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.
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Pricing
Apache Spark
Cloudera Data Science Workbench
Editions & Modules
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Pricing Offerings
Apache Spark
Data Science Workbench
Free Trial
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No
Free/Freemium Version
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Premium Consulting/Integration Services
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Entry-level Setup Fee
No setup fee
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Community Pulse
Apache Spark
Cloudera Data Science Workbench
Features
Apache Spark
Cloudera 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 Sources
00 Ratings
7.02 Ratings
Extend Existing Data Sources
00 Ratings
8.02 Ratings
Automatic Data Format Detection
00 Ratings
7.02 Ratings
MDM Integration
00 Ratings
8.02 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
Visualization
00 Ratings
7.12 Ratings
Interactive Data Analysis
00 Ratings
8.02 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 Enrichment
00 Ratings
7.02 Ratings
Data Transformations
00 Ratings
8.02 Ratings
Data Encryption
00 Ratings
8.02 Ratings
Built-in Processors
00 Ratings
8.02 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 Tools
00 Ratings
8.02 Ratings
Automated Machine Learning
00 Ratings
7.01 Ratings
Single platform for multiple model development
00 Ratings
7.12 Ratings
Self-Service Model Delivery
00 Ratings
8.12 Ratings
Model Deployment
Comparison of Model Deployment features of Product A and Product B
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.
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.
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
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.
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.
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.
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.