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103 Ratings
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Score 8.5 out of 101
1 Ratings
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Score 10 out of 101

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Likelihood to Recommend

Apache Spark

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.
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Oracle Big Data Cloud

We use it only when we need to and we have found that the software does what it needs to, it's user friendly and us also really helpful in many other ways as well. Like I mentioned before, we love the security and the speed we receive.
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Feature Rating Comparison

Platform-as-a-Service

Apache Spark
Oracle Big Data Cloud
8.1
Ease of building user interfaces
Apache Spark
Oracle Big Data Cloud
9.0
Scalability
Apache Spark
Oracle Big Data Cloud
7.0
Platform management overhead
Apache Spark
Oracle Big Data Cloud
9.0
Workflow engine capability
Apache Spark
Oracle Big Data Cloud
8.0
Platform access control
Apache Spark
Oracle Big Data Cloud
8.0
Services-enabled integration
Apache Spark
Oracle Big Data Cloud
8.0
Development environment creation
Apache Spark
Oracle Big Data Cloud
9.0
Issue recovery
Apache Spark
Oracle Big Data Cloud
7.0

Pros

  • 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
Nitin Pasumarthy profile photo
  • User friendly
  • Offers support and assistance
  • Worth the cost
  • Time efficient
  • Good reliability
  • Reliable support
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Cons

  • Resource heavy, jobs, in general, can be very memory intensive and you will want the nodes in your cluster to reflect that.
  • Debugging, it has gotten better with every release but sometimes it can be difficult to debug an error due to ambiguous or misleading exceptions and stack traces.
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  • Less pricey
  • Customizable
  • A little more help in setting up/using the systems
  • Constant upgrades are always pricey
  • Some bugs noticable
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Alternatives Considered

I prefer Apache Spark compared to Hadoop, since in my experience Spark has more usability and comes equipped with simple APIs for Scala, Python, Java and Spark SQL, as well as provides feedback in REPL format on the commands. At the same time, Apache Spark seems to have the best performance in the processing of large data that works in memory and, therefore, more processes can be downloaded on Spark than on Hadoop, despite the fact that Hadoop is also a very useful tool.
Carla Borges profile photo
Although new, Oracle has been exceptionally good speed wise and the customer service is top notch
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Return on Investment

  • We were able to make batch job faster by 20 times as compared to MapReduce
  • With the language support like Scala, Java, and Python, easily manageable
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  • It has had a good impact we are able to complete our projects in time
  • We can save all our data in a safe and secure location
  • Our data analysis is now faster than ever
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Pricing Details

Apache Spark

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details

Oracle Big Data Cloud

General
Free Trial
Free/Freemium Version
Premium Consulting/Integration Services
Entry-level set up fee?
No
Additional Pricing Details