On a scale from one to ten, I would give AWS Glue a nine.

Glue is a NoSQL-based data ETL tool that has some advantages over IIS and ISAs. It is tailored and customized to use with SQL Server, which works very well in that platform.

If you want to use other data sources, the NoSQL concept makes it very easy, because missing data can be inserted as a new column or with null values.

That is not the case with many other tools. If you have on-premises tools, such as IIS, they don't manage missing data well.

If you want extremely high-performance functionality, you have to use both AWS Glue or Data Lake to store it in some temporary table. First, you will have to do some cleaning of the data, then if you need performance and speed, you have to use IIS with an IBM tool. 

You have to use the right tool in the right places. For example, if you're using Oracle, you have got to use the Oracle tools. If you are using SQL, you have to use the SQL tools. There is no other tool that provides the performance.

It's context-based and project-based. In the projects that I have used, it has worked well.

There is a learning curve to this tool.

I have been working with AWS Glue for four years.

Everything runs on AWS, even if it belongs to a third party. For example, if you have a Netflix subscription, it runs on AWS. We have other products or vendor subscriptions that run on AWS.

Undoubtedly, the cloud is built to handle failure. If you have your devices, and your resources configured correctly, you won't have any issues. I haven't seen a problem.

You have to pay for their technical support, and depending on which level of subscription, you will receive a call within an hour; otherwise, you will have to wait for days.

We also use Azure's Data Lake, and I worked with Tipco in the past, though it's been a few years since we used it.

You should select the best tool for the job or the projects that are currently being worked on. Tipco was heavily used in the previous project we worked on.

It takes some time to learn, but once you get the hang of it, you'll be fine. It's like any other IT tool, where nobody is an expert or isn't an expert, it is just the way you are exposed to a tool. 

You've chosen the right tool if you understand how the data works and what it needs to do. It's like going to Home Depot to get the right tool. You can purchase a set of tools, and it will work for you, but you will still need to purchase something else.

It's one of those tools in which someone must be an expert. After that, all tools and platforms become secondary.

With AWS Glue, you pay more, but if you want to process the data, with speed and performance, you need the correct EC2 instances.

There is a price to pay. It doesn't come free.

Technical support is a paid service, and which subscription you have is dependent on that. You must pay one of them, and it ranges from $15,000 to $25,000 per year. 

You sign up for a level of service, and it does not come for free. As previously stated, everything is based on performance, ELAs.

It was very expensive, at that time. If a company wants to pay the money, it makes my job easier. However, if the company or enterprise does not have the funds to pay for it, then it is a hassle.

In that environment, there is a lot going on. There are some things that you can get for free, and there are some add-ons that you can develop or use that have been tested. It's all about convenience and service. You will get what you pay for if you pay for what you want.

I'm not a fan of any tools; it all depends on the organization I work for, where their data is, what they want to do with it, how quickly they want to get there, and what their budget is, and you work around that. For me, I would not choose one over the other, unless I know the details of the project.

I would rate AWS Glue a nine 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.