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The main concern in AWS Glue is to so much costing of Glue jobs and I was worked with 5000 dataset I was facing some performance issue as …
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What is AWS Glue?
AWS Glue is a managed extract, transform, and load (ETL) service designed to make it easy for customers to prepare and load data for analytics. With it, users can create and run an ETL job in the AWS Management Console.…
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Engineer in Information Technology
Curitics health (Health, Wellness and Fitness, 201-500 employees)
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per DPU-Hour
$0.44
Cloud
billed per second, 1 minute minimum
Entry-level set up fee?
- No setup fee
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- Free Trial
- Free/Freemium Version
- Premium Consulting/Integration Services
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- Tech Details
What is AWS Glue?
AWS Glue Technical Details
| Deployment Types | Software as a Service (SaaS), Cloud, or Web-Based |
|---|---|
| Operating Systems | Unspecified |
| Mobile Application | No |
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October 23, 2023
Software developer
Score 7 out of 10
Vetted Review
Verified User
The main concern in AWS Glue is to so much costing of Glue jobs and I was worked with 5000 dataset I was facing some performance issue as compare to cost they need to work on performance aand also reduced our time utilisation to save time using this method. AWS Glue integrates with services like Amazon Redshift, Amazon Athena, and Amazon QuickSight, enabling organizations to analyze data with their preferred analytics tools.
- Data integration
- Data transformation
- Job scheduling
- Complexity transformation
- Debugging and monitoring
- Custom connectors
- Etl automation
- Data transformation
- Job scheduling
- Reduced the time and effort
- Improving data quality
- Job scheduling
AWS Glue is a fully managed ETL service that automates many ETL tasks, making it easier to set AWS Glue simplifies ETL through a visual interface and automated code generation.
September 14, 2022
AWS Glue is a good data catalog and integration service
We heavily rely on AWS Glue for cataloging our data objects (tables and views). We use AWS Glue as our Data Catalog and use it in our data pipelines to sync external and internal data sources. We also utilize AWS Glue to auto-generate SQL-based ETL based on AWS Glue catalog objects.
- Create schemes, tables and views (data catalog).
- Sync external and internal data sources.
- Auto-generate SQL-based data pipelines, based on AWS Glue catalog objects.
- It is very difficult (almost impossible) to scale
- We sometimes get throttled by service limitations.
- AWS Glue crawlers sometimes mismatch the data in the files
- Data Catalog (schemas, tables, views)
- Crawlers
- It had a positive impact on the way we build our data lake.
- It is the single source of truth for data structure (schemas/tables/views).
AWS Glue is a managed service. It was easier for us to integrate it into our stack since we are already an AWS shop. It saved us the headache of managing a 3rd part service.
July 03, 2022
AWS Glue - The managed ETL service for your data
We use AWS Glue for ETL of the healthcare data. The input data come from different source systems and so with different formats. With help of the AWS Glue jobs, we translate the data into a common format. With help of python scripts and the scheduled job feature, the data is fetched in a periodic manner, processed with help of the python script, converted to the parquet format, and stored in the S3 bucket. The glue catalog generates the schema of the stored data and allows AWS Athena to query the same for analytics purposes.
- It is extremely fast, easy, and self-intuitive. Though it is a suite of services, it requires pretty less time to get control over it.
- As it is a managed service, one need not take care of a lot of underlying details. The identification of data schema, code generation, customization, and orchestration of the different job components allows the developers to focus on the core business problem without worrying about infrastructure issues.
- It is a pay-as-you-go service. So, there is no need to provide any capacity in advance. So, it makes scheduling much easier.
- The sample code should cover more scenarios. They are quite basic. However, you can find good pointers from the internet and AWS community and tickets.
- AWS Glue runs on Apache Spark. So, to take the best of the AWS Glue service, the developer should have a good idea of Apache Spark.
- AWS Glue Data catalog to write the efficient queries.
- AWS Glue Crawler for the automatic schema recognition.
- AWS Glue schedule job to perform certain ETL tasks on the defined interval.
- We were transforming the data using a simple python script and were facing a lot of orchestration issues. The failure of the script was quite prominent as the nature of the data was a bit more dynamic. With help of AWS glue, we could fix ~80% of orchestration issues. With help of automatic schema generation, dynamism is also addressed very well. So, we have started realising the ROI from day 1.
Glue comes in form of a managed service. However, the AWS Data Pipeline puts additional responsibility to manage the infrastructure. We were not requiring fine-grained control of the hardware which the AWS Data Pipeline provides. We also want to park our data on DynamoDB. AWS Glue allows storing the data to DynamoDB but the same is not possible with the AWS Data Pipeline. So, we decided to move ahead with AWS Glue.