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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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
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 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
mohammad Gufran j.
Unified Analytics Powerhouse with Minor Hiccups
Reviewed on Apr 25, 2026
Review provided by G2
What do you like best about the product?
I like most about Databricks is that it brings data engineering, analytics, and AI workflow into one shared platform, which makes collaboration much easier. It's valuable for working with a large dataset and notebooks, and it helps set up suitable pipelines without the hassle of managing too many separate tools.
What do you dislike about the product?
Cost visibility and resource users can be hard to track, especially as more teams cluster and jobs start using the platform. I also like to sync up permission management. Clear troubleshooting for a job failure and a smoother experience around the workspace governance and configuration. CDC lake flow is always stuck for a last table and not giving a clear picture till now. Serverless logs are sometimes very difficult to track, making it hard to understand the reason for job failures.
What problems is the product solving and how is that benefiting you?
I find Databricks solves handling large-scale data processing and analytics by unifying data engineering, analytics, and AI workflow into one platform. It simplifies collaboration on notebooks and automation workflows, enabling faster work with big datasets using Spark.
Staffing and Recruiting
Seamless, Scalable Unified Platform for Data, Analytics, and ML
Reviewed on Apr 25, 2026
Review provided by G2
What do you like best about the product?
Its unified platform for data engineering, analytics, and machine learning makes workflows seamless and scalable.
What do you dislike about the product?
It can be expensive and has a bit of a learning curve for new users.
What problems is the product solving and how is that benefiting you?
It simplifies big data processing and analytics in one platform, helping me save time and scale workflows efficiently.
Corrado P.
BI and Data Engineering in One Place, with AI-Assistant
Reviewed on Apr 23, 2026
Review provided by G2
What do you like best about the product?
Possibility to combine data warehousing and data lakes into a “lakehouse.” So I can do BI and data engineering all in one place instead of stitching together multiple systems.
Using AI to improve and make faster the SQL writing and execution
Using AI to improve and make faster the SQL writing and execution
What do you dislike about the product?
Unity Catalog is powerful, but setting up fine-grained access control across data, schemas, and workspaces can become tricky, especially in larger organizations. The UX/UI of some parts of the platform feels polished, others less so.
What problems is the product solving and how is that benefiting you?
Databricks is essentially solving fragmentation and inefficiency across the data lifecycle and the benefits come from removing a lot of friction between teams, tools, systems and data.
Yash P.
Unified Platform with Powerful Features, Needs Faster Cluster Startups
Reviewed on Apr 23, 2026
Review provided by G2
What do you like best about the product?
I appreciate how Databricks brought everything onto one unified platform, allowing our teams to collaborate in shared notebooks and ensuring data consistency with Delta Lake's ACID transactions. My favorite feature is Auto Loader, which automatically ingests new data files as they land in cloud storage, saving our team 2-3 hours a week on manual pipeline monitoring. Unity Catalog has been a game changer for us, providing a central place for governance and access control, which before was a mess. The initial setup was straightforward, and we had our first cluster and notebooks connected to S3 within a day, which was impressive given the platform's power. The workspace configuration and cloud integration guides are solid to follow.
What do you dislike about the product?
The cluster startup time is something that still catches us off guard. Cold start can take anywhere from 3-5 minutes, which gets frustrating when you are in the middle of an iterative debugging session and just need to test a quick fix. The cost management also needs some upgrades as currently the billing dashboards are improving but it still takes some digging to pinpoint exactly which job or user is driving up spend.
What problems is the product solving and how is that benefiting you?
I use Databricks to unify our data processing and machine learning, reducing pipeline delivery delays by 40%. It enables team collaboration with consistent data, saving hours with the autoloader, and simplifies governance with Unity Catalog.
Antarix K.
Streamlined Data Processing with Unmatched Speed
Reviewed on Apr 22, 2026
Review provided by G2
What do you like best about the product?
I use Databricks for real-time data ingestion and processing as well as batch processing. I find it easy to use with PySpark, and I appreciate that it serves as a single platform for both real-time and batch processing. The in-memory processing drastically reduces processing time, and working with dataframes makes handling structured data straightforward. I like the fast execution and the ability to clean, massage, and manipulate data all on the same platform. It's also easy to deploy, and I enjoy the smooth CI pipeline with just one click. The initial setup was quite easy, and the product support made it a cakewalk.
What do you dislike about the product?
Databricks should come up with agentic framework integrated, making it a single stop for Data and AI.
What problems is the product solving and how is that benefiting you?
Databricks offers an easy-to-use platform for both realtime and batch processing. It integrates easily with PySpark and supports in-memory processing, significantly reducing processing time. Dataframes make handling structured data simpler.