Databricks Lakehouse Platform

Databricks Lakehouse Platform

About TrustRadius Scoring
Score 8.7 out of 100
Databricks Lakehouse Platform (Unified Analytics Platform)

Overview

Recent Reviews

Databricks--a good all-rounder

9 out of 10
May 28, 2021
We use Databricks Lakehouse Platform (Unified Analytics Platform) in our ETL process (data loading) to perform transformations and to …
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Databricks for modern day ETL

9 out of 10
January 31, 2019
Data from APIs is streamed into our One Lake environment. This one lake is S3 on AWS.
Once this raw data is on S3, we use Databricks to …
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Databricks Review

9 out of 10
August 22, 2018
We leverage Databricks (DB) to run Big Data workloads. Primarily we build a Jar and attach to DB. We do not leverage the notebooks except …
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Databricks Review

6 out of 10
September 15, 2017
Across whole organization.

[It's] Used by self-service analysts to quickly do analysis
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Reviewer Pros & Cons

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Pricing

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Standard

$0.07

Cloud
Per DBU

Premium

$0.10

Cloud
Per DBU

Enterprise

$0.13

Cloud
Per DBU

Entry-level set up fee?

  • No setup fee

Offerings

  • Free Trial
  • Free/Freemium Version
  • Premium Consulting / Integration Services

Features Scorecard

No scorecards have been submitted for this product yet..

Product Details

What is Databricks Lakehouse Platform?

Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run all analytic processes in one place, and manage ML models across the full lifecycle. The Machine Learning Runtime (MLR) provides data scientists and ML practitioners with scalable clusters that include popular frameworks, built-in AutoML and optimizations.

Databricks Lakehouse Platform Technical Details

Deployment TypesSaaS
Operating SystemsUnspecified
Mobile ApplicationNo

Comparisons

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Frequently Asked Questions

What is Databricks Lakehouse Platform?

Databricks in San Francisco offers the Databricks Lakehouse Platform (formerly the Unified Analytics Platform), a data science platform and Apache Spark cluster manager. The Databricks Unified Data Service aims to provide a reliable and scalable platform for data pipelines, data lakes, and data platforms. Users can manage full data journey, to ingest, process, store, and expose data throughout an organization. Its Data Science Workspace is a collaborative environment for practitioners to run all analytic processes in one place, and manage ML models across the full lifecycle. The Machine Learning Runtime (MLR) provides data scientists and ML practitioners with scalable clusters that include popular frameworks, built-in AutoML and optimizations.

What is Databricks Lakehouse Platform's best feature?

Reviewers rate Usability highest, with a score of 9.

Who uses Databricks Lakehouse Platform?

The most common users of Databricks Lakehouse Platform are from Enterprises (1,001+ employees) and the Information Technology & Services industry.

Reviews

(1-8 of 8)
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Score 8 out of 10
Vetted Review
Verified User
Review Source
The most important differentiating factor for Databricks Lakehouse Platform from these other platforms is support for ACID transactions and the time travel feature. Also, native integration with managed MLflow is a plus. EMR, Cloudera, and Hortonworks are not as optimized when it comes to Spark Job Execution. Other platforms need to be self-managed, which is another huge hassle.
Score 10 out of 10
Vetted Review
Verified User
Review Source
  • Azure Synapse Analytics (Azure SQL Data Warehouse)
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
Surendranatha Reddy Chappidi | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Databricks provides support for CURD operations by introducing Delta Lake file format.
Cloudera doesn't have support for the same.

Databricks Lakehouse platform provides different interfaces for data analysts, data engineers and data scientists.
Databricks Lakehouse platform provides seamless integration with different cloud platforms like Azure and AWS.
Cost-wise Azure Lakehouse platform is better than Cloudera.
Score 9 out of 10
Vetted Review
Verified User
Review Source
Databricks was picked among other competitors. Closest competition in our organization was H2O.ai and Databricks came out to be more useful for ROI and time to market in our internal research.
We could have used AWS products, however Databricks notebooks and ability to launch clusters directly from notebooks was seen as a very helpful tool for non tech users.
August 22, 2018

Databricks Review

Score 9 out of 10
Vetted Review
Verified User
Review Source
When we started using it, only the notebook experience was mature. However, DB was very helpful giving us direct support to get onto their platform. Really there was little in the way to compare to them at the time. AWS has services but not the same low-cost angle.
Ann Le | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
  • Azure ML
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.