What is your primary use case for AWS Glue?

I use AWS Glue primarily for ETL jobs. In my organization, it's just me using it as we are a small company. The IT team consists of four people, and I am the data engineering specialist.

We use AWS Glue for handling data-intensive tasks such as data lake creation, log analysis, machine learning pipelines, data warehouse population for analytics, and real-time data integration with AWS Lambda.

I have been working as a data engineer, where dealing with the ETL process is essential. We are using AWS Glue as a primary ETL tool to serve our organization's needs. I have implemented several Glue jobs still in production.

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.

We have a lot of microservices written in Glue, which are responsible for triggering based on certain events. The solution will be responsible for another container to containerize them and run over the cloud. We use the solution for different purposes, including data computing.

I use the solution in my company for building datalake and for a variety of data sources like Oracle, MongoDB, and other multiple data sources, like SQL server, and AWS S3 buckets as a datalake storage tool, and then further we use AWS Glue to process it and move to AWS' search engine which will be like a lakehouse solution.

We are implementing a solution in AWS for one of our customers. It is more of a data analytics solution. We wanted to process data from different sources and put it into a central repository that can be used for any analysis or predictive modeling.

We use the solution to build tables on CSV data. We get data from some different sources, pull it in S3, and then create tables using Glue to get some metrics out of that data.

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

We use AWS Glue for ETL batch processing purposes.

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.

We use AWS Glue for data analytics.

In my company, we use AWS Glue to build data engineering pipelines, so we ingest data from either S3 or other sources and put it back into Redshift, where we have a data lake or data warehouse.

I had the source data, which was unstructured and non-fixable, and my responsibility was to convert it into structured data. For this task, I used PySpark as the programming language. With Python, I implemented the creation of a data frame using Glue jobs. Since Glue jobs are a serverless mechanism, I deployed my code into the Glue job, and that's how I got the job done.

I constructed a straightforward ETL job using AWS Glue, wherein I had to load a couple of files in the Teradata database.

Our primary use cases include pulling data from multiple sources and loading it into the central capacity for data transformation, integration, and processing.

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

We're using GPU 0.2 in ten verticals and wanted to use AWS Glue only for one purpose: to optimize Amazon Redshift. We have millions of data that we have to back up. Previously, we did it once every six months, but the client data have been very interactive, and we need spontaneous back and forth of data communication in real-time. In one second, we have almost one million records that come and go continuously. The client wanted to keep all data because they're using it for analytics and wanted to back up the data every second without delay. We tried to optimize Amazon Redshift and found out about AWS Glue, which comes with massive costs, but the client is willing to pay.

Our primary use case is ETL.

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.

We are primarily using it for batch crossing and transformations.

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.

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.

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.

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

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.

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.

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.

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

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.

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

We are collecting some TV audience data and analyzing it.