What users are saying about
49 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>
Score 8.7 out of 100
40 Ratings
<a href='https://www.trustradius.com/static/about-trustradius-scoring' target='_blank' rel='nofollow noopener'>trScore algorithm: Learn more.</a>
Score 7.5 out of 100

Attribute Ratings

  • Databricks Lakehouse Platform (Unified Analytics Platform) is rated higher in 1 area: Likelihood to Recommend
  • TensorFlow is rated higher in 1 area: Support Rating
  • Databricks Lakehouse Platform (Unified Analytics Platform) and TensorFlow are tied in 1 area: Usability

Likelihood to Recommend

8.7

Databricks Lakehouse Platform

87%
15 Ratings
6.8

TensorFlow

68%
14 Ratings

Usability

9.0

Databricks Lakehouse Platform

90%
3 Ratings
9.0

TensorFlow

90%
1 Rating

Support Rating

7.5

Databricks Lakehouse Platform

75%
2 Ratings
9.1

TensorFlow

91%
4 Ratings

Implementation Rating

Databricks Lakehouse Platform

N/A
0 Ratings
8.0

TensorFlow

80%
2 Ratings

Contract Terms and Pricing Model

8.0

Databricks Lakehouse Platform

80%
1 Rating

TensorFlow

N/A
0 Ratings

Professional Services

10.0

Databricks Lakehouse Platform

100%
1 Rating

TensorFlow

N/A
0 Ratings

Likelihood to Recommend

Databricks Lakehouse Platform

If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
Anonymous | TrustRadius Reviewer

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).
Anonymous | TrustRadius Reviewer

Pros

Databricks Lakehouse Platform

  • Process raw data in One Lake (S3) env to relational tables and views
  • Share notebooks with our business analysts so that they can use the queries and generate value out of the data
  • Try out PySpark and Spark SQL queries on raw data before using them in our Spark jobs
  • Modern day ETL operations made easy using Databricks. Provide access mechanism for different set of customers
Anonymous | TrustRadius Reviewer

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.
Nitin Pasumarthy | TrustRadius Reviewer

Cons

Databricks Lakehouse Platform

  • Better Localized Testing
  • When they were primarily OSS Spark; it was easier to test/manage releases versus the newer DB Runtime. Wish there was more configuration in Runtime less pick a version.
  • Graphing Support went non-existent; when it was one of their compelling general engine.
Anonymous | TrustRadius Reviewer

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.
Nisha murthy | TrustRadius Reviewer

Pricing Details

Databricks Lakehouse Platform

General

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

Starting Price

$0.07 Per DBU

Databricks Lakehouse Platform Editions & Modules

Edition
Standard$0.071
Premium$0.101
Enterprise$0.131
  1. Per DBU
Additional Pricing Details

TensorFlow

General

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

Starting Price

TensorFlow Editions & Modules

Additional Pricing Details

Usability

Databricks Lakehouse Platform

Databricks Lakehouse Platform 9.0
Based on 3 answers
Because it is an amazing platform for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, as well as it allows to share the information and insights across the company with their shared workspaces, while keeping it secured.

in terms of graph generation and interaction it could improve their UI and UX
Anonymous | TrustRadius Reviewer

TensorFlow

TensorFlow 9.0
Based on 1 answer
Support of multiple components and ease of development.
Anupam Mittal | TrustRadius Reviewer

Support Rating

Databricks Lakehouse Platform

Databricks Lakehouse Platform 7.5
Based on 2 answers
One of the best customer and technology support that I have ever experienced in my career. You pay for what you get and you get the Rolls Royce. It reminds me of the customer support of SAS in the 2000s when the tools were reaching some limits and their engineer wanted to know more about what we were doing, long before "data science" was even a name. Databricks truly embraces the partnership with their customer and help them on any given challenge.
Jonatan Bouchard | TrustRadius Reviewer

TensorFlow

TensorFlow 9.1
Based on 4 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.
Anonymous | TrustRadius Reviewer

Implementation Rating

Databricks Lakehouse Platform

No score
No answers yet
No answers on this topic

TensorFlow

TensorFlow 8.0
Based on 2 answers
Use of cloud for better execution power is recommended.
Anupam Mittal | TrustRadius Reviewer

Alternatives Considered

Databricks Lakehouse Platform

Databricks has a much better edge than Synapse in hundred different ways. Databricks has Photon engine, faster available release in cloud and databricks does not run on Open source spark version so better optimization, better performance and better agility and all kind of performance boost can be achieved in Databricks rather Open source synapse spark
Anonymous | TrustRadius Reviewer

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
Anonymous | TrustRadius Reviewer

Contract Terms and Pricing Model

Databricks Lakehouse Platform

Databricks Lakehouse Platform 8.0
Based on 1 answer
The problem with this tool and all other ones that are at the top of the industry, it's so expensive that soon as another one will be on the market and deliver the same or different value, it will be catastrophic for them. So you get the fact that they are cashing every dime right now like SAS or Hadoop once did. Now, look at them
Jonatan Bouchard | TrustRadius Reviewer

TensorFlow

No score
No answers yet
No answers on this topic

Professional Services

Databricks Lakehouse Platform

Databricks Lakehouse Platform 10.0
Based on 1 answer
Again, another level of professional services, this is not their biggest strength but this is the cherry on top. I couldn't think about any other professional services like this one. Now I'm talking about meaningful services that really help out our project and delivery.
Jonatan Bouchard | TrustRadius Reviewer

TensorFlow

No score
No answers yet
No answers on this topic

Return on Investment

Databricks Lakehouse Platform

  • Machine learning is a very new concept and not many universities offer to teach it. My school and a few others have been utilizing Databricks as one of the tools to teach and learn machine learning. By doing this, my university is creating a strong future workforce for the job market.
Ann Le | TrustRadius Reviewer

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.
Shambhavi Jha | TrustRadius Reviewer

Add comparison