Cloudera Data Science Workbench vs. Databricks Data Intelligence Platform

• If you already have a Cloudera partnership and a cluster, having this is a no brainer.
• It integrates well with your existing ecosystem and it immediately starts working on projects, accessing full datasets and share analysis and results.
• With the inclusion of Kubernetes, CPU and memory across worker nodes can be managed effectively.

Incentivized

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

• Enterprise grade security.
• Self-service analytics platform.
• Popular programming support.

Incentivized

• There is databricks community, which is a free version. It is available for beginners to have an easy start with a big data platform. It does not have every feature of the full version but is still adequate for extremely new coders.
• There are many resourceful training elements that are available to developers, data scientists, data engineers and other IT professionals to learn Apache Spark.

Incentivized

• Not as great as RStudio; lacks some features when compared with it
• It is quite simple still (because its very early in its initiative), and companies may want to wait until they see a more developed product

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

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 is expensive and difficult to install and maintain.

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.

Since our organization had already implemented Cloudera Data Platform as our Big Data Warehouse platform, implementing CDSW as the go-to Analytic and Data Science Platform is the most logical and cost-effective decision to make. It integrates seamlessly with our CDH clusters and it also provides enterprise-grade security for on-premise implementation.

Incentivized

Databricks is a true all-in-one platform, and at the time of implementation, it had more features available to us, making it a clear choice over Snowflake. Moving our workloads from local computing to the servers in Databricks gave our start-up staff a great quality of life boost.

Incentivized

• Paid off for demonstration purposes.

Incentivized

• ROI for us has been tremendous. Time to market by processing raw data in our big data infrastructure has been pretty fast.
• Non engineers can easily use Databricks, hence helping business customers.
• Thousands of different data combinations can easily be joined and used by our data teams.

Incentivized