Sold by: Databricks, Inc.
Deployed on AWS
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The Databricks Data Intelligence Platform unlocks the power of data and AI for your entire organization. Enjoy up to $400 in usage credits during your 14-day free trial. Cancel anytime. After your trial ends, you will automatically be enrolled into a Databricks pay-as-you-go plan.
4.6
Overview
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Databricks at AWS re:Invent 2024
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Get started today with up to $400 in usage credits during your 14-day free trial. Trial ends the earlier of when credits are consumed or the 14-day period expires. After your trial ends, you will be automatically enrolled into a Databricks pay-as-you-go plan using the payment method associated with your AWS Marketplace account, paying only for what you use and you can cancel anytime. You can view the full per-product rates for Databricks Units (DBUs) at https://www.databricks.com/product/pricing
The Databricks Data Intelligence Platform allows your entire organization to use data and AI. Its built on a lakehouse to provide an open, unified foundation for all your data and governance. And its powered by a Data Intelligence Engine that speaks the language of your organization so anyone can access the data and insights they need.
The Data Intelligence Platform simplifies your modern data stack by eliminating the data silos that traditionally separate and complicate data engineering, analytics, BI, data science and machine learning. Databricks is built on open source and open standards to maximize flexibility. And the platforms common approach to data management, security and governance helps you operate more efficiently and innovate faster across all analytics use cases.
Reach out to sales@databricks.com to get specialized configurations and pricing for Databricks on AWS Marketplace on a contract basis.
** Technical Support: For help setting up your account, connecting to data, or exploring the platform please reach out to awsmp-onboarding-help@databricks.com **
Highlights
- Simple: Databricks provides a simplified data architecture by unifying data, analytics and AI workloads on one common platform running on Amazon S3.
- Open: Built on top of the world's most successful open source data projects, the Lakehouse Platform unifies your data ecosystem with open standards and formats.
- Collaborative: With native collaboration capabilities, the Databricks Lakehouse Platform unifies data teams to collaborate across the entire data and AI workflow.
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Dimension | Cost/unit |
|---|---|
Databricks Consumption Units | $1.00 |
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Vendor support
Please reach out to sales@databricks.com with any questions or for options on contract or pricing terms.
Technical Support: For help setting up your account, connecting to data, or exploring the platform please reach out to awsmp-onboarding-help@databricks.com
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Updated weekly
By Databricks, Inc.
By Cloudera
Top
10
In Databases & Analytics Platforms, ML Solutions, Data Analytics
Top
10
In ML Solutions
Top
10
In Data Analysis
Sentiment is AI generated from actual customer reviews on AWS and G2
Functionality
Ease of use
Customer service
Cost effectiveness
Positive reviews
Mixed reviews
Negative reviews
AI generated from product descriptions
Lakehouse Architecture
Built on a lakehouse foundation providing unified data storage and governance across data engineering, analytics, BI, data science, and machine learning workloads
Open Source Integration
Constructed on open source data projects and open standards to maximize flexibility and interoperability across the data ecosystem
Data Intelligence Engine
Powered by a Data Intelligence Engine that enables organizational access to data and insights across diverse user roles and technical skill levels
Unified Data Platform
Consolidates data, analytics, and AI workloads on a single common platform running on Amazon S3, eliminating traditional data silos
Collaborative Capabilities
Provides native collaboration features enabling data teams to work together across the entire data and AI workflow
AWS Data Source Integration
Secure connectivity to Amazon S3, Amazon Redshift, and Amazon RDS with push-down computation capabilities.
Elastic Compute Scaling
Distributed data and machine learning processing powered by Amazon EKS supporting Python, R, Spark, and additional frameworks.
AWS AI Service Integration
Pre-built workflows integrating AWS AI services including Amazon SageMaker and Amazon Comprehend for accelerated AI development.
Large Language Model Connectivity
LLM Mesh capability enabling connections to Amazon Bedrock for Chat, Retrieval-Augmented Generation (RAG), and Agentic workflows.
Visual Analytics and ML Interface
Low-code visual platform for data preparation, pipeline creation, and machine learning model development accessible to both technical and non-technical users.
Workload Auto-scaling
Intelligently autoscales workloads up and down across hybrid and public cloud environments for optimized cloud infrastructure utilization.
Multi-function Analytics Platform
Provides integrated data warehouse, machine learning, and custom analytics capabilities with unified analytic functions to eliminate data silos.
Shared Data Experience (SDX)
Implements security and governance policies that are set once and applied consistently across all data and workloads, with portability across supported infrastructures.
Data Lifecycle Management
Manages complete data lifecycle functions including ingestion, transformation, querying, optimization, and predictive analytics across multiple cloud environments.
Unified Security and Governance
Ensures all workloads share common security, governance, and metadata with capabilities for data discovery, curation, and self-service access controls.
Standard contract
No
No
No
Customer reviews
Oscar Estorach
Data lakehouse has powered faster analytics and has simplified collaboration for big data projects
Reviewed on Jun 16, 2026
Review provided by PeerSpot
What is our primary use case?
I did not work with Apache, particularly Keeper for Apache Kafka , but I started to work maybe five years ago and right now I don't do anything there.
In the last year, I did not work with Kafka. Currently, I am an architect, data architect, but I don't implement anything with Kafka or Confluence ; it's a commercial product in the cloud.
I don't know the name of the product I am currently working with. I was working with Databricks , Snowflake , and DBT.
I prefer working with Microsoft Azure cloud because I need time for me and don't need to learn more clouds. For example, in a project, I am working with Snowflake into Azure and into Amazon, but I don't touch any technological stack or any services into Amazon; I only work with Azure.
What is most valuable?
I have been familiar with Databricks Data Intelligence Platform for maybe eight years.
Databricks Data Intelligence Platform is a cloud solution and a business solution, but the origin is Spark. Apache Spark is the base of Databricks Data Intelligence Platform, and the same person who created Spark now has the company Databricks , so all is in the cloud.
Databricks Data Intelligence Platform is faster and easier to work with for deploying the Medallion Lakehouse, while Fabric from Microsoft is better for Business Intelligence ; combining them makes for great solutions.
Delta Lake is a great solution from Databricks; it's an open-source system but deployed by Databricks, and I think it's the standard for Microsoft.
I use the collaboration feature in Databricks Data Intelligence Platform, which is great collaboration, but I don't understand exactly what feature you mean by collaborate.
What needs improvement?
I need to pass my lesser two certificates because I study a lot and started to work, but I can't finish my official certificates; I have only two and needed more.
I don't know what features I would like to see in future updates because they innovate too much.
Pricing is something that could always be improved.
For how long have I used the solution?
I have been using Databricks Data Intelligence Platform for maybe eight years.
How was the initial setup?
The deployment of Databricks Data Intelligence Platform is not always easy; it depends on what your problem is. You need to have some experience because it's easier in the cloud, but it's more difficult to implement on-premise like Spark. You need to know what things you want to do there, configure clusters, and use services specific for the corresponding problem or the solution for the customer.
If you only use Databricks Data Intelligence Platform for implementing simple systems, it's easy and takes hours. But if you need to implement governance and Unity Catalog or use BI AI for deploying natural language dashboards for business insights, it can take more time. You can start working in two days or three days with the right help from IT.
What about the implementation team?
I participated in deployment in the configuration for eight years of experience with Databricks Data Intelligence Platform.
What other advice do I have?
There is a lot of information and support available now; you have access to many courses and support, so don't worry.
For creating big data with AI, I rate Databricks Data Intelligence Platform very high, maybe a nine or eight. I would rate my overall experience with this product a 9.
Matthew Z.
Scales to Production Pipelines in Minutes
Reviewed on Jun 13, 2026
Review provided by G2
What do you like best about the product?
The way you scale up to production level pipelines in a mater of minutes.
What do you dislike about the product?
The lack of status between job dependencies. Some other products are ahead in the scheduling and dependency department.
What problems is the product solving and how is that benefiting you?
Connecting non technical people with the data our tech teams are producing.
Financial Services
Flexible Spark SQL/PySpark Development with an Excellent AI Assistant
Reviewed on Jun 12, 2026
Review provided by G2
What do you like best about the product?
I really appreciate the ease and flexibility of development using Spark SQL or PySpark. We use a mix of the two across different team members. The AI assistant is also excellent. It really helps enable users who are not very technical to still get questions answered and search through data.
What do you dislike about the product?
The BI side. We build analytics products from the ingestion of raw data to visualizations and we use a different BI tool. I know Databricks is not necessarily a BI tool but if they could compete with a Quicksight/DOMO/Tableau in this area we would use Databricks for everything.
What problems is the product solving and how is that benefiting you?
Databricks is our primary data platform tool in terms of all of the data work we do.
Arturo S.
Super Easy Setup and Data Source Integration for Quick ROI
Reviewed on Jun 11, 2026
Review provided by G2
What do you like best about the product?
Setting up and integration into data sources was super easy… allowed for quick ROI
What do you dislike about the product?
So far my interactions with the Databricks platform have been positive
What problems is the product solving and how is that benefiting you?
Connecting various data sources into a single platform to then consume
Konjengbam M.
Powerful Lakehouse for Big Data, Collaboration, and Efficient Pipelines
Reviewed on Jun 11, 2026
Review provided by G2
What do you like best about the product?
I love this platform for its capability to handle big pool of data efficiently. I love the idea of Data Lakehouse of this platform. The Collaborative work supported by this platform greatly enhances productivity and team work. In our context of using the Databricks SQL query we can easily identify the best match of entrepreneurs needed to avail a specific scheme. This saves both time and increases efficiency. The capacity of this platform to analyze the past performance and trends enable us to map out a highly targeted approach that is really efficient. I love the capability of this platform as it can identify probable stress asset which might be an issue for future investment. This makes us rethink our strategy and approach towards the best and efficient way forward. I feel that the platform have a user friendly interface. I love the integration ability of this platform as it integrates with most of the major platform. This makes this platform more robust and powerful. The onboarding of this platform is also easy as we could easily login with our Google ID. Other than this I love the ability of this platform to create pipeline. The capability of this platform to create agents also ease up tasks. It also enhances capability to handle work load more effectively and efficiently.
What do you dislike about the product?
I love most part of this platform but I have to admit that user will need a some training for the user to be more efficient. I feel that having a more technical experience will adapt well with this platform. I also wish that the pricing of this platform was also more moderate. Frankly saying the accuracy of output of this platform depends on how clean the Data is . So there is always a chance of spilling in the unclean data. This will directly impact the result.
What problems is the product solving and how is that benefiting you?
Frankly this platform translates into assistance in accurate decision making. The decision made by using this platform directly impacts productivity and averts risk.