Likelihood to Recommend
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.
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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.
Read full review Pros Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner. Apache Spark does a fairly good job implementing machine learning models for larger data sets. Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use. Read full review 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. Read full review Cons Memory management. Very weak on that. PySpark not as robust as scala with spark. spark master HA is needed. Not as HA as it should be. Locality should not be a necessity, but does help improvement. But would prefer no locality Read full review 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. Read full review Likelihood to Renew
Capacity of computing data in cluster and fast speed.
Read full review Usability
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Good API, multi-protocol support is great.
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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.
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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.
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All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like
. Combining it with Jupyter Notebooks (
), one can develop the Spark code in an interactive manner in Scala or Python
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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.
Read full review Return on Investment Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark. Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy. Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs. Read full review 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. Read full review ScreenShots