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 …
Continue reading

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 …
Continue reading

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 …
Continue reading

Databricks Review

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

[It's] Used by self-service analysts to quickly do analysis
Continue reading

Reviewer Pros & Cons

View all pros & cons

Video Reviews

Leaving a video review helps other professionals like you evaluate products. Be the first one in your network to record a review of Databricks Lakehouse Platform, and make your voice heard!

Pricing

View all pricing

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

View all alternatives

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)
Companies can't remove reviews or game the system. Here's why
Score 8 out of 10
Vetted Review
Verified User
Review Source
If you need a managed big data megastore, which has native integration with highly optimized Apache Spark Engine and native integration with MLflow, go for Databricks Lakehouse Platform. The Databricks Lakehouse Platform is a breeze to use and analytics capabilities are supported out of the box. You will find it a bit difficult to manage code in notebooks but you will get used to it soon.
February 08, 2022

Best in the industry

Jonatan Bouchard | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Currently the best Data Science tool for a large-scale company that needs strong tech support once and a while. The performance and the connectivity/integration with a large bread of tools and platform is also important when you don't want to change all your stack. DataBricks is a great non-drage and drops tool for real Data Scientist that knows their things.
Score 8 out of 10
Vetted Review
Verified User
Review Source
I reckon is an amazing platform for users with a certain level of expertise for designing experiments and delivering a deep dive analysis that requires execution of highly complex queries, also it is very useful when it comes to cross company shared workspaces for unified comprehension of the data.

it is less appropriate for users who don't have full knowledge of the tables they are going to query on and need more support on the data, since the platform doesn't give an option to see what are the fields in a table before even querying it
Score 9 out of 10
Vetted Review
Verified User
Review Source
Databricks is great for beginner as well as advanced coders. The interface is extremely user-friendly and the learning curve is quite short. It is well suited for automation where we can have scripts running late at night when the load is less and wake up to an email notification of success or failure. It is also well suited for writing codes that require the use of multiple languages (in some cases of data modeling)

The ability to store temporary/permanent tables on data lakes is a fabulous feature as well. PySpark is an excellent language to learn and it works really fast with large datasets.
July 12, 2021

Data for insights

Score 7 out of 10
Vetted Review
Verified User
Review Source
[Databricks Lakehouse Platform (Unified Analytics Platform)] makes the power of Spark accessible. Databricks's proactive and customer-centric service. It is a highly adaptable solution for data engineering, data science, and AI. Load times are not consistent and no ability to restrict data access to specific users or groups.
Surendranatha Reddy Chappidi | TrustRadius Reviewer
Score 9 out of 10
Vetted Review
Verified User
Review Source
Databricks Lakehouse platform is well suited for below use cases :
1. Process different types of data sources like structured data, semi structured data and unstructured data.
2. Process data different data sources like RDBMS, REST APIs, File servers, IoT sensors.
3. Provide support for Updates, Deletes, schema evaluation

Databricks Lakehouse platform is not well suited for below usecases :
1. Less data volume and doesn't have analytics requirements
2. Developers doesn't have skill set on spark and Hive


Score 9 out of 10
Vetted Review
Verified User
Review Source
Databricks Lakehouse Platform (Unified Analytics Platform) can be used to process raw data from any system like IoT, structured, and unstructured data sources. Since it supports Pyspark and Scala to do data processing, it can do any complex business transformation very easily. Also, the Databricks Lakehouse Platform (Unified Analytics Platform) architecture is very similar to Big Data; it can process huge datasets from Hadoop systems and machine learning models in minutes.
Score 8 out of 10
Vetted Review
Verified User
Review Source
It is great for both ad-hoc analyzes and scheduled jobs. It supports most of the cloud storage technologies and provides an easy to use API to connect with them. Clusters can be auto scaled with the load, and you can also create temporary clusters for job runs, which cost less compared to all purpose clusters.
Score 9 out of 10
Vetted Review
Verified User
Review Source
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.
Score 9 out of 10
Vetted Review
Verified User
Review Source
Great end to end analytics solution on AWS or Azure. Databricks continues to grow based on customer feedback. Just like everyone in the industry, they are focused on Machine Learning, but they also understand a complete solution is needed.
Ann Le | TrustRadius Reviewer
Score 7 out of 10
Vetted Review
Verified User
Review Source
Right now, I am learning about Spark ML and general machine learning concepts. It is a good practice space to run different Spark ML codes. Databricks does provide valid errors and detailed descriptions of where I can fix my code. And to run a set of codes is very easy to maneuver around. If you do not know how to code, it might be less appropriate to use Databricks. But then again, they do have a large community where help can be found.
September 15, 2017

Databricks Review

Score 6 out of 10
Vetted Review
Verified User
Review Source
When you have analysts that are not cloud-savvy, this tool helps them quickly run code and not be overwhelmed by infrastructure and optimization. [It's] Less appropriate in production deployments.