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. Users point AWS Glue to data stored on AWS, and AWS Glue discovers data and stores the associated metadata (e.g. table definition and schema) in the AWS Glue Data Catalog. Once cataloged, data is immediately searchable, queryable, and available for ETL.
$0.44
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DataGrip
Score 9.6 out of 10
N/A
DataGrip, from JetBrains, is a database IDE that is tailored to suit the specific needs of professional SQL developers.
Glue is easier especially if you are already in AWS. It easily integrates to other AWS services. Compliments well with Amazon Athena, S3, and Lake Formation. Compared to Snowflake, it is also much much cheaper and you don't have to build outside AWS. Support is also good if you …
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.
AWS Glue is easier to use and has more and better features compared to it. And more documentation and tutorials and labs are widely available on the internet about AWS Glue which in turn helps in easier implementation of the spark jobs. Auto scaling is an added advantage. It's …
The main reason we choose AWS Glue over Talend open studio 1) Does not support Spark 2) Run only on java 3) not really feasible solution for heavy workloads 4) most of the cases need customer support 5) no proper documentation is available
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.
The cataloging of data objects is the best in the case of AWS Glue. We use AWS Glue in all of our data pipelines to sync external and internal data sources and to automatically produce SQL-based ETL based on AWS Glue catalog objects. Integration with Amazon products is the …
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 …
We are already in AWS services, so AWS glue is the first choice for us. But for the comparison of ETL job making and process time, it's way faster for other services.
I think it blows away mysql workbench hands down. Workbench does have more functionality when it comes managing the mysql instance, viewing performance etc. Navicat is ok, it might be better for new database develoopers. I stumbled upon DataGrip cause it came with jetbrains …
DataGrip provides a single UI for many DBMS platforms, instead of using one for each. Because of that, you can migrate things between platforms using the tool and "look across" all databases at once.
DataGrip is the most widely used software for simplified data management; we can know what is missing and what is leftover. The interface is straightforward and with a lot of security in its use of the system. All this has been its plus point.
DataGrip makes it easy to access and manage multiple databases locally and remotely, simplifies everything, and has powerful integrated features and resources that allow you to visualize and analyze data from a single platform.
When the data which requires ETL has different formats, schema, and volume, this service suits them best. So, when the volume is not consistent (typical use-case of healthcare and online shopping), AWS Glue can be the prime choice. When the data is available in both batch and streaming mode, the developer needs to generate a separate codebase. This increases the source code management efforts. So, prefer to go with Glue when the nature of the data is the same (either batched or streamed).
It is undoubtedly one of the best database management programs. It dramatically simplifies database management and administration. Its extensive support for various database engines is a point to highlight when we talk about DataGrip; you will like having such a powerful resource much as we do.
After data cleansing, the team also implemented the best practices for using AWS platform services as a Data Lake, such as job bookmarking for AWS Glue jobs, proper delimiter for the AWS Glue crawlers, partitioning in AWS S3, and transformation to parquet file for compression and faster querying time in Amazon Athena.
Data modernization through combining data from multiple sources into a functioning datasets, rebuilding DW, and resctructuring data sources.
Aims to lessen customer complaints, eliminate manual data extraction requests via SR from different data sources, and Increase accuracy, consistency and speed up reconciliation process.
Perfect other than a way to handle saving and re-using queries. A simple/better way of creating a pool of queries for each project or database connection would be very helpful. It is not bad now, just could be a better. I have used Navicat for MySQL in the past it had that feature. It could save all your queries to the cloud and you could use them on any device.
Amazon responds in good time once the ticket has been generated but needs to generate tickets frequent because very few sample codes are available, and it's not cover all the scenarios.
The cataloging of data objects is the best in the case of AWS Glue. We use AWS Glue in all of our data pipelines to sync external and internal data sources and to automatically produce SQL-based ETL based on AWS Glue catalog objects. Integration with Amazon products is the other advantage.
DataGrip is the most widely used software for simplified data management; we can know what is missing and what is leftover. The interface is straightforward and with a lot of security in its use of the system. All this has been its plus point.