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134 Ratings
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Score 8.9 out of 100
36 Ratings
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Score 9.1 out of 100

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.
Thomas Young | TrustRadius Reviewer

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 | TrustRadius Reviewer

Pros

Apache Spark

  • 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 | TrustRadius Reviewer

IBM Analytics Engine

  • Jobs with Spark, Hadoop, or Hive queries are rapidly attained
  • Can collect, organize and analyze your data accurately
  • You can customize, for example, Spark or Hadoop configuration settings, or Python, R, Scala, or Java libraries.
Anonymous | TrustRadius Reviewer

Cons

Apache Spark

  • 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
Anson Abraham | TrustRadius Reviewer

IBM Analytics Engine

  • Easier pricing and plug-and-play like you see with AWS and Azure, it would be nice from a budgeting and billing standpoint, as well as better support for the administration.
  • Bundling of the Cloud Object Storage should be included with the Analytics Engine.
  • The inability to add your own Hadoop stack components has made some transfers a little more complex.
Anonymous | TrustRadius Reviewer

Usability

Apache Spark

Apache Spark 8.7
Based on 3 answers
Apache integrates with multiple big data frameworks. It does not exert too much load on the disks. Moreover, it is easy to program and use. It reduces the headache of using different applications separately through its high-level APIs. Big data processing has never been as easy as it is with Apache Spark.
Partha Protim Pegu | TrustRadius Reviewer

IBM Analytics Engine

No score
No answers yet
No answers on this topic

Support Rating

Apache Spark

Apache Spark 8.4
Based on 7 answers
1. It integrates very well with scala or python.2. It's very easy to understand SQL interoperability.3. Apache is way faster than the other competitive technologies.4. The support from the Apache community is very huge for Spark.5. Execution times are faster as compared to others.6. There are a large number of forums available for Apache Spark.7. The code availability for Apache Spark is simpler and easy to gain access to.8. Many organizations use Apache Spark, so many solutions are available for existing applications.
Yogesh Mhasde | TrustRadius Reviewer

IBM Analytics Engine

No score
No answers yet
No answers on this topic

Alternatives Considered

Apache Spark

Spark in comparison to similar technologies ends up being a one stop shop. You can achieve so much with this one framework instead of having to stitch and weave multiple technologies from the Hadoop stack, all while getting incredibility performance, minimal boilerplate, and getting the ability to write your application in the language of your choosing.
Anonymous | TrustRadius Reviewer

IBM Analytics Engine

We initially wanted to go with Google BigQuery, mainly for the name recognition. However, the pricing and support structure led us to seek alternatives, which pointed us to IBM. Apache Spark was also in the running, but here IBM's domination in the industry made the choice a no-brainer. As previously stated, the support received was not quite what we expected, but was adequate.
Anonymous | TrustRadius Reviewer

Return on Investment

Apache Spark

  • Business leaders are able to take data driven decisions
  • Business users are able access to data in near real time now . Before using spark, they had to wait for at least 24 hours for data to be available
  • Business is able come up with new product ideas
Surendranatha Reddy Chappidi | TrustRadius Reviewer

IBM Analytics Engine

  • This product has allowed us to gather analytics data across multiple platforms so we can view and analyze the data from different workflows, all in one place.
  • IBM Analytics has allowed us to scale on demand which allows us to capture more and more data, thus increasing our ROI.
  • The convenience of the ability to access and administer the product via multiple interfaces has allowed our administrators to ensure that the application is making a positive ROI for our business users and partners.
Anonymous | TrustRadius Reviewer

Pricing Details

Apache Spark

General

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

IBM Analytics Engine

General

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

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