Likelihood to Recommend If you are looking to take a lot of the traditional "database administration" work off someone's plate, going with Amazon Athena certainly has "no code" options to optimize lots of database tasks. I would say this option is less appropriate if you have other Microsoft things at play, such as Power BI.
Read full review 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 Pros Nested Schemas like JSON data structure Ability to adapt the data model to fit your queries better Performance Improvement Read full review 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 Cons Query manager can incoperate GUI based query designer Auto-completion engine sometimes overwrite the query Time range selection should be implicit Read full review 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 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 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 Alternatives Considered Amazon Athena, a product from Amazon, competes with offerings from Google and Microsoft. Overall, I think your database choice depends on some of the other applications you are running at your company. For example, if you are using
Microsoft Power BI for reporting needs, you might want to consider going the Azure route.
Read full review 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 Return on Investment Easy to query terabytes of data with faster response Pricing model is also cheap No indexing and partitioning Read full review 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 ScreenShots