Databricks Data Intelligence Platform vs. Dell PowerScale

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

EMC Isilon Scale-Out NAS is well suited for larger files (greater the 128 Kb) and where you need to have everything in one common name space. Where it is less appropriate is for many small files (millions of files less than 128 Kb in size) - this causes the protection level to becoming mirroring, which will cost more space.

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

• NAS Storage
• Capacity and Performance details of the array
• Alert and Operations details

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 upgrades require the entire cluster to be rebooted simultaneously. In this day and age, that should not be necessary. This is my biggest disappointment with Isilon to date.
• When using multiple storage pools you have to be very careful with your capacity management. Filling up one pool can cause an overflow of data to a pool that is less performance driven. Do not underestimate your capacities or you will find yourself in a tight spot.
• Block size is almost always an issue with Isilon. It does not handle all types of data well. In many cases PACS and VNA data is best to be stored on a different storage platform that will utilize the capacity more efficiently that Isilon is capable of.
• Deduplication seems to be less efficient on Isilon than on other platforms for similar types of data.

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.

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

Raw disk space vs. logical disk space ratio was significantly better on the Isilon. Fast cache using SSD drives for faster searching is available on the Isilon but not available on the Overland solution. Isilon solution included faster backend switching between nodes.

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

• Isilon provides very high value for money
• Matches the performance and storage requirements of the arrays
• EMC provides very skillful and high response team in case of any issues in isilon

Incentivized