Databricks Lakehouse Platform vs Paxata

Likelihood to Recommend

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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Paxata can be highly useful to someone who doesn't like/have any experience with writing codes to treat data before using it as input into BI dashboards. Paxata can accelerate data cleaning in environments where a large amount of unclean data is generated and business decisions on the go are required. It performs really well while dealing with natural language.

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Ipsit Pradhan

Experienced Associate

PwCManagement Consulting, 10,001+ employees

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Pros

It supports all data science programming languages like Python and R
it takes very few minutes to deploy models into production
it has tools that ensures collaborations between developers

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Kofi Joshua

Machine Learning And Deep Learning Mentee

Runmila AI InstituteEducation Management, 201-500 employees

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Visualize distributions in large data sets effectively which enable the user to quickly spot outliers and treat them appropriately
Provides recommendation to merge datasets based on matching column values
The cluster and edit feature in my opinion is its most powerful feature and reduces cardinality in column with text

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Ipsit Pradhan

Experienced Associate

PwCManagement Consulting, 10,001+ employees

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Cons

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.

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Verified User

Director in Information Technology

Financial Services Company, 201-500 employees

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Doesn't provide recommendation on how to impute values
There is a lag quite often
We can say whether a column has errors or quality issues in the first look

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Ipsit Pradhan

Experienced Associate

PwCManagement Consulting, 10,001+ employees

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Usability

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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No score

No answers yet

No answers on this topic

Alternatives Considered

I also use Microsoft Azure Machine Learning in parallel with Databricks. They use different file formats which teach me to be flexible and able to write different programs. They are equally useful to me and I would like to master both platforms for any future usage. I do prefer Databricks because it could be free if I decided to go with the Databricks Community Edition only.

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Ann Le

Freelance Translator

ZOO Digital Group plcEntertainment, 501-1000 employees

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Paxata is a much better tool when it comes to handling natural language but Talend provides recommendations on how to impute missing values and outliers. Paxata provides recommendations on dataset tie-ups and joins but Talend doesn't provide any such recommendations. In paxata you can visualize distribution of data in a column and filter them by dragging and selecting the section you'd like to retain

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Ipsit Pradhan

Experienced Associate

PwCManagement Consulting, 10,001+ employees

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Return on Investment

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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It saves time to clean data
It reduces the requirement of too many data engineer/stewards and hence adds positive impact on the return of the business

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Ipsit Pradhan

Experienced Associate

PwCManagement Consulting, 10,001+ employees

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Pricing Details

General

Free Trial

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Free/Freemium Version

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Premium Consulting/Integration Services

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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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General

Free Trial

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Free/Freemium Version

—

Premium Consulting/Integration Services

—

Entry-level set up fee?

No

Paxata Editions & Modules

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Additional Pricing Details

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