Apache Spark vs. Drools

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.7 out of 10
N/A
N/AN/A
Drools
Score 7.0 out of 10
N/A
Drools is an open source business rules management system developed by Red Hat.N/A
Pricing
Apache SparkDrools
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkDrools
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
Best Alternatives
Apache SparkDrools
Small Businesses

No answers on this topic

No answers on this topic

Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.9 out of 10
IBM Cloud Pak for Business Automation
IBM Cloud Pak for Business Automation
Score 9.1 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 8.7 out of 10
IBM Cloud Pak for Business Automation
IBM Cloud Pak for Business Automation
Score 9.1 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkDrools
Likelihood to Recommend
10.0
(23 ratings)
7.0
(2 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
10.0
(3 ratings)
-
(0 ratings)
Support Rating
8.7
(4 ratings)
-
(0 ratings)
User Testimonials
Apache SparkDrools
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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Open Source
As an open source rule engine and product suite, Drools is well suited for the small and middle scale business to manage and integrate the rules to build the rule-driven system which can process the business-critical data and events to produce the automated decision. It is better to use Drools in the well-secured environment (back-end behind the DMZ), not putting it on the customer-facing front or exposing it directly the to public where may bring direct security risk in the enterprise environment. Drools still needs a lot hardening on the security side.
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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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Open Source
  • Writing rules with business focus
  • Rules evolution and maintenance
  • separate business logic from program code
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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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Open Source
  • Fusion doesn't support persistence of working memory, which brings some extra high availability risk to our business.
  • Guvnor still has a lot room to be implemented, it is not so user-friendly for non-technical people, so a lot of business users complain it is hard to master.
  • Rule execution server doesn't even have JMX implemented, hard to be monitored.
  • Drools is still lacking support for key Web services standards.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Open Source
No answers on this topic
Usability
Apache
The only thing I dislike about spark's usability is the learning curve, there are many actions and transformations, however, its wide-range of uses for ETL processing, facility to integrate and it's multi-language support make this library a powerhouse for your data science solutions. It has especially aided us with its lightning-fast processing times.
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Open Source
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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Open Source
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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Open Source
I did not participate in drools choice. I can only compare drools with the previous situation which was using nothing.
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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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Open Source
  • The IT department quickly adopted Drools as it is a very good java-based rule engine, which saves a lot of time to meet the project timeline and balanced our business requirements.
  • Recently we start considering the OpenRules, which may be more business user-friendly.
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