Sold by: Databricks, Inc.
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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.
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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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Pricing is based on actual usage, with charges varying according to how much you consume. Subscriptions have no end date and may be canceled any time.
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Dimension | Cost/unit |
|---|---|
Databricks Consumption Units | $1.00 |
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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
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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 Service Integration
Secure connectivity to Amazon S3, Amazon Redshift, and Amazon RDS with push-down computation capabilities
Elastic Compute Scaling
Distributed processing powered by Amazon EKS supporting Python, R, Spark, and other frameworks for data and ML workloads
Pre-built AI Workflows
Integration with AWS AI services including Amazon SageMaker and Amazon Comprehend for accelerated AI development
Large Language Model Integration
LLM Mesh connectivity to Amazon Bedrock enabling Chat, RAG, and Agentic workflow capabilities
Visual Development 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
X Z.
Genie’s Quick Updates and Releases Make It a Joy to Use
Reviewed on May 20, 2026
Review provided by G2
What do you like best about the product?
I love Genie and the quick update/release
What do you dislike about the product?
Honestly, I don’t really have anything to dislike, lol.
What problems is the product solving and how is that benefiting you?
We are currently building a semantic layer using Genie and the agent. I like how easy and structured Databricks is to help with that.
Yelnur K.
Databricks in my case: Multiple Integrations, Intuitive UI, and Reliable Performance
Reviewed on May 19, 2026
Review provided by G2
What do you like best about the product?
What I like most about Databricks is its Integrations part. In workplace, we integrate Database within multiple data soucres. Also, I can't complete my review without mentioning UX and UI design, which makes the overall workflow feel intuitive and genuinely user-friendly. When it comes to speed of the processes, it never offended us. It works as expected. Comparitevly from the market pricing, the price of the service is quite reliable for us. There is a help center of the Databricks, if you can't find any answers to your questions, there are specialists that may assist you with your inqurires. As an instance, I can remember the case where we had an issue within exam process, they helped us to solve this problem.
What do you dislike about the product?
From dislikes the ai quality of Genie. Guys it could be improved, especially the reasoning part. Also, I can say the case when we had an issue with exam process. Specialists helped us, but it took us little discomforties. Well,
What problems is the product solving and how is that benefiting you?
In aviation, we utilize this software for data analysis. We automized a lot of processes, which simple workplace tools can not handle. We also, integrate with multiple tools (names which I can not mention for securirty purposes) Particularly, it helps us to analyze passenger demand by route and season. We combine and analyze big datasets using this software. Overall, a good tool. Out team is satisfied.
Leonardo .
Databricks centralizes data, analytics, and AI
Reviewed on May 16, 2026
Review provided by G2
What do you like best about the product?
What I like most about Databricks is how it centralizes data engineering, analytics, and AI in a single platform, which greatly facilitates the workflow on a daily basis. The integration between notebooks, pipelines, and distributed processing makes development faster and more organized, especially in projects with a large volume of data and automations.
Another point that I consider very strong is the experience with Apache Spark, integrated in a simplified way. Even in more complex scenarios, the performance is usually excellent, allowing large-scale data processing with good stability and scalability. This greatly helps in integrations, ETLs, and analyses that, in other solutions, would require much more effort.
Another point that I consider very strong is the experience with Apache Spark, integrated in a simplified way. Even in more complex scenarios, the performance is usually excellent, allowing large-scale data processing with good stability and scalability. This greatly helps in integrations, ETLs, and analyses that, in other solutions, would require much more effort.
What do you dislike about the product?
Although I quite like the platform, some aspects of Databricks can still be challenging. The main one is the cost, especially in environments with intensive processing or when clusters are not well optimized. Without more rigorous usage control, expenses can increase rapidly.
Another aspect is the learning curve, which can be steep for teams that are starting in the distributed data ecosystem. Concepts related to Spark, clusters, optimization, and resource management require time to adapt, especially for those coming from more traditional tools.
In terms of UI/UX, although the interface is generally good, some administrative processes and more advanced configurations can seem confusing at first. In certain scenarios, identifying performance or permission issues may also require more technical knowledge.
Another aspect is the learning curve, which can be steep for teams that are starting in the distributed data ecosystem. Concepts related to Spark, clusters, optimization, and resource management require time to adapt, especially for those coming from more traditional tools.
In terms of UI/UX, although the interface is generally good, some administrative processes and more advanced configurations can seem confusing at first. In certain scenarios, identifying performance or permission issues may also require more technical knowledge.
What problems is the product solving and how is that benefiting you?
Databricks has primarily helped to solve problems related to the centralization, processing, and analysis of large volumes of data. Previously, many processes were distributed among different tools, which made integrations, maintenance, and governance difficult. With Databricks, a large part of the data engineering, analytics, and AI workflow can be concentrated on a single platform, bringing more consistency to daily work.
Wiliam R.
Effortless Data Unification with Databricks and Lakebase
Reviewed on May 15, 2026
Review provided by G2
What do you like best about the product?
I like the ability to unify data engineering with Databricks. The lakehouse feature is especially helpful in my solutions, with persistent memory offering the most value. The initial setup was easy.
What do you dislike about the product?
Databricks could improve governance troubleshooting and simplify operational aspects.
What problems is the product solving and how is that benefiting you?
I use Databricks to build scalable data engineering, AI, and data governance solutions. It helps unify data engineering and supports standards validation, permissions management, and persistent memory.
Puttaraju D.
Streamlined Data Management and Transformation
Reviewed on May 15, 2026
Review provided by G2
What do you like best about the product?
I use Databricks for storing and consuming data. I really like the unified catalog feature, as it helps me manage permissions and access to metadata easily. The ability to publish datamart data to Thoughtspot is beneficial, and I find data transformation using Databricks notebooks particularly helpful. The ease of initial setup with Databricks was great and our team of over 1000 people transitioned smoothly from Hadoop.
What do you dislike about the product?
Table level access. Provision to restrict access at table level is required.
What problems is the product solving and how is that benefiting you?
I use Databricks for storing and consuming data, with a unified catalog for easy access to metadata. The ability to transform and publish datamart data to Thoughtspot is valuable, though I'd like improved table-level access restrictions.