Databricks Data Intelligence Platform vs. Nasuni

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

Well suited if you have a lot of data that doesn't need to be stored and read right away. I think even if you don't have much data, you can still use it for it's intended purpose to great effect, but think of it as the more data you have, the even better it will work. I don't think it would be particularly useful if you already have a slick file restore system in place and you don't need to store your data elsewhere with redundancy.

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

• The management console is extremely simple and easy to navigate, making common tasks easy to do.
• Our storage appliance is configured to snapshot data several times an hour, making the risk of data loss very low.
• Data restores are very intuitive, and take seconds to initiate regardless of whether it is one file or 300GB of data. We have successfully restored many Gigs of data in minutes.

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

• Better analytics when issues arise.
• Better onboarding documentation.
• Real-time monitoring.

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

As I mentioned, the user interface is amazing and straight forward. It's very easy to learn how to configure and restore files. I would like a bit more reporting, especially in terms of live reporting and monitoring. The support is great when you have a question on how to do something, which helps with usability.

Incentivized

Again, it may have a little to do with the size and speed of your own environment, but we've been nothing but pleased with the speed of access of the files - even pulling old files from the cloud storage. Recovery of huge and many data files is a bit slow if you don't have the specs of the filer up to snuff.

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.

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

The technical support and escalation path for Nasuni is much more reliable and efficient. No getting transferred to various teams. Often times, the person who answers your call is able to resolve your issue. If they cannot, they get the case assigned to the appropriate engineer right away. Time to close has always been very good.

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

• Dramatically reduced time spent managing our storage platform. Quotas and reporting tools take all the guesswork out of data growth. Updates are easy to deploy. Time freed up can be used for more user-facing activities that we consider more valuable to the organization.
• The overall stability of the platform has been very good. We have been running on the same hardware for the past four years without any performance issues.

Incentivized