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. 

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.

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.

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 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.