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.
Read full review Splunk is excellent when all your data is in one location. Its ability to correlate all that data is intuitive (once the hurdle of learning the query language is overcome). It is also easy to standardize the presentation of information to the company. When data is siloed/standalone, other systems can be cheaper and faster to implement.
Read full review Pros Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issues Faster in execution times compare to Hadoop and PIG Latin Easy SQL interface to the same data set for people who are comfortable to explore data in a declarative manner Interoperability between SQL and Scala / Python style of munging data Read full review This SIEM consolidates multiple data points and offers several features and benefits, creating custom dashboards and managing alert workflows. Splunk Cloud provides a simple way to have a central monitoring and security solution. Though it does not have a huge learning curve, you should spend some time learning the basics. Splunk Cloud enables me to create and schedule statistical reports on network use for Management. 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 The SPL programming language that the queries are built in is not very intuitive. There should be a better repository of pre-built queries for what I would think of as common Active Directory usage monitoring. I would like to see more free training/familiarization information made available. Read full review Likelihood to Renew Capacity of computing data in cluster and fast speed.
Steven Li Senior Software Developer (Consultant)
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.
Read full review Overall, it is very usable. I would like if recent searches were saved for longer because I always have to refer to my notes when I'm looking for something specific and it's been a few weeks. But that's a small issue, and the actual search and browsing interface is easy to use and powerful.
Read full review Support Rating 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.
Read full review Splunk Cloud support is sorely lacking unfortunately. The portal where you submit tickets is not very good and is lacking polish. Tickets are left for days without any updates and when chased it is only sometimes you get a reply back. I get the feeling the support team are very understaffed and have far too much going on. From what I know, Splunk is aware of this and seem to be trying to remedy it.
Read full review Alternatives Considered 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.
Read full review Splunk Cloud blows
Sumo Logic out of the water. The experience is night and day. We went from several highly stressed IT security professionals who were unsure if the data they were getting was valuable, to very happy IT security professionals who can now be more proactive and get all the information they need.
Read full review Return on Investment Business leaders are able to take data driven decisions Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available Business is able come up with new product ideas Read full review End-end visibility across your departmental silos Strengthen the overall global monitoring posture Move from Reactive to Proactive Monitoring Highly secure environment at your finger-tips Takes you away from managing infrastructure/administration, allows saving time & money. Reduce the overall TCO (Total Cost of Ownership) Read full review ScreenShots