Apache Spark vs. Kognitio

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.5 out of 10
N/A
N/AN/A
Kognitio
Score 9.1 out of 10
N/A
WX2 is the data and analytics focused data warehouse appliance solution from UK company Kognitio.N/A
Pricing
Apache SparkKognitio
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkKognitio
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 SparkKognitio
Small Businesses

No answers on this topic

No answers on this topic

Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.1 out of 10
IBM Analytics Engine
IBM Analytics Engine
Score 9.1 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkKognitio
Likelihood to Recommend
9.4
(23 ratings)
9.0
(3 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
9.4
(2 ratings)
-
(0 ratings)
Support Rating
8.6
(6 ratings)
-
(0 ratings)
User Testimonials
Apache SparkKognitio
Likelihood to Recommend
Apache
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.
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Kognitio
What you have are different strategies for data encoding, which makes the process quite flexible, it is perfectly done so that a joint and collaborative work can be carried out, this information analyzed in large quantities, is extremely vital for the company, by giving it the correct and timely reading
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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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Kognitio
  • Ultra fast query results.
  • IN Memory Database.
  • Easy integration to reporting services.
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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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Kognitio
  • Multiple Query facilitation that can be user friendly.
  • Easy workflows to implement.
  • Historical query loader.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Kognitio
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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Kognitio
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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Kognitio
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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Kognitio
The understandable and complete tables and graphs, the cleaning methods and the way of encrypting the data are quite feasible, which does not help to prepare our data, it helps that the data that is thrown as results is separated from each other, the process prior to structuring requires high-level advice and is somewhat time-consuming, there is a risk that they overwrite the data themselves by accident at a later time
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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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Kognitio
  • Faster data analysis.
  • Analytical reports are now easy to create.
  • Company strategy in terms of overall are improved.
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