Amazon Tensor Flow vs Databricks Lakehouse Platform

Likelihood to Recommend

A well-suited scenario for using AWS Tensor Flow is when having a project with a geographically dispersed team, a client overseas and large data to use for training. AWS Tensor Flow is less appropriate when working for clients in regions where it hasn't been allowed yet for use. Since smaller clients are in regions where AWS Tensor Flow hasn't been allowed for use, and those clients traditionally don't have enough hardware, this situation deters a wider use of the tool.

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Mauricio Quiroga-Pascal Ortega

CEO

DisperSuranceComputer Software, 51-200 employees

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Databricks has helped my teams write PySpark and Spark SQL jobs and test them out before formally integrating them in Spark jobs. Through Databricks we can create parquet and JSON output files. Datamodelers and scientists who are not very good with coding can get good insight into the data using the notebooks that can be developed by the engineers.

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Verified User

Team Lead in Engineering

Financial Services Company, 10,001+ employees

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Feature Rating Comparison

Amazon Tensor Flow

Databricks Lakehouse Platform

Connect to Multiple Data Sources

Extend Existing Data Sources

Automatic Data Format Detection

Amazon Tensor Flow

Databricks Lakehouse Platform

Visualization

Interactive Data Analysis

Amazon Tensor Flow

Databricks Lakehouse Platform

Interactive Data Cleaning and Enrichment

Data Transformations

Data Encryption

Built-in Processors

Amazon Tensor Flow

Databricks Lakehouse Platform

Multiple Model Development Languages and Tools

Automated Machine Learning

Single platform for multiple model development

Self-Service Model Delivery

Amazon Tensor Flow

Databricks Lakehouse Platform

Flexible Model Publishing Options

Security, Governance, and Cost Controls

Pros

Amazon Elastic Compute Cloud (EC2) allows resizable compute capacity in the cloud, providing the necessary elasticity to provide services for both, small and medium-sized businesses.
Tensor Flow allows us to train our models much faster than in our on-premise equipment.
Most of the pre-trained models are easy to adapt to our clients' needs.

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Mauricio Quiroga-Pascal Ortega

CEO

DisperSuranceComputer Software, 51-200 employees

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Extremely Flexible in Data Scenarios
Fantastic Performance
DB is always updating the system so we can have latest features.

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Verified User

Director in Information Technology

Financial Services Company, 201-500 employees

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Cons

SageMaker isn't available in all regions. This is complicated for some clients overseas.
For larger instances, when using a GPU, it takes a while to talk to a customer service representative to ask for a limit increase. Given this, it's recommendable to ask in advance for a limit increase in more expensive and larger cases; otherwise, SageMaker will set the limit to zero by default.
Since the data has to be stored in S3 and copied to training, it doesn't allow to test and debug locally. Therefore, we have to wait a lot to check everything after every trail.

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Mauricio Quiroga-Pascal Ortega

CEO

DisperSuranceComputer Software, 51-200 employees

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The navigation through which one would create a workspace is a bit confusing at first. It takes a couple minutes to figure out how to create a folder and upload files since it is not the same as traditional file systems such as box.com
Also, when you create a table, if you forgot to copy the link where the table is stored, it is hard to relocate it. Most of the time I would have to delete the table and re-created.

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Ann Le

Freelance Translator

ZOO Digital Group plcEntertainment, 501-1000 employees

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Usability

No score

No answers yet

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Databricks Lakehouse Platform 9.0

Based on 1 answer

This has been very useful in my organization for shared notebooks, integrated data pipeline automation and data sources integrations. Integration with AWS is seamless. Non tech users can easily learn how to use Databricks. You can have your company LDAP connect to it for login based access controls to some extent

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Verified User

Team Lead in Engineering

Financial Services Company, 10,001+ employees

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Alternatives Considered

Microsoft Azure is better than Amazon Tensor Flow because it provides easier and pre-built capabilities such as Anomaly Detection, Recommendation, and Ranking.AWS is better than IBM Watson ML Studio because it has direct and prebuilt clustering capabilities AWS, like IBM Watson ML Studio, has powerful built-in algorithms, providing a stronger platform when comparing it with MS Azure ML Services and Google ML Engine.

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Mauricio Quiroga-Pascal Ortega

CEO

DisperSuranceComputer Software, 51-200 employees

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Easier to set up and get started. Less of a learning curve.

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Verified User

Director in Engineering

Financial Services Company, 10,001+ employees

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Return on Investment

Positive: It has allowed us to work with our overseas teams without any large hardware investing.
Positive: Pre-trained models significantly reduce the time to develop solutions for our clients.
Negative: Since it's a relatively new tool, you have to be careful about not paying for large errors while learning to use the tool.

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Mauricio Quiroga-Pascal Ortega

CEO

DisperSuranceComputer Software, 51-200 employees

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Rapid growth of analytics within our company.
Cost model aligns with usage allowing us to make a reasonable initial investment and scale the cost as we realize the value.
Platform is easy to learn and Databricks provides excellent support and training.
Platform does not require a large DevOPs investment

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Verified User

Strategist in Engineering

Computer Hardware Company, 10,001+ employees

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Pricing Details

General

Free Trial

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Free/Freemium Version

—

Premium Consulting/Integration Services

—

Entry-level set up fee?

No

Amazon Tensor Flow Editions & Modules

—

Additional Pricing Details

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General

Free Trial

—

Free/Freemium Version

—

Premium Consulting/Integration Services

—

Entry-level set up fee?

No

Databricks Lakehouse Platform Editions & Modules

Edition

Standard	$0.07¹
Premium	$0.10¹
Enterprise	$0.13¹

Per DBU

Additional Pricing Details

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Rating Summary

Amazon Tensor Flow

Databricks Lakehouse Platform

Amazon Tensor Flow

Databricks Lakehouse Platform

Likelihood to Recommend