Amazon SageMaker vs Databricks Lakehouse Platform

Likelihood to Recommend

Amazon SageMaker is a great tool for developing machine learning models that take more effort than just point-and-click type of analyses. The software works well with the other tools in the Amazon ecosystem, so if you use Amazon Web Services or are thinking about it, SageMaker would be a great addition. SageMaker is great for consumer insights, predictive analytics, and looking for gems of insight in the massive amounts of data we create. SageMaker is less suitable for analysts who do generally "small" data analyses, and "small" data analyses in today's world can be billions of records.

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Thomas Young

Owner, previous CEO

Econometric StudiosFinancial Services, 11-50 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 SageMaker

Databricks Lakehouse Platform

Connect to Multiple Data Sources

Extend Existing Data Sources

Automatic Data Format Detection

Amazon SageMaker

Databricks Lakehouse Platform

Visualization

Interactive Data Analysis

Amazon SageMaker

Databricks Lakehouse Platform

Interactive Data Cleaning and Enrichment

Data Transformations

Data Encryption

Built-in Processors

Amazon SageMaker

Databricks Lakehouse Platform

Multiple Model Development Languages and Tools

Automated Machine Learning

Single platform for multiple model development

Self-Service Model Delivery

Amazon SageMaker

Databricks Lakehouse Platform

Flexible Model Publishing Options

Security, Governance, and Cost Controls

Pros

Provides enough freedom for experienced data scientists and also for those who just need things done without going much deeper into building models.
Customization and easy to alter and change.
If you already are an Amazon user, you do not need to transition over to another software.

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Verified User

Employee in Human Resources

Real Estate Company, 1001-5000 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

It's very good for the hardcore programmer, but a little bit complex for a data scientist or new hire who does not have a strong programming background.
Most of the popular library and ML frameworks are there, but we still have to depend on them for new releases.

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Verified User

Employee in Research & Development

Computer Software Company, 501-1000 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

Amazon SageMaker took the heavy lifting out of building and creating models. It allowed for our organization to use our current system for integration and essentially added on a feature to help all levels of Data scientists and IT professionals in our department and company as a whole. The training was simple as well.

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Verified User

Professional in Legal

Legal Services Company, 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

We have been able to deliver data products more rapidly because we spend less time building data pipelines and model servers.
We can prototype more rapidly because it is easy to configure notebooks to access AWS resources.
For our use-cases, serving models is less expensive with SageMaker than bespoke servers.

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Gavin Hackeling

Data Scientist

Wonder (AskWonder.com)Research, 11-50 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 SageMaker 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 SageMaker

Databricks Lakehouse Platform

Amazon SageMaker

Databricks Lakehouse Platform

Likelihood to Recommend