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

(1-15 of 15)
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Score 8 out of 10
Vetted Review
Verified User
Review Source
  • Very well optimized Spark Jobs Execution Engine.
  • Time travel in Databricks Lakehouse Platform allows you to version your datasets.
  • Newly integrated Analytics feature allows you to build visualization dashboards.
  • Native integration with managed MLflow service.
  • Running MLflow jobs remotely is extremely cluttered and needs to be simplified.
  • All the runnable code has to stay in Notebooks which are not very production-friendly.
  • File management on DBFS can be improved.
Score 8 out of 10
Vetted Review
Verified User
Review Source
  • Cross company shared workspaces for unified comprehension of the data
  • Combining different languages such as SQL and Python in one single space in order to make data analysis
  • Quick execution of highly complex queries
  • How graphs are created, it requires a certain level of expertise in the platform and it could be more intuitive and user friendly
  • More guidance on the basics, since some of the new users come from different platforms expecting a similar UI
  • An option where all the tables are shown with their respective fields, when a DB is selected for a query
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Scheduling jobs to automate queries
  • User friendly - a new user can easily navigate through SQL/Python queries
  • Options to code in multiple languages (SQL, Python, Scala, R) and easy to switch with the use of the % operator
  • Errors can be difficult to understand at times
  • Session resets automatically at times, which leads to the temporary tables being wiped out from memory
  • Git connections are dicey
  • Very inconsistent with job success/failure notification emails
Surendranatha Reddy Chappidi | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Seamless integration with Azure cloud platform services like Azure Data Lake Storage, Blobstorage , Azure Data Factory, Azure DevOps.
  • Databricks lakehouse platform in backed uses Apache Spark for all the computation to be faster and distributed. It helps to complete data pipelines to process huge amounts [of] big data in lesser time with low cost.
  • Databricks Lakehouse solves the problems data lake, by introducing Delta Lake concept. It provides support for updates, deletes, schema evaluation.
  • Databricks Lakehouse platform can provide better platform for managing, and monitoring the cluster performance, utilization, optimization suggestions. It helps developers to leverage those insights for building better data pipelines.
  • Databricks Lakehouse platform can provide GUI version to create spark jobs by click, drag and drop. That reduces the significant amount of time to develop code.
  • Databricks Lakehouse platform can provide better insights and details regarding the jobs failures and resources consumption
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • 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
  • Databricks should come with a fine grained access control mechanism. If I have tables or views created then access mechanism should be able to restrict access to certain tables or columns based on the logged in user
  • There should be improved graphing and dash boarding provided from within Databricks
  • Better integration with AWS could help me code jobs in Databricks and run them in AWS EMR more easily using better devops pipelines
Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Collaborative Development Environment using Notebooks.
  • Stable and Secure Cloud Development Environment requiring minimum DevOPs support
  • Fast with excellent scalability reduces time to market
  • Open source library support
  • Automation of Machine Learning Development
  • Optimization of GPU usage
August 22, 2018

Databricks Review

Score 9 out of 10
Vetted Review
Verified User
Review Source
  • Extremely Flexible in Data Scenarios
  • Fantastic Performance
  • DB is always updating the system so we can have latest features.
  • 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.
Ann Le | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
  • There is databricks community, which is a free version. It is available for beginners to have an easy start with a big data platform. It does not have every feature of the full version but is still adequate for extremely new coders.
  • There are many resourceful training elements that are available to developers, data scientists, data engineers and other IT professionals to learn Apache Spark.
  • 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.
September 15, 2017

Databricks Review

Score 6 out of 10
Vetted Review
Verified User
Review Source
  • Very simplified infrastructure initialization
  • Seamless and automated optimization of job execution
  • Simple tool to get used to
  • Visualization - Great area of improvement
  • Integration with Git
  • COST