Databricks Data Intelligence Platform vs. Everpure FlashBlade

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

Pure Storage FlashBlade is best for companies that want their data stored and secure. The big storage capacity and cloud integration secure our data and ensure no third-party access without authorization. It provides us a secure data storage services for our objects. Fast and easy to install and easy to use.

Incentivized

• First, it handles large amounts of data. We run daily and weekly jobs that process a lot of records. Databricks manages it very well, with no issues, if the cluster is set up properly.
• Second, it really works well for incremental updates. We load only new or changed data, which makes it easy to update existing tables without duplicating records.
• Third, job scheduling is useful. We can schedule the jobs easily and monitor them. The best part is that we can retry or repair the failed runs.
• The last one is about the notebook interface that I really love. It makes development and debugging easy. We can test logic step by step, validate data, and fix all our issues.

Incentivized

• Speed. We are seeing large transactions take very little time.
• Upgrades- In-place upgrades of both hardware and software are extremely easy.
• Ease of use- I have several engineers working on this and from setup to day to day operations it is extremely easy to maintain.

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

• When reporting out a user has exceeded there quote, it only references the UID. It would certainly be nice it calls out the UID name that is clearly present in the Dashboard.
• The ability to determine a snapshot total size would be helpful.
• Proactive reachout to discuss new versions and assist in planning the upgrade would be a key win.

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

Good API, multi-protocol support is great.

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.

Without exception, the contacts with support have been quick and extremely knowledgeable. I do not fear getting an underqualified engineer to assess or work on my arrays. In addition to this support structure, the sales engineers are top notch as well.

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

Pure Storage FlashBlade is in our experience far faster, performance-wise, compared to its close competitors. However, if you factor in any packages you currently have with IBM or AWS for example, you can get more value for money for your data requirements. However, if performance is a must, the increased cost is not an issue.

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

• Pure has greatly narrowed the performance gap between file storage and fiber channel block storage with FlashBlade. Exotic file systems and costly FC fabrics are no longer needed to run performant storage.
• Training is not required for a storage admin who has never touched a Pure array. Everything is where you would expect to find it.

Incentivized