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.
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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
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In ML Solutions
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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
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Customer reviews
Shweta D.
Powerful Lakehouse Platform for Scalable Pipelines and Collaboration
Reviewed on May 06, 2026
Review provided by G2
What do you like best about the product?
in my role i focus on designing scalable and future ready data platform, and databricks has become a key part of that architecture i have used it across multiple project for building data pipelines, enabling analytics, and support data science teams. what stand out it brings engineering, analytics and machine learning into one platform, which simplifies overall data architecture. the biggest strength is the lakehouse approach ., it combines the flexibility of a data lake with the reliability of a data ware house, this helps to avoid maintaining separate system for storage and analytics, i also like how well it handles large scale processing using spark, whether its batch or steaming data, it performs consistently when configured properly. collaboration is another strong point, teams can work together in notebooks, share logic, and reuse code easily, which improves productivity across departments. the UI is designed for well, notebooks are clean and flexible and switching between SQL , python and scala is smooth. it integrates well with AWS , Azure and GCP and Airflow. performance is strong for large scale workloads . the AI features like Genie is very useful.
What do you dislike about the product?
the biggest concern is cost control, if clusters are not managed properly or left running longer than needed, cost can increases faster than expected, auto scaling is helpful but without monitoring , it can still lead to higher usage. sometimes starting cluster can take time, especially when you just want to run quick tests or small jobs, this can slow down development and reduce productivity during short tasks. when something fails in a pipeline or job, debugging is not always easy, logs can be detailed, but tracing the exact issues in complex workflows can take time.
What problems is the product solving and how is that benefiting you?
it mainly solves the problem of handling large scale data processing and unifying different data workloads in one platform, earlier building and maintaining ETL pipelines require multiple tools and a lot of manual effort, with databricks i can build, run and manage pipelines in one place using spark, which simplifies the overall process, processing big datasets used to require heavy infrastructure setup, databricks handles this using distributed computing, so i can process large amount of data efficiently without worrying about scaling manually.in traditional setup, we needed separate tools for data engineering , analytics , machine learning, it brings all this into one platform, with shared notebooks and a unified workspace, team can collaborate more easily share code, and work on the same data.
Computer Software
Straightforward SQL, Smooth Workflow Scheduling, and a Handy Notebook Feature
Reviewed on May 02, 2026
Review provided by G2
What do you like best about the product?
It’s straightforward to write and run SQL, schedule workflows, and I especially like the notebook feature. Genie AI is helpful for diagnosing bugs, and it can also answer ad hoc questions whenever I need it.
What do you dislike about the product?
Genie’s AI feature could still use some improvement. It sometimes takes a long time to respond, and with more complex problems it doesn’t always handle them well.
What problems is the product solving and how is that benefiting you?
The workflow is very easy to schedule. It’s also simple to set up alerts, and the visualization makes it easy for me to modify and debug.
Shreeram P.
Solves Developers’ Problems with Genie, Lakeflow Connect, and DLT
Reviewed on Apr 30, 2026
Review provided by G2
What do you like best about the product?
This platform solves developers’ problems by offering features like Genie, Lakeflow Connect, and DLT.
What do you dislike about the product?
Before using it, I want to understand the compute and charges, and how to use it properly. Basically, I need to learn a lot first.
What problems is the product solving and how is that benefiting you?
It solved our data pipeline and dashboard creation challenges. With SDP and AI/BI Genie, we moved from manually managing the data pipeline to simply declaring it in SQL and having everything handled for us. Instead of spending so much time building dashboards, we can now just ask questions in natural language and get the answers we need without wasting a lot of time.
Arif V.
Intuitive UI and AI-Powered Experience That Keeps Getting Better
Reviewed on Apr 29, 2026
Review provided by G2
What do you like best about the product?
The UI is pretty intuitive and they are using ai to make the experience even better
What do you dislike about the product?
For the most part, it’s a great platform, but some of the debugging options could be improved.
What problems is the product solving and how is that benefiting you?
I use it to write queries for extracting data and running experiments, mostly with SQL and Python.
Antonio V.
Scalable, All-in-One Environment with Some Learning Curve
Reviewed on Apr 28, 2026
Review provided by G2
What do you like best about the product?
I like Databricks for its scalability and all-in-one environment for data engineering, analytics, and machine learning. It allows me to process large datasets efficiently while keeping workflows organized in one platform. The scalability is very valuable because it lets me handle growing data volumes and complex workloads without performance issues. As projects expand, the platform can scale resources efficiently.
What do you dislike about the product?
Some features can have a learning curve, especially for new users working with advanced configurations or cluster management. The interface could also be more intuitive in certain areas. The setup was relatively smooth for core features, but some advanced settings like cluster optimization, permissions, and integrations required more time and technical knowledge.
What problems is the product solving and how is that benefiting you?
Databricks solves major data management and analytics challenges by efficiently handling large datasets, simplifying ETL processes, and centralizing workflows. Its scalability allows me to manage growing data volumes without performance issues, ensuring resources scale efficiently as projects expand.