Pure Storage offers FlashBlade, a scale-out file and object storage – architected to consolidate complex data silos (like backup appliances and data lakes) while accelerating tomorrow's discoveries and insights.
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
If data storage, access, and security [are] of the highest priority to your business then Pure Storage FlashBlade is an excellent tool that must be considered. Analytics or sharing that requires the fastest speeds available will benefit from the NVMe solid-state drives they use which are far superior to spinning rust. It is less ideal for those who do not require such time-critical work.
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.
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
1. It integrates very well with scala or python. 2. It's very easy to understand SQL interoperability. 3. Apache is way faster than the other competitive technologies. 4. The support from the Apache community is very huge for Spark. 5. Execution times are faster as compared to others. 6. There are a large number of forums available for Apache Spark. 7. The code availability for Apache Spark is simpler and easy to gain access to. 8. Many organizations use Apache Spark, so many solutions are available for existing applications.
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.
Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
The NetApp a800 we tested was 14% faster than Pure FlashBlade with NFS workloads. However, NetApp lacked ease of administration and performing simple tasks such as creating multiple NFS volumes required scripting from the command line. Our flashblade contained 15 baldes and our NetApp was a clustered pair with each half containing 24 nvme devices.
We were able to consolidate 5 different storage platforms of lesser performance onto a single Flashblade and achieve much, much lower latency and higher throughput.
We've been able to reduce the amount of training and configuration required to just Pure Flashblade, instead of 5 different vendors and products.
In addition to our core use cases, Flashblade has capabilities that we are pursuing for some new projects, i.e. analytics data store and the object store features.
