153 Reviews and Ratings
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The software appears to run more efficiently than other big data tools, such as Hadoop. Given that, Apache Spark is well-suited for querying and trying to make sense of very, very large data sets. The software offers many advanced machine learning and econometrics tools, although these tools are used only partially because very large data sets require too much time when the data sets get too large. The software is not well-suited for projects that are not big data in size. The graphics and analytical output are subpar compared to other tools. Incentivized
hydrograph is very usefull when we need to analyze big data. in our scenario it helped a lot with rdms databasesIncentivized
Rich APIs for data transformation making for very each to transform and prepare data in a distributed environment without worrying about memory issuesFaster in execution times compare to Hadoop and PIG LatinEasy SQL interface to the same data set for people who are comfortable to explore data in a declarative mannerInteroperability between SQL and Scala / Python style of munging dataIncentivized
Coupling between complex model and MapReduce framework without reducer procedure was simplified.The ability to reduce execution time and handle partial failureThe framework adapts to higherned complex modelIncentivized
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 localityIncentivized
Microsoft azure is recently joined in 2020 that can be improved.Incentivized
Capacity of computing data in cluster and fast speed.
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.Incentivized
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.
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.Incentivized
Snap logic fits good for small/medium whereas hydrograph suits even for Enterprise grade.Incentivized
Business leaders are able to take data driven decisionsBusiness 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 availableBusiness is able come up with new product ideasIncentivized
Their is no tangible ROI for us Management of bid data is easy.Incentivized