Apache Spark vs Google BigQuery

• 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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• How many pros can a person type? This storage program gives workers and students the reality of unlimited storage space. I have never came close to overfilling my google cloud storage because it's huge and the best. I can view anything I save on there from any of my internet devices which is very important.
• Depending on how you have the program set up - either online or through an application that lives on your desktop, dragging and dropping files to and from Cloud Storage couldn't be any more uncomplicated. Plus, new users who meet certain criteria - like updating personal security, or share the program receive additional free online storage.
• The array of tools is very impressive, intuitive to use, and well organized in the sense that you don't have to go looking for individual apps. They're all easily accessed via a single dropdown.

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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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• Though it is SQL some syntax are different but they are getting used to after you use for some time.
• The legacy SQL is in beta state but can be used and you can run the query with simple SQL.
• More documentation is needed for using User-defined functions in Big Query.

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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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• Google BigQuery has had enormous impact in terms of ROI to our business, as it has allowed us to ease our dependence on our physical servers, which we pay for monthly from another hosting service. We have been able to run multiple enterprise scale data processing applications with almost no investment
• Since our business is highly client focused, Google Cloud Platform, and BigQuery specifically, has allowed us to get very granular in how our usage should be attributed to different projects, clients, and teams.
• Plain and simple, I believe the meager investments that we have made in Google BigQuery have paid themselves back hundreds of times over.

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