Databricks Lakehouse Platform vs. IBM Netezza Performance Server

If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.

Incentivized

We can query the data source and treat multiple databases
as one with IBM Netezza Performance Server.



While delivering fast and reliable analytical performance, the IBM Netezza Performance Server requires minimal configuration and ongoing
management.



To drive organizational performance, Netezza Performance
Server automatically simplifies data and AI to centralize all analytics
activities on the device, exactly where the data resides.



For data processing and application dashboards, IBM Netezza
Performance Server is quite beneficial.





IBM Netezza Performance Server simplifies event setup
by notifying you when a hardware component fails, allowing you to quickly
replace it.

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

• IBM Netezza Performance Server enables capabilities to integrate with leading ETL solutions.
• Data can be exported from the IBM Netezza Performance Server to a variety of formats, including Excel.
• We can prioritize specific users and queries using IBM Netezza Performance Server.

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

• More tech support may make it easier as it is only limited to be done via company representatives
• Overhead cost for replacements and services looks like it is high compared to others
• Integration with other cloud based application will be great

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

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.

Compared to Synapse & Snowflake, Databricks provides a much better development experience, and deeper configuration capabilities. It works out-of-the-box but still allows you intricate customisation of the environment. I find Databricks very flexible and resilient at the same time while Synapse and Snowflake feel more limited in terms of configuration and connectivity to external tools.

Netezza is sufficient against similar products. It comes down to personal preference, I'd love to have the data objects popping up as I type but some people may not like it.

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

• The appliance is all in one so we have all licenses and support contracts under one roof.
• The cost is expensive so ROI may take time to be realized.
• If done correctly, then Netezza provides the opportunity for huge ROI.

Incentivized