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Databricks

Databricks Data Intelligence Platform

Composite Score

8.7 /10

CX Score

9.1 /10

75 Reviews Write a Review

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What is Databricks Data Intelligence Platform?

The Databricks Data Intelligence Platform allows your entire organization to use data and AI. It’s built on a lakehouse to provide an open, unified foundation for all data and governance, and is powered by a Data Intelligence Engine that understands the uniqueness of your data. From ETL to data warehousing to generative AI, Databricks helps you simplify and accelerate your data and AI goals.

Company Details

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Awards & Recognition

Databricks Data Intelligence Platform won the following awards in the Machine Learning Platforms category

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2026 Data Quadrant Champion

Latest Award, 2026

0 Top Rated Capabilities

1 Top Rated Features

Latest Award, 2026

2025 Emotional Footprint Champion

2025

Strategy and Innovation
Contract Negotiation
Conflict Resolution

2025

2025 Data Quadrant Champion

2025

4 Top Rated Capabilities

4 Top Rated Features

2025

Service Experience
Product Impact
Contract Negotiation

2024

2024 Data Quadrant Champion

2024

3 Top Rated Capabilities

8 Top Rated Features

2024

2023 Emotional Footprint Champion

2023

Strategy and Innovation
Service Experience
Product Impact
Contract Negotiation
Conflict Resolution

2023

2023 Data Quadrant Champion

2023

3 Top Rated Capabilities

4 Top Rated Features

2023

2022 Emotional Footprint Champion

2022

Strategy and Innovation
Service Experience
Product Impact
Contract Negotiation
Conflict Resolution

2022

2022 Data Quadrant Champion

2022

2 Top Rated Capabilities

2 Top Rated Features

2022

Databricks Data Intelligence Platform Ratings

Real user data aggregated to summarize the product performance and customer experience.
Download the entire Product Scorecard to access more information on Databricks Data Intelligence Platform.

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

87 Likeliness to Recommend

1
Since last award

96 Plan to Renew

77 Satisfaction of Cost Relative to Value

1
Since last award

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Emotional Footprint Overview

Product scores listed below represent current data. This may be different from data contained in reports and awards, which express data as of their publication date.

+97 Net Emotional Footprint

The emotional sentiment held by end users of the software based on their experience with the vendor. Responses are captured on an eight-point scale.

How much do users love Databricks Data Intelligence Platform?

0% Negative

4% Neutral

96% Positive

Pros

Continually Improving Product
Friendly Negotiation
Helps Innovate
Reliable

Feature Ratings

Average 80

Model Training

Performance and Scalability

Data Pre-Processing

Model Tuning

Feature Engineering

Openness and Flexibility

Pre-Packaged AI/ML Services

Data Ingestion

Data Exploration and Visualization

Data Labeling

Algorithm Diversity

Vendor Capability Ratings

Average 79

Breadth of Features

Ease of Data Integration

Product Strategy and Rate of Improvement

Ease of Implementation

Quality of Features

Availability and Quality of Training

Ease of IT Administration

Business Value Created

Usability and Intuitiveness

Vendor Support

Ease of Customization

Also Featured in...

Data Preparation for Analytics

Score 8.8

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Analytical Data Store

Score 8.5

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Retail Analytics

Score 7.9

View Databricks Data Intelligence Platform in this category (opens in a new tab)

Databricks Data Intelligence Platform Reviews

Rintu K.

Role: Information Technology
Industry: Technology
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Aug 2026

A Strong Platform for Managing Data at Scale

Likeliness to Recommend

10 /10

What differentiates Databricks Data Intelligence Platform from other similar products?

When you have a data team spread across various projects, one of the benefits of Databricks is the combination of data engineering, analytics, and machine learning tools. As a team lead, I find it convenient to have a single place for engineers and analysts to access the same data in a way that eliminates duplicate workflows.

What is your favorite aspect of this product?

Working with large amounts of data and creating analytics workflows is something that's very valuable to me. It allows the team flexibility for data preparation, analysis, and more advanced workloads without having to constantly move data between tool.

What do you dislike most about this product?

There’s a learning curve involved for things like advanced features and platform configuration. It’s also important to watch workloads and consumption as cost can rise over time.

What recommendations would you give to someone considering this product?

I would recommend Databricks to those who have data pipelines which handle large volumes, or datasets with some complexity. Setting up some of the standards around notebooks, pipelines, access, and workload management upfront will make onboarding other members of your data team a lot easier in the future, as the platform grows with your team.

Pros

Continually Improving Product
Inspires Innovation
Saves Time
Fair

Sikha S.

Role: Information Technology
Industry: Other
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Mar 2026

centralized management of team based development

Likeliness to Recommend

10 /10

What differentiates Databricks Data Intelligence Platform from other similar products?

centralized management of team based development and experiments through collaborative notebooks

What is your favorite aspect of this product?

strong support for ml flow for end to end ml lifecycle management

What do you dislike most about this product?

limited customization options for some advanced configurations

What recommendations would you give to someone considering this product?

if your team is familiar with the ml flow and spark ecosystems adoption will be more effective

Pros

Helps Innovate
Reliable
Trustworthy
Effective Service

Ayushi T.

Role: Information Technology
Industry: Government
Involvement: End User of Application

Validated Review

Verified Reviewer

Submitted Mar 2026

unified platform for end to end data science

Likeliness to Recommend

10 /10

What differentiates Databricks Data Intelligence Platform from other similar products?

apache spark based distributed processing which handles large scale data efficiently.

What is your favorite aspect of this product?

excellent for scalable machine learning and feature engineering on large data sets.

What do you dislike most about this product?

cost management can be challenging especially when using large clusters.

What recommendations would you give to someone considering this product?

adopt it as the primary platform for large scale data and machine learning projects.

Pros

Reliable
Enables Productivity
Efficient Service
Effective Service

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Databricks Data Intelligence Platform

What is Databricks Data Intelligence Platform?

Company Details

Need Assistance?

Awards & Recognition

2026 Data Quadrant Champion

2025 Emotional Footprint Champion

2025 Data Quadrant Champion

2024 Data Quadrant Champion

2023 Emotional Footprint Champion

2023 Data Quadrant Champion

2022 Emotional Footprint Champion

2022 Data Quadrant Champion

Databricks Data Intelligence Platform Ratings

87 Likeliness to Recommend

1 Since last award

96 Plan to Renew

77 Satisfaction of Cost Relative to Value

1 Since last award

Emotional Footprint Overview

+97 Net Emotional Footprint

How much do users love Databricks Data Intelligence Platform?

Pros

Feature Ratings Average rating of all product features, some of which may not be shown below

Model Training Ability to train a model with a range of data sets that you provide.

Performance and Scalability Systems performance (time) and scalability end-to-end: from data load and processing to model creation and deployment.

Data Pre-Processing Ability to wrangle or pre-process data to ready it for model training.

Model Tuning Ability to fine-tune models based on monitoring feedback within the platform.

Feature Engineering Ability to extract and build features from raw data.

Openness and Flexibility Integration with open-source and 3P resources (languages, frameworks, libraries, etc.)

Pre-Packaged AI/ML Services Pre-trained models and associated resources, e.g., for forecasting, recommendations, image and video analysis, NLP, voice, etc.

Data Ingestion Ability to access data from various sources on-prem and in the cloud and of various type: structured, unstructured, and semi-structured, real-time, etc.

Data Exploration and Visualization Ability to visually manipulate, interact with, and explore data, and perform basic descriptive statistics and pattern detection.

Data Labeling Ability to annotate/label data to create a training data set.

Algorithm Diversity Provides multiple diverse ready-to-use machine learning algorithms out-of-the-box.

Vendor Capability Ratings Average rating of all vendor capabilities, some of which may not be shown below

Breadth of Features Users prefer feature rich software that enables them to perform diverse series of tasks. This data expresses user satisfaction with the product's breadth of features.

Ease of Data Integration Use this data to determine whether the product will cause headaches or make data integration easy.

Product Strategy and Rate of Improvement Vendors who don't stay on top of emerging needs and trends won't enable you to meet your business goals. Use this data to separate innovators from imposters.

Ease of Implementation Successfully implementing new software is necessary to realize its full value and promote end user adoption. This data indicates whether or not the product is easy to implement.

Quality of Features Feature quality is just as important as quantity. Use this data to determine if this product will do what you're purchasing it to do, easily, intuitively, reliably, and effectively.

Availability and Quality of Training Effective and readily available training enables users to get the most out of the software you've chosen. Use this section to make sure your vendor's training programs and materials measure up.

Ease of IT Administration This data indicates whether IT personnel will be able to resolve issues and perform configurations efficiently and effectively.

Business Value Created Software needs to create value for employees, customers, partners, and, ultimately, shareholders. This data expresses user satisfaction - or lack thereof - with the product's business value.

Usability and Intuitiveness End user learning curves cost the organization money. Pay attention to your end users' technical ability to determine how important UX is in your purchase.

Vendor Support The importance of vendor support will vary for each organization depending on internal capabilities, but there will always be issues that only the vendor can resolve.

Ease of Customization Don't get bogged down in a difficult customization; use this data to make sure you can easily achieve the functionality you need for your particular situation.

Also Featured in...

Data Preparation for Analytics

Analytical Data Store

Retail Analytics

Databricks Data Intelligence Platform Reviews

Rintu K.

A Strong Platform for Managing Data at Scale

Likeliness to Recommend

Pros

Sikha S.

centralized management of team based development

Likeliness to Recommend

Pros

Ayushi T.

unified platform for end to end data science

Likeliness to Recommend

Pros

Most Popular Databricks Data Intelligence Platform Comparisons

Gemini Enterprise Agent Platform

MathWorks MATLAB

Microsoft Azure Machine Learning

AWS Machine Learning

Altair RapidMiner

DataRobot AI Platform

1
Since last award

1
Since last award

Feature Ratings

Model Training

Performance and Scalability

Data Pre-Processing

Model Tuning

Feature Engineering

Openness and Flexibility

Pre-Packaged AI/ML Services

Data Ingestion

Data Exploration and Visualization

Data Labeling

Algorithm Diversity

Vendor Capability Ratings

Breadth of Features

Ease of Data Integration

Product Strategy and Rate of Improvement

Ease of Implementation

Quality of Features

Availability and Quality of Training

Ease of IT Administration

Business Value Created

Usability and Intuitiveness

Vendor Support

Ease of Customization