Apache Spark Reviews

114 Ratings
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Score 8.4 out of 100

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Reviews (1-17 of 17)

Thomas Young profile photo
Score 7 out of 10
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Return on Investment

  • In one sense, Apache Spark has been a positive ROI because it helps us figure out details of the vast amounts of data. Sometimes the software leads to answers to questions that are surprising. Small data software tools probably would have failed in discovering some of the insights Spark makes possible.
  • Spark has been a negative ROI in the sense that it takes lots and lots of time to produce simple answers to simple questions, and often the answers are what was expected. Because of the confirmatory rather than insightful nature of the software, it seems like a lot of effort for the results garnered.
  • Apache Spark represents a positive ROI on the instances when it gives a well-producing machine learning model, a model that produces predictions that actually get used.
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March 16, 2019

Apache Spark Review

Score 7 out of 10
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Return on Investment

  • Positive impact on analyzing big data.
  • Fast customer service saved our time.
  • Easy to use which means less time spent on training the team.
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March 06, 2019

Sparking the future

Score 8 out of 10
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Return on Investment

  • The ability to program and run Spark programs makes consulting companies more attractive to clients. Clients like hearing new technology being leveraged and fancy terms.
  • Projects can be completed faster because the programs run faster.
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Nitin Pasumarthy profile photo
Score 10 out of 10
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Return on Investment

  • Switching from PIG Latin to Apache Spark sped up the overall development time and also the resource utilization has gone up.
  • Our offline jobs also run faster than traditional map-reduce like systems.
  • Integrating with Jupyter like notebook environments, the development experience becomes more pleasant and we can iterate much faster.
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Carla Borges profile photo
Score 10 out of 10
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Return on Investment

  • 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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Kartik Chavan profile photo
Score 9 out of 10
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Return on Investment

  • Apache Spark has faster performance compared to MapReduce.
  • Combination of Python & Spark is the best. Shorter code, faster and efficient performance.
  • Can replace RDBMS
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Jordan Moore profile photo
Score 8 out of 10
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Return on Investment

  • By learning Spark, we can become certified and/or provide proper recommendations or implementations on Spark solutions.
  • With a background in Hadoop distributed processes, it has been easy to understand and diagnose how Spark handles the transfer of data within a cluster. Especially when using YARN as the resource manager and HDFS as the data source.
  • Staying up to date with the latest changes to Spark has become a repetitive task. While most Hadoop distributions only support Spark 1.6 at the moment, Spark 2.0 has introduced some useful features, but those require a re-write of existing applications.
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Score 9 out of 10
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Return on Investment

  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
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About Apache Spark

Categories:  Hadoop-Related

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