Apache Spark vs. Cloudera Data Science Workbench

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Apache Spark
Score 8.7 out of 10
N/A
N/AN/A
Data Science Workbench
Score 6.7 out of 10
N/A
Cloudera Data Science Workbench enables secure self-service data science for the enterprise. It is a collaborative environment where developers can work with a variety of libraries and frameworks.N/A
Pricing
Apache SparkCloudera Data Science Workbench
Editions & Modules
No answers on this topic
No answers on this topic
Offerings
Pricing Offerings
Apache SparkData Science Workbench
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 SparkCloudera Data Science Workbench
Top Pros
Top Cons
Features
Apache SparkCloudera Data Science Workbench
Platform Connectivity
Comparison of Platform Connectivity features of Product A and Product B
Apache Spark
-
Ratings
Cloudera Data Science Workbench
7.5
2 Ratings
12% below category average
Connect to Multiple Data Sources00 Ratings7.02 Ratings
Extend Existing Data Sources00 Ratings8.02 Ratings
Automatic Data Format Detection00 Ratings7.02 Ratings
MDM Integration00 Ratings8.02 Ratings
Data Exploration
Comparison of Data Exploration features of Product A and Product B
Apache Spark
-
Ratings
Cloudera Data Science Workbench
7.6
2 Ratings
10% below category average
Visualization00 Ratings7.12 Ratings
Interactive Data Analysis00 Ratings8.02 Ratings
Data Preparation
Comparison of Data Preparation features of Product A and Product B
Apache Spark
-
Ratings
Cloudera Data Science Workbench
7.8
2 Ratings
5% below category average
Interactive Data Cleaning and Enrichment00 Ratings7.02 Ratings
Data Transformations00 Ratings8.02 Ratings
Data Encryption00 Ratings8.02 Ratings
Built-in Processors00 Ratings8.02 Ratings
Platform Data Modeling
Comparison of Platform Data Modeling features of Product A and Product B
Apache Spark
-
Ratings
Cloudera Data Science Workbench
7.6
2 Ratings
11% below category average
Multiple Model Development Languages and Tools00 Ratings8.02 Ratings
Automated Machine Learning00 Ratings7.01 Ratings
Single platform for multiple model development00 Ratings7.12 Ratings
Self-Service Model Delivery00 Ratings8.12 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
Apache Spark
-
Ratings
Cloudera Data Science Workbench
8.0
2 Ratings
7% below category average
Flexible Model Publishing Options00 Ratings8.12 Ratings
Security, Governance, and Cost Controls00 Ratings7.82 Ratings
Best Alternatives
Apache SparkCloudera Data Science Workbench
Small Businesses

No answers on this topic

Saturn Cloud
Saturn Cloud
Score 9.1 out of 10
Medium-sized Companies
Cloudera Manager
Cloudera Manager
Score 9.7 out of 10
Mathematica
Mathematica
Score 8.3 out of 10
Enterprises
IBM Analytics Engine
IBM Analytics Engine
Score 9.3 out of 10
Dataiku
Dataiku
Score 8.6 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Apache SparkCloudera Data Science Workbench
Likelihood to Recommend
9.9
(24 ratings)
9.0
(3 ratings)
Likelihood to Renew
10.0
(1 ratings)
-
(0 ratings)
Usability
10.0
(3 ratings)
-
(0 ratings)
Support Rating
8.7
(4 ratings)
7.9
(2 ratings)
User Testimonials
Apache SparkCloudera Data Science Workbench
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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Cloudera
Organizations which already implemented on-premise Hadoop based Cloudera Data Platform (CDH) for their Big Data warehouse architecture will definitely get more value from seamless integration of Cloudera Data Science Workbench (CDSW) with their existing CDH Platform. However, for organizations with hybrid (cloud and on-premise) data platform without prior implementation of CDH, implementing CDSW can be a challenge technically and financially.
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Pros
Apache
  • Apache Spark makes processing very large data sets possible. It handles these data sets in a fairly quick manner.
  • Apache Spark does a fairly good job implementing machine learning models for larger data sets.
  • Apache Spark seems to be a rapidly advancing software, with the new features making the software ever more straight-forward to use.
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Cloudera
  • One single IDE (browser based application) that makes Scala, R, Python integrated under one tool
  • For larger organizations/teams, it lets you be self reliant
  • As it sits on your cluster, it has very easy access of all the data on the HDFS
  • Linking with Github is a very good way to keep the code versions intact
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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
  • Installation is difficult.
  • Upgrades are difficult.
  • Licensing options are not flexible.
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Likelihood to Renew
Apache
Capacity of computing data in cluster and fast speed.
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Cloudera
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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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
Cloudera Data Science Workbench has excellence online resources support such as documentation and examples. On top of that the enterprise license also comes with SLA on opening a ticket to Cloudera Services and support for complaint handling and troubleshooting by email or through a phone call. On top of that it also offers additional paid training services.
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Alternatives Considered
Apache
All the above systems work quite well on big data transformations whereas Spark really shines with its bigger API support and its ability to read from and write to multiple data sources. Using Spark one can easily switch between declarative versus imperative versus functional type programming easily based on the situation. Also it doesn't need special data ingestion or indexing pre-processing like Presto. Combining it with Jupyter Notebooks (https://github.com/jupyter-incubator/sparkmagic), one can develop the Spark code in an interactive manner in Scala or Python
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Cloudera
Both the tools have similar features and have made it pretty easy to install/deploy/use. Depending on your existing platform (Cloudera vs. Azure) you need to pick the Workbench. Another observation is that Cloudera has better support where you can get feedback on your questions pretty fast (unlike MS). As its a new product, I expect MS to be more efficient in handling customers questions.
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Return on Investment
Apache
  • Faster turn around on feature development, we have seen a noticeable improvement in our agile development since using Spark.
  • Easy adoption, having multiple departments use the same underlying technology even if the use cases are very different allows for more commonality amongst applications which definitely makes the operations team happy.
  • Performance, we have been able to make some applications run over 20x faster since switching to Spark. This has saved us time, headaches, and operating costs.
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Cloudera
  • Paid off for demonstration purposes.
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ScreenShots