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102 Ratings
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Score 8.5 out of 101
22 Ratings
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Score 8 out of 101

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Likelihood to Recommend

Apache Spark

Apache Spark has rich APIs for regular data transformations or for ML workloads or for graph workloads, whereas other systems may not such a wide range of support. Choose it when you need to perform data transformations for big data as offline jobs, whereas use MongoDB-like distributed database systems for more realtime queries.
Nitin Pasumarthy profile photo

IBM Analytics Engine

  • Well suited for my big data related project or a static data set analysis especially for uploading huge dataset to the cluster.
  • But had some issues with connecting IoT real-time data and feeding to Power BI. It might be my understanding please take it as a mere comment rather than a suggestion.
Prasanna Nattuthurai profile photo

Pros

  • Machine Learning.
  • Data Analysis
  • WorkFlow process (faster than MapReduce).
  • SQL connector to multiple data sources
Anson Abraham profile photo
  • We are able to build and deploy clusters within minutes to simplify user experience and increase scalability and reliability.
  • We are able to scale and compute on-demand to handle newer workloads like machine learning.
  • We really like that we are able to access and administer the application via multiple interfaces.
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Cons

  • Data visualization.
  • Waiting for Web Development for small apps to be started with Spark as backbone middleware and HDFS as data retrieval file system.
  • Transformations and actions available are limited so must modify API to work for more features.
Kamesh Emani profile photo
  • Some of the documentation can be overwhelming and very technical. We would love to have some documentation written for non-technical people.
  • We would like to see better communication from IBM regarding upgrades and patching cycles of the product.
  • The learning curve for this product is steep. This is definitely not a product that will be used right out of the box.
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Alternatives Considered

There are a few newer frameworks for general processing like Flink, Beam, frameworks for streaming like Samza and Storm, and traditional Map-Reduce. I think Spark is at a sweet spot where its clearly better than Map-Reduce for many workflows yet has gotten a good amount of support in the community that there is little risk in deploying it. It also integrates batch and streaming workflows and APIs, allowing an all in package for multiple use-cases.
No photo available
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Return on Investment

  • Workflow process using spark went from 1 day to 2 hours
  • Spark Streaming allowed for quick determiniation of data validity
  • spark on yarn was good for manangement. But Spark with Kubernetes was easier to use.
Anson Abraham profile photo
  • Increasing learning success. My class and I were able to practice real tools
  • The only downsize is without the school, it would be unaffordable to use the tools
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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

IBM Analytics Engine

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