Apache Spark vs IBM Netezza Performance Server

• 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

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• Fast Queries
• Handles Extra Large Data Sets
• Easy to Manage

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• 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

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• Cannot use functions such as pivot, which is in MS SQL Server
• Objects such as tables do not pop up automatically in the editor, users needs to navigate themselves to know the object names

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• It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
• Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
• It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.

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• It allows us to work together simultaneously in the database to query our custom results.
• It requires users to know the object name as it does not pop up in their text editor. Thus, users take more time in writing queries.

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