Apache Spark vs. Oracle Big Data Cloud Service

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.9 out of 10
N/A
N/AN/A
Oracle Big Data Cloud Service
Score 7.0 out of 10
N/A
The Oracle Big Data Cloud Services features managed and secure platform cloud service for Apache Hadoop and Apache Spark delivered as an elastic, integrated platform. It provides support for streaming, batch, and interactive analysis.N/A
Pricing
Apache SparkOracle Big Data Cloud Service
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkOracle Big Data Cloud Service
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional Details
More Pricing Information
Community Pulse
Apache SparkOracle Big Data Cloud Service
Top Pros
Top Cons
Features
Apache SparkOracle Big Data Cloud Service
Platform-as-a-Service
Comparison of Platform-as-a-Service features of Product A and Product B
Apache Spark
-
Ratings
Oracle Big Data Cloud Service
8.1
1 Ratings
1% above category average
Ease of building user interfaces00 Ratings9.01 Ratings
Scalability00 Ratings7.01 Ratings
Platform management overhead00 Ratings9.01 Ratings
Workflow engine capability00 Ratings8.01 Ratings
Platform access control00 Ratings8.01 Ratings
Services-enabled integration00 Ratings8.01 Ratings
Development environment creation00 Ratings9.01 Ratings
Issue recovery00 Ratings7.01 Ratings
Best Alternatives
Apache SparkOracle Big Data Cloud Service
Small Businesses

No answers on this topic

AWS Elastic Beanstalk
AWS Elastic Beanstalk
Score 8.9 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
Red Hat OpenShift
Red Hat OpenShift
Score 9.2 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 7.7 out of 10
Red Hat OpenShift
Red Hat OpenShift
Score 9.2 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkOracle Big Data Cloud Service
Likelihood to Recommend
9.3
(24 ratings)
10.0
(1 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
8.7
(4 ratings)
-
(0 ratings)
Support Rating
8.7
(4 ratings)
-
(0 ratings)
User Testimonials
Apache SparkOracle Big Data Cloud Service
Likelihood to Recommend
Apache
Well suited: To most of the local run of datasets and non-prod systems - scalability is not a problem at all. Including data from multiple types of data sources is an added advantage. MLlib is a decently nice built-in library that can be used for most of the ML tasks. Less appropriate: We had to work on a RecSys where the music dataset that we used was around 300+Gb in size. We faced memory-based issues. Few times we also got memory errors. Also the MLlib library does not have support for advanced analytics and deep-learning frameworks support. Understanding the internals of the working of Apache Spark for beginners is highly not possible.
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Oracle
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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Pros
Apache
  • 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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Oracle
  • User friendly
  • Offers support and assistance
  • Worth the cost
  • Time efficient
  • Good reliability
  • Reliable support
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Cons
Apache
  • 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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Oracle
  • 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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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Oracle
No answers on this topic
Usability
Apache
If the team looking to use Apache Spark is not used to debug and tweak settings for jobs to ensure maximum optimizations, it can be frustrating. However, the documentation and the support of the community on the internet can help resolve most issues. Moreover, it is highly configurable and it integrates with different tools (eg: it can be used by dbt core), which increase the scenarios where it can be used
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Oracle
No answers on this topic
Support Rating
Apache
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.
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Oracle
No answers on this topic
Alternatives Considered
Apache
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.
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Oracle
Although new, Oracle has been exceptionally good speed wise and the customer service is top notch
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Return on Investment
Apache
  • 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
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Oracle
  • 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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ScreenShots