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

• Data import/export and SQL Script generation are quick and simple to do.
• Fairly intuitive when viewing/editing table data.
• Ability to save SQL scripts per server and recall them easily.
• Handy intelligence when writing queries, functions, and stored procs.

Incentivized

• Connect my local code in Visual code to my Databricks Lakehouse Platform cluster so I can run the code on the cluster. The old databricks-connect approach has many bugs and is hard to set up. The new Databricks Lakehouse Platform extension on Visual Code, doesn't allow the developers to debug their code line by line (only we can run the code).
• Maybe have a specific Databricks Lakehouse Platform IDE that can be used by Databricks Lakehouse Platform users to develop locally.
• Visualization in MLFLOW experiment can be enhanced

Incentivized

• Some icons are kinda small and difficult to understand what they do
• The spanish translation could be better
• I think there are many functions that could be useful for me but I don't know what they do

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

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