Databricks Data Intelligence Platform vs. DBeaver

Medium to Large data throughput shops will benefit the most from Databricks Spark processing. Smaller use cases may find the barrier to entry a bit too high for casual use cases. Some of the overhead to kicking off a Spark compute job can actually lead to your workloads taking longer, but past a certain point the performance returns cannot be beat.

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

• Process raw data in One Lake (S3) env to relational tables and views
• Share notebooks with our business analysts so that they can use the queries and generate value out of the data
• Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
• Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers

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.

• Sometimes, when multiple jobs depend on each other in different environments, it is not always easy to see the full workflow in one place.
• It is sometimes difficult to determine which job or cluster contributes more to the overall cost.
• For beginners, cluster configuration may be a little difficult. So more recommendation in the platform can help.

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

Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX

Incentivized

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

Incentivized

One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.

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

The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.

Incentivized

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

• The ability to spin up a BIG Data platform with little infrastructure overhead allows us to focus on business value not admin
• DB has the ability to terminate/time out instances which helps manage cost.
• The ability to quickly access typical hard to build data scenarios easily is a strength.

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