AWS Glue vs. DbVisualizer

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

Seamlessly querying PostgreSQL/PostGIS, Oracle, SQL Server, and SQLite from one interface without switching tools. The intelligent autocomplete and schema browsing make it ideal for writing queries and understanding unfamiliar databases. Complex performance tuning, deep monitoring, and automated backup/recovery require specialized DBA tools. Performance degrades with tens or hundreds of thousands of rows; command-line tools or ETL solutions are better suited to bulk data processing.

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

• Out-of-the-box support for many different databases.
• Intuitive UI for interacting with data.
• Flexibility of SQL Commander for writing scripts.
• Great support for working with JSON data in recent updates.
• Database connections over SSH.
• Table relationship visualization is very helpful.
• The tree view is well organized in how it splits out schemas.

• 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

• Small, but noticeable: it would be nice to be able to highlight just one column of a data output for copy/paste by clicking on the column header (a feature some of us are accustomed to from SQL Server Management Studio)
• Is there an ability to bulk import an excel file to a table in a connected SQL Server in DbVis? If so, I haven't figured that out yet. I still largely use Microsoft SSMS to import data to our team's personal SQL Server, but can easily query that server via DbVis)
• (I really don't have any major complaints - our company has tried to steer users toward DBeaver for YEARS, which I cannot stand. DbVis knocks the beaver out of the water)

We never had problems with it. It is able to connect to all data sources (Oracle, Informix, DB2, SQL Server) that we currently use.

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 does what we need it for, which is manipulate data, check connections, visualize database structures, check on user rights, or even just version checks on the used databases. The 'type to get to table name' feature is often irrational in behaviour, which leads to frustration. The start-up time could be much better, and the UI changes between versions are sometimes a hassle for us when buttons are suddenly moved or their appearance changes. We use a wide variety of DBVis versions on customer installations (which use the once-bought version), and differences in the look and feel between versions sometimes slow us down.

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

Never used

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.

[DbVisualizer] is pretty easy to use compared to IntelliJ because of it's simplicity. The performance is very good, it feels as good as a native application compared to the other two softwares I used for the same purpose. It's very cheaper compared to the other two tools and that's a big selling point.

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: Real time saver when working across multiple databases. At least 30-40 mins a day are saved
• Positive: Helpful for the 'quick DBA' tasks without switching to the native tools: approx 1-2 hours saved weekly
• Positive: Easy switch between environments / schemas / etc without hassle: saved at least an hour daily