Apache Spark vs. Cloudera Manager

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.5 out of 10
N/A
N/AN/A
Cloudera Manager
Score 9.7 out of 10
N/A
Cloudera Manager is a management application for Apache Hadoop and the enterprise data hub, from Cloudera.
$0.07
per hour CCU
Pricing
Apache SparkCloudera Manager
Editions & Modules
No answers on this topic
Data Hub
$0.04/CCU
Hourly rate
Data Engineering
$0.07/CCU
Hourly rate
Data Warehouse
$0.07/CCU
Hourly rate
Operational Database
$0.08/CCU
Hourly rate
Flow Management on Data Hub
$0.15/CCU
Hourly rate
Machine Learning
$0.17/CCU
Hourly rate
DataFlow
$0.30/CCU
Hourly rate
Offerings
Pricing Offerings
Apache SparkCloudera Manager
Free Trial
NoNo
Free/Freemium Version
NoNo
Premium Consulting/Integration Services
NoNo
Entry-level Setup FeeNo setup feeNo setup fee
Additional DetailsPricing is per Cloudera Compute Unit (CCU) which is a combination of Core and Memory. CCU prices shown for each service are estimates and may vary depending on actual instance types. The prices reflected do not include infrastructure cost, networking costs, and other related costs which will vary depending on the services you choose and your cloud service provider.
More Pricing Information
Community Pulse
Apache SparkCloudera Manager
Top Pros
Top Cons
Best Alternatives
Apache SparkCloudera Manager
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
Apache Spark
Apache Spark
Score 8.5 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 SparkCloudera Manager
Likelihood to Recommend
9.4
(23 ratings)
8.5
(2 ratings)
Likelihood to Renew
10.0
(1 ratings)
8.5
(2 ratings)
Usability
9.4
(2 ratings)
-
(0 ratings)
Support Rating
8.6
(6 ratings)
-
(0 ratings)
User Testimonials
Apache SparkCloudera Manager
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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Cloudera
It would be suited for customers who feel more comfortable with using a GUI. It is less appropriate for developers or engineers who are comfortable with command line
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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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Cloudera
  • Graphical user interface
  • Management of third party applications start/stop/restart functionality through framework
  • Support of Apache Hadoop ecosystem
  • Ability to do "rolling restarts"
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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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Cloudera
  • Cloudera Manager needs to be more agile with integrating other applications, such as Accumulo 1.7, to their software.
  • Cloudera Manager can do a better job at explaining why a node fails to add to a cluster using their assistant.
  • Cloudera Manager should show graphs only when there is data, instead of showing just an empty box.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Cloudera
It is a well developed product with a good user interface.
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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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Cloudera
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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Cloudera
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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Cloudera
I have not used any competitors, such as Hortonworks, because Cloudera Manager just works and meets all my customer's needs. I only have deployed Hadoop using command line, which is not easy to use and manage.
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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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Cloudera
  • Cloudera Manager has allowed our organization to deploy Apache Hadoop to operations quicker and with less training versus using the command line exclusively.
  • Increased employee efficiency.
  • Increased product adoption.
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