Cloudera Data Platform vs. TensorFlow

Overview
ProductRatingMost Used ByProduct SummaryStarting Price
Cloudera Data Platform
Score 6.7 out of 10
N/A
Cloudera Data Platform (CDP), launched September 2019, is designed to combine the best of Hortonworks and Cloudera technologies to deliver an enterprise data cloud. CDP includes the Cloudera Data Warehouse and machine learning services as well as a Data Hub service for building custom business applications.
$0.04
per CCU (hourly rate)
TensorFlow
Score 8.8 out of 10
N/A
TensorFlow is an open-source machine learning software library for numerical computation using data flow graphs. It was originally developed by Google.N/A
Pricing
Cloudera Data PlatformTensorFlow
Editions & Modules
CDP Public Cloud - Data Hub
$0.04
per CCU (hourly rate)
CDP Public Cloud - Data Warehouse
$0.054
per CCU (hourly rate)
CDP Public Cloud - Data Engineering
$0.07
per CCU (hourly rate)
CDP Public Cloud - Operational Database
$0.08
per CCU (hourly rate)
CDP Public Cloud - Flow Management
$0.15
per CCU (hourly rate)
CDP Public Cloud - Machine Learning
$0.17
per CCU (hourly rate)
CDP Private Cloud - Plus Edition
$400
CCU (annual subscription)
CDP Private Cloud - Base Edition
$10,000.00
node + variable (annual subscription)
No answers on this topic
Offerings
Pricing Offerings
Cloudera Data PlatformTensorFlow
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
Cloudera Data PlatformTensorFlow
Small Businesses
Google BigQuery
Google BigQuery
Score 8.7 out of 10
Saturn Cloud
Saturn Cloud
Score 9.1 out of 10
Medium-sized Companies
Cloudera Enterprise Data Hub
Cloudera Enterprise Data Hub
Score 9.0 out of 10
Posit
Posit
Score 9.0 out of 10
Enterprises
Oracle Exadata
Oracle Exadata
Score 8.1 out of 10
Dataiku
Dataiku
Score 8.6 out of 10
All AlternativesView all alternativesView all alternatives
User Ratings
Cloudera Data PlatformTensorFlow
Likelihood to Recommend
7.0
(1 ratings)
8.5
(14 ratings)
Usability
-
(0 ratings)
9.0
(1 ratings)
Support Rating
8.0
(1 ratings)
9.1
(2 ratings)
Implementation Rating
-
(0 ratings)
8.0
(1 ratings)
User Testimonials
Cloudera Data PlatformTensorFlow
Likelihood to Recommend
Cloudera
I have seen that Cloudera Data Platform is well suited for large batch processes. It works really well for our indication analyses that are performed by the actuaries. I feel that rapid streaming operations may be a situation where additional technology would be needed to provide for a robust solution.
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Open Source
TensorFlow is great for most deep learning purposes. This is especially true in two domains: 1. Computer vision: image classification, object detection and image generation via generative adversarial networks 2. Natural language processing: text classification and generation. The good community support often means that a lot of off-the-shelf models can be used to prove a concept or test an idea quickly. That, and Google's promotion of Colab means that ideas can be shared quite freely. Training, visualizing and debugging models is very easy in TensorFlow, compared to other platforms (especially the good old Caffe days). In terms of productionizing, it's a bit of a mixed bag. In our case, most of our feature building is performed via Apache Spark. This means having to convert Parquet (columnar optimized) files to a TensorFlow friendly format i.e., protobufs. The lack of good JVM bindings mean that our projects end up being a mix of Python and Scala. This makes it hard to reuse some of the tooling and support we wrote in Scala. This is where MXNet shines better (though its Scala API could do with more work).
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Pros
Cloudera
  • Scales
  • Highly available
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Open Source
  • A vast library of functions for all kinds of tasks - Text, Images, Tabular, Video etc.
  • Amazing community helps developers obtain knowledge faster and get unblocked in this active development space.
  • Integration of high-level libraries like Keras and Estimators make it really simple for a beginner to get started with neural network based models.
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Cons
Cloudera
  • Constantly changing costs
  • Log visibility
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Open Source
  • RNNs are still a bit lacking, compared to Theano.
  • Cannot handle sequence inputs
  • Theano is perhaps a bit faster and eats up less memory than TensorFlow on a given GPU, perhaps due to element-wise ops. Tensorflow wins for multi-GPU and “compilation” time.
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Usability
Cloudera
No answers on this topic
Open Source
Support of multiple components and ease of development.
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Support Rating
Cloudera
We have utilized Cloudera support quite frequently and are very satisfied with the capability and responsiveness of that team. Often, the new features delivered with the platform give us an opportunity to mature the way we're doing things, and the support team have been valuable in developing those new patterns.
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Open Source
Community support for TensorFlow is great. There's a huge community that truly loves the platform and there are many examples of development in TensorFlow. Often, when a new good technique is published, there will be a TensorFlow implementation not long after. This makes it quick to ally the latest techniques from academia straight to production-grade systems. Tooling around TensorFlow is also good. TensorBoard has been such a useful tool, I can't imagine how hard it would be to debug a deep neural network gone wrong without TensorBoard.
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Implementation Rating
Cloudera
No answers on this topic
Open Source
Use of cloud for better execution power is recommended.
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Alternatives Considered
Cloudera
IBM's offering of the Cloud Pak for Data has been a moving target and difficult to compare to Cloudera Data Platform. We have implemented our solution on Amazon Web Services, which appears to be supported by IBM at this point, but the migration would be very expensive for us to endeavor.
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Open Source
Keras is built on top of TensorFlow, but it is much simpler to use and more Python style friendly, so if you don't want to focus on too many details or control and not focus on some advanced features, Keras is one of the best options, but as far as if you want to dig into more, for sure TensorFlow is the right choice
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Return on Investment
Cloudera
  • Reduced operational costs
  • Speed to market
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Open Source
  • Learning is s bit difficult takes lot of time.
  • Developing or implementing the whole neural network is time consuming with this, as you have to write everything.
  • Once you have learned this, it make your job very easy of getting the good result.
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