Apache Spark vs Data Science Workbench

• Spark uses Scala which is a functional programming language and easy to use language. Syntax is simpler and human readable.
• It can be used to run transformations on huge data on different cluster parallelly. It automatically optimizes the process to get output efficiently in less time.
• It also provides machine learning API for data science applications and also Spark SQL to query fast for data analysis.
• I also use Zeppelin online tool which is used to fast query and very helpful for BI guys to visualize query outputs.

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• One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
• For larger organizations/teams, it lets you be self reliant
• As it sits on your cluster, it has very easy access of all the data on the HDFS
• Linking with Github is a very good way to keep the code versions intact

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• No true streaming.
• Lack of strongly typed yet convenient APIs.

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• Not as great as RStudio; lacks some features when compared with it
• It is quite simple still (because its very early in its initiative), and companies may want to wait until they see a more developed product

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• Positive: we don't worry about scale.
• Positive: large support community.
• Negative: Takes time to set up, overkill for many simpler workflows.

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• As the tool itself can access all the HDFS, Spark data easily, the wait time between teams has reduced
• Installation was a breeze, and ramp up time was fairly easy

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