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Apache Spark vs TensorFlow

Apache Spark
127 Ratings
TensorFlow
33 Ratings

Apache Spark

127 Ratings
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Score 8.7 out of 100

Based on 127 reviews and ratings

TensorFlow

33 Ratings
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Score 7.6 out of 100

Based on 33 reviews and ratings

Likelihood to Recommend

Apache Spark

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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Thomas Young | TrustRadius Reviewer
Thomas Young
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TensorFlow

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 networks2. 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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Anonymous | TrustRadius Reviewer
Verified User
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Pros

Apache Spark

  • 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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Nitin Pasumarthy | TrustRadius Reviewer
Nitin Pasumarthy
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TensorFlow

  • 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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Nitin Pasumarthy | TrustRadius Reviewer
Nitin Pasumarthy
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Cons

Apache Spark

  • 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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Anson Abraham | TrustRadius Reviewer
Anson Abraham
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TensorFlow

  • 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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Nisha murthy | TrustRadius Reviewer
Nisha murthy
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Usability

Apache Spark

Apache Spark 8.7
Based on 3 answers
Apache integrates with multiple big data frameworks. It does not exert too much load on the disks. Moreover, it is easy to program and use. It reduces the headache of using different applications separately through its high-level APIs. Big data processing has never been as easy as it is with Apache Spark.

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Partha Protim Pegu | TrustRadius Reviewer
Partha Protim Pegu
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TensorFlow

TensorFlow 9.0
Based on 1 answer
Support of multiple components and ease of development.

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Anupam Mittal | TrustRadius Reviewer
Anupam Mittal
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Support Rating

Apache Spark

Apache Spark 8.2
Based on 6 answers
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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Yogesh Mhasde | TrustRadius Reviewer
Yogesh Mhasde
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TensorFlow

TensorFlow 9.2
Based on 2 answers
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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Anonymous | TrustRadius Reviewer
Verified User
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Implementation Rating

Apache Spark

No score
No answers yet
No answers on this topic

TensorFlow

TensorFlow 8.0
Based on 1 answer
Use of cloud for better execution power is recommended.

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Anupam Mittal | TrustRadius Reviewer
Anupam Mittal
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Alternatives Considered

Apache Spark

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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Anonymous | TrustRadius Reviewer
Verified User
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TensorFlow

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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Anonymous | TrustRadius Reviewer
Verified User
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Return on Investment

Apache Spark

  • It has had a very positive impact, as it helps reduce the data processing time and thus helps us achieve our goals much faster.
  • Being easy to use, it allows us to adapt to the tool much faster than with others, which in turn allows us to access various data sources such as Hadoop, Apache Mesos, Kubernetes, independently or in the cloud. This makes it very useful.
  • It was very easy for me to use Apache Spark and learn it since I come from a background of Java and SQL, and it shares those basic principles and uses a very similar logic.

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Carla Borges | TrustRadius Reviewer
Carla Borges
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TensorFlow

  • 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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Shambhavi Jha | TrustRadius Reviewer
Shambhavi Jha
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Pricing Details

Apache Spark

General

Free Trial
—
Free/Freemium Version
—
Premium Consulting/Integration Services
—
Entry-level set up fee?
No

TensorFlow

General

Free Trial
—
Free/Freemium Version
—
Premium Consulting/Integration Services
—
Entry-level set up fee?
No

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