AWS Glue vs. DBeaver

One of AWS Glue's most notable features that aid in the creation and transformation of data is its data catalog. Support, scheduling, and the automation of the data schema recognition make it superior to its competitors aside from that. It also integrates perfectly with other AWS tools. The main restriction may be integrated with systems outside of the AWS environment. It functions flawlessly with the current AWS services but not with other goods. Another potential restriction that comes to mind is that glue operates on a spark, which means the engineer needs to be conversant in the language.

Incentivized

If you are connecting to Snowflake and want to query from your laptop, I find that this is much easier to use than Snowflake's IDE. It allows us as a business intelligence team to more easily connect to our servers, and code with much less hassle. It would be less appropriate if you are only on an on-premises SQL server, in that case, I would just use SSMS.

Incentivized

• 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.

Incentivized

• Identify types of DB connections by color code: development, testing and production.
• Predictive typing, it is incredibly accurate and saves me a lot of time.
• Jump between different simultaneous DB connections.
• Navigate through the schema object tree.
• Provide all the tools I need in one window.

• In-Stream schema registries feature people can not use this more efficiently
• in Connections feature they can add more connectors as well
• The crucial problem with AWS Glue is that it only works with AWS.

Incentivized

• Schema editing is not very intuitive. Editing a single column forces you into multiple tab windows when trying to change something simple like a column name.
• Sorting and filtering in data is nice, but buried in long right-click menus.
• Some things are definitely non-standard UI for a Windows application, so it might be hard for die-hard Windows fans to get used to.

Incentivized

While easy to set up and manage monitoring for large datasets, its complexity can be a barrier for new users. Integration with AWS Ecosystem, Managed Monitoring, Dashboards and monitoring tools for AWS Glue are generally easy to set up and maintain, Automated Data Pipelines. Automates data pipeline creation, making it efficient for certain data integration

Incentivized

It's perfectly easy to use. Not super new and shiny with lots of accessibility tailored tools, but does the job

Incentivized

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.

Incentivized

Not a lot of users have DBeaver so fewer resources are available online to help you if you have any issues. When I was trying to figure out how to create my own ER diagrams, it was a little tough to find resources

Incentivized

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.

MySQL workbench from MySQL only supports MySQL databases and it only provides basic functionality. On top of that, the user experience could be quite confusing for first-time users. SSMS from SQL server doesn't support inline editing nicely. The view for inline editing and view data is different, making it uncomfortable to use. All in all, DBeaver is the best tool when you manage a lot of databases with different types.

Incentivized

• We are using GLUE for our ETL purpose. it’s ease with other our AWS services makes our ROI, 100% ROI.
• One missing piece was compatibility with other data source for which we found a work around and made our data source as S3 only, so our dependencies on other data source is also reducing

Incentivized

• Positive Ability to manage DBeaver and query ad-hoc quickly
• By having the queries saved it has saved developer time and thereby increased the ROI on the investment on the tool
• This has made firefighting quicker and freed up resources for more development work.

Incentivized