Databricks
JSON twin: https://www.healthaidb.com/software/databricks.json
Company Name
Databricks
Product URL
https://www.databricks.com
Company URL
https://www.databricks.com
Categories
Summary
Databricks offers a unified data and AI platform that enables healthcare organizations to accelerate innovation and improve patient outcomes by integrating and analyzing large-scale data efficiently.
Description
Databricks provides a comprehensive platform for data management, analytics, and AI, facilitating healthcare organizations in unifying fragmented data systems, enhancing data governance, and delivering personalized health insights. The platform supports various use cases, including disease risk prediction, digital pathology classification, and real-world evidence analysis, enabling healthcare providers to make data-driven decisions and improve patient care.
Api Available
yes
Certifications
- SOC 2 Type II
- ISO 27001
- PCI-DSS
- FedRAMP Moderate
- IRAP
- C5
- CCCS Medium (Protected B)
- DoD IL5
- Infosec Registered Assessors Program (IRAP)
- Korean Financial Security Institute (K-FSI)
- UK Cyber Essentials Plus
Company Founding
2013
Company Offices
Compliance
- HIPAA (supports HIPAA workloads / BAA)
- GDPR
- SOC 2
- ISO 27001
- PCI-DSS
- FedRAMP Moderate
- IRAP
- C5
- CCCS Medium (Protected B)
- DoD IL5
- Infosec Registered Assessors Program (IRAP)
- Korean Financial Security Institute (K-FSI)
- UK Cyber Essentials Plus
Customers
Data Residency
Region selection by cloud provider (customer chooses AWS/Azure/GCP region / BYO cloud region)
Data Standards
- FHIR
- HL7 v2
- HL7 FHIR streaming/pipelines (via accelerators)
- CSV
- JSON
- Parquet
- Delta formats
Deployment Model
- SaaS
- cloud-hosted by customer on AWS
- cloud-hosted by customer on Azure
- cloud-hosted by customer on GCP
- hybrid (customer-managed cloud + Databricks control plane)
Features
- Unified Lakehouse (Delta Lake)
- Data engineering (ETL/streaming)
- Data warehousing / Databricks SQL
- Data science workspaces / notebooks
- Machine learning / MLOps
- Generative AI and LLM support
- Data governance (Unity Catalog)
- Delta Sharing (secure data sharing)
- Real-time streaming, event pipelines
- Marketplace & solution accelerators
- Collaborative notebooks and IDE integrations
- Job scheduling and orchestration
Id
SW1201
Integration Partners
- AWS
- Azure
- Google Cloud
- SAP
- MLflow
- Apache Spark
- Delta Lake
Integrations
- Microsoft Azure
- AWS
- Google Cloud Platform
- Datavant
- Ribbon Health
- Avanade
- Synapxe
- Tableau
- Power BI
- Fivetran
- Confluent
- Looker
- Snowflake
- Salesforce
- Redox (healthcare interoperability partner)
- IQVIA (life sciences data partner)
- SAP (partner/cloud integration)
- Partner Connect ecosystem (technology partners)
- Third-party IDEs and BI tools via connectors
Languages Supported
- English
- Spanish
- French
- German
- Italian
- Portuguese
- Dutch
- Russian
- Chinese
- Japanese
- Korean
- Arabic
- Hindi
- Bengali
- Punjabi
- Telugu
- Marathi
- Tamil
- Gujarati
- Malayalam
Last Updated
2025-10-11
License
commercial
Market Segment
Optional Modules
- Databricks Apps (application development)
- MarketPlace / Partner Connect
- Lakehouse-specific solution accelerators
Os Platforms
Pricing Details
Contact vendor for pricing information.
Pricing Model
subscription
Privacy Features
- Business Associate Agreement (BAA) available
- Data anonymization / de-identification tooling (via partners/accelerators)
- Access controls and consent-supporting controls
- Data residency controls
- Data minimization
- User control over data deletion and management
- Zero data retention endpoints from model partners
- Azure OpenAI content filtering
- Evaluation with simulated user interactions to protect against harmful content
- Protection against harmful output
- Data protection in transit and at rest
Product Code
SW1201
Product Name
Databricks
Ratings
- 4.6 out of 5 on G2 ([g2.com](https://www.g2.com/products/databricks-lakehouse-platform/reviews?utm_source=openai))
- 4.6 out of 5 on G2 ([g2.com](https://www.g2.com/products/databricks-data-intelligence-platform/reviews?utm_source=openai))
- 4.6 out of 5 on G2 ([g2.com](https://www.g2.com/sellers/databricks-inc?utm_source=openai))
- Top Healthcare Software Product on G2 ([g2.com](https://www.g2.com/best-software-companies/top-healthcare?utm_source=openai))
Regions Available
Related Urls
Release Year
2013
Security Features
- Encryption at rest and in transit
- Role-based access control (RBAC)
- Single sign-on / SAML / OIDC
- Audit logging / event logs
- Customer-managed keys / KMS integration
- Enhanced security monitoring
- Hardened operating system image
- Automatic cluster updates
- Enforced use of AWS Nitro instance types
- All egress communication uses TLS 1.2 or higher
Specialties
Support Channels
- email
- phone
- chat
- ticketing
- community
- 24x7
System Requirements
AWS, Azure, GCP, or customer-managed cloud infrastructure
Target Users
- clinicians
- nurses
- patients
- admins
- payers
- data scientists
- healthcare providers
- researchers
Training Options
- documentation
- webinars
- live_online
- onsite
- certification
Type
product
User Reviews
- Databricks has made working with massive datasets so much easier for our team. The collaborative notebooks help us share ideas and troubleshoot together, and the platform’s ability to scale means we don’t have to worry about hitting limits. It’s sped up our analytics and machine learning projects, and connecting to different data sources is a breeze.
- I am using Databricks from around 1 year. In a week I use it approx 3-4 days. Everything is integrated, which means I don’t have to switch between multiple tools to do different tasks. It really improves team collaboration. Sharing notebooks and collaborating on models is super easy. This has been great for our team since we often work together on projects and need to see each other's code and progress.
- The best thing about Databricks is that it very easily consolidates data engineering, data science, and analytics – all in one place. Therefore, I can process huge data sets quickly, run very complex machine learning operations, all without switching tools. Collaboration through notebooks with my team in real-time really reduced a lot of back and forth that I used to have.
- Databricks excels in unifying data engineering, analytics, and machine learning in a collaborative, cloud-based environment. Its support for multiple programming languages (Python, SQL, Scala, R) makes it incredibly flexible. The Lakehouse architecture simplifies data management by combining the best of data lakes and data warehouses. The auto-scaling compute clusters, tight integration with tools like MLflow, and powerful notebooks streamline experimentation and production deployment.
- Databricks excels at unifying data engineering, analytics, and AI/ML on a single platform. The Lakehouse architecture bridges the gap between data lakes and warehouses, making it incredibly efficient for managing structured and unstructured data. I especially appreciate the seamless integration with Apache Spark, robust notebook support for collaborative development, and the simplicity of Delta Lake for versioned data storage. Features like AutoML and Unity Catalog bring governance and intelligence together, making it easier to scale analytics securely and reliably.
- Databricks excels at unifying data engineering, analytics, and machine learning into one seamless platform. What I like best is how effortlessly it handles massive data volumes while enabling collaborative development through notebooks. The integration with Apache Spark and the ability to run scalable workloads with ML, SQL, and Python side-by-side makes it a powerhouse for data-driven teams. Its governance and Delta Lake architecture also ensure reliability and security across the data pipeline.
- Databricks unifies majority of engineering platforms. Last but not the least, Databricks Notebook concept is awesome. In the same Databricks notebook, one can have code in multiple languages (Python, Scala, SQL etc.) and each can be flipped at runtime. Built-in collaborative notebooks with support for multiple languages (Python, Scala, SQL, R) and real-time co-authoring make it easier for teams to iterate together quickly.
- I love Databricks. What I like best about Databricks is its seamless integration of big data processing and AI. The notebook-based interface makes collaboration easy, and the use of Spark ensures fast performance. Delta Lake also provides reliable data versioning and management, which is extremely helpful in enterprise environments.
Version
1.0
Alternatives
See related products
Canonical JSON
{
"product_name": "Databricks",
"company_name": "Databricks",
"product_url": "https://www.databricks.com",
"company_url": "https://www.databricks.com",
"related_urls": [
"https://elion.health/products/databricks"
],
"product_code": "SW1201",
"summary": "Databricks offers a unified data and AI platform that enables healthcare organizations to accelerate innovation and improve patient outcomes by integrating and analyzing large-scale data efficiently.",
"description": "Databricks provides a comprehensive platform for data management, analytics, and AI, facilitating healthcare organizations in unifying fragmented data systems, enhancing data governance, and delivering personalized health insights. The platform supports various use cases, including disease risk prediction, digital pathology classification, and real-world evidence analysis, enabling healthcare providers to make data-driven decisions and improve patient care.",
"categories": [
"clinical Care",
"administrative Operations",
"patient Facing",
"diagnostic Support",
"data Management",
"health Data Analytics",
"ai Clinical Documentation Integrity",
"clinical Decision Support",
"imaging Software",
"healthcare It",
"data Governance",
"data Integration",
"Clinical",
"Administrative",
"Patient-facing",
"Diagnostic",
"Data Management",
"Analytics",
"Ai",
"Healthcare It",
"Data Governance",
"Interoperability"
],
"market_segment": [
"enterprise",
"smb",
"consumer"
],
"target_users": [
"clinicians",
"nurses",
"patients",
"admins",
"payers",
"data scientists",
"healthcare providers",
"researchers"
],
"specialties": [
"Genomics",
"Oncology",
"Cardiology",
"Neurology",
"Pediatrics",
"Geriatrics",
"Infectious Diseases",
"Orthopedics",
"Psychiatry",
"Obstetrics",
"Gynecology",
"Emergency Medicine",
"Anesthesiology",
"Radiology",
"Pathology",
"Dermatology",
"Pulmonology",
"Gastroenterology",
"Endocrinology",
"Rheumatology"
],
"regions_available": [
"United States",
"Canada",
"United Kingdom",
"Germany",
"France",
"Italy",
"Spain",
"Australia",
"India",
"Singapore",
"Japan",
"South Korea",
"Brazil",
"Mexico",
"South Africa",
"United Arab Emirates",
"Saudi Arabia",
"China",
"South America",
"Europe"
],
"languages_supported": [
"English",
"Spanish",
"French",
"German",
"Italian",
"Portuguese",
"Dutch",
"Russian",
"Chinese",
"Japanese",
"Korean",
"Arabic",
"Hindi",
"Bengali",
"Punjabi",
"Telugu",
"Marathi",
"Tamil",
"Gujarati",
"Malayalam"
],
"pricing_model": "subscription",
"pricing_details": "Contact vendor for pricing information.",
"license": "commercial",
"company_offices": [
"United States",
"United Kingdom",
"Germany",
"France",
"India",
"Singapore",
"Australia",
"Japan",
"South Korea",
"China"
],
"company_founding": "2013",
"deployment_model": [
"SaaS",
"cloud-hosted by customer on AWS",
"cloud-hosted by customer on Azure",
"cloud-hosted by customer on GCP",
"hybrid (customer-managed cloud + Databricks control plane)"
],
"os_platforms": [
"Web",
"Linux",
"Windows",
"macOS"
],
"features": [
"Unified Lakehouse (Delta Lake)",
"Data engineering (ETL/streaming)",
"Data warehousing / Databricks SQL",
"Data science workspaces / notebooks",
"Machine learning / MLOps",
"Generative AI and LLM support",
"Data governance (Unity Catalog)",
"Delta Sharing (secure data sharing)",
"Real-time streaming, event pipelines",
"Marketplace & solution accelerators",
"Collaborative notebooks and IDE integrations",
"Job scheduling and orchestration"
],
"optional_modules": [
"Databricks Apps (application development)",
"MarketPlace / Partner Connect",
"Lakehouse-specific solution accelerators"
],
"integrations": [
"Microsoft Azure",
"AWS",
"Google Cloud Platform",
"Datavant",
"Ribbon Health",
"Avanade",
"Synapxe",
"Tableau",
"Power BI",
"Fivetran",
"Confluent",
"Looker",
"Snowflake",
"Salesforce",
"Redox (healthcare interoperability partner)",
"IQVIA (life sciences data partner)",
"SAP (partner/cloud integration)",
"Partner Connect ecosystem (technology partners)",
"Third-party IDEs and BI tools via connectors"
],
"data_standards": [
"FHIR",
"HL7 v2",
"HL7 FHIR streaming/pipelines (via accelerators)",
"CSV",
"JSON",
"Parquet",
"Delta formats"
],
"api_available": "yes",
"system_requirements": "AWS, Azure, GCP, or customer-managed cloud infrastructure",
"compliance": [
"HIPAA (supports HIPAA workloads / BAA)",
"GDPR",
"SOC 2",
"ISO 27001",
"PCI-DSS",
"FedRAMP Moderate",
"IRAP",
"C5",
"CCCS Medium (Protected B)",
"DoD IL5",
"Infosec Registered Assessors Program (IRAP)",
"Korean Financial Security Institute (K-FSI)",
"UK Cyber Essentials Plus"
],
"certifications": [
"SOC 2 Type II",
"ISO 27001",
"PCI-DSS",
"FedRAMP Moderate",
"IRAP",
"C5",
"CCCS Medium (Protected B)",
"DoD IL5",
"Infosec Registered Assessors Program (IRAP)",
"Korean Financial Security Institute (K-FSI)",
"UK Cyber Essentials Plus"
],
"security_features": [
"Encryption at rest and in transit",
"Role-based access control (RBAC)",
"Single sign-on / SAML / OIDC",
"Audit logging / event logs",
"Customer-managed keys / KMS integration",
"Enhanced security monitoring",
"Hardened operating system image",
"Automatic cluster updates",
"Enforced use of AWS Nitro instance types",
"All egress communication uses TLS 1.2 or higher"
],
"privacy_features": [
"Business Associate Agreement (BAA) available",
"Data anonymization / de-identification tooling (via partners/accelerators)",
"Access controls and consent-supporting controls",
"Data residency controls",
"Data minimization",
"User control over data deletion and management",
"Zero data retention endpoints from model partners",
"Azure OpenAI content filtering",
"Evaluation with simulated user interactions to protect against harmful content",
"Protection against harmful output",
"Data protection in transit and at rest"
],
"data_residency": "Region selection by cloud provider (customer chooses AWS/Azure/GCP region / BYO cloud region)",
"customers": [],
"user_reviews": [
"Databricks has made working with massive datasets so much easier for our team. The collaborative notebooks help us share ideas and troubleshoot together, and the platform’s ability to scale means we don’t have to worry about hitting limits. It’s sped up our analytics and machine learning projects, and connecting to different data sources is a breeze.",
"I am using Databricks from around 1 year. In a week I use it approx 3-4 days. Everything is integrated, which means I don’t have to switch between multiple tools to do different tasks. It really improves team collaboration. Sharing notebooks and collaborating on models is super easy. This has been great for our team since we often work together on projects and need to see each other's code and progress.",
"The best thing about Databricks is that it very easily consolidates data engineering, data science, and analytics – all in one place. Therefore, I can process huge data sets quickly, run very complex machine learning operations, all without switching tools. Collaboration through notebooks with my team in real-time really reduced a lot of back and forth that I used to have.",
"Databricks excels in unifying data engineering, analytics, and machine learning in a collaborative, cloud-based environment. Its support for multiple programming languages (Python, SQL, Scala, R) makes it incredibly flexible. The Lakehouse architecture simplifies data management by combining the best of data lakes and data warehouses. The auto-scaling compute clusters, tight integration with tools like MLflow, and powerful notebooks streamline experimentation and production deployment.",
"Databricks excels at unifying data engineering, analytics, and AI/ML on a single platform. The Lakehouse architecture bridges the gap between data lakes and warehouses, making it incredibly efficient for managing structured and unstructured data. I especially appreciate the seamless integration with Apache Spark, robust notebook support for collaborative development, and the simplicity of Delta Lake for versioned data storage. Features like AutoML and Unity Catalog bring governance and intelligence together, making it easier to scale analytics securely and reliably.",
"Databricks excels at unifying data engineering, analytics, and machine learning into one seamless platform. What I like best is how effortlessly it handles massive data volumes while enabling collaborative development through notebooks. The integration with Apache Spark and the ability to run scalable workloads with ML, SQL, and Python side-by-side makes it a powerhouse for data-driven teams. Its governance and Delta Lake architecture also ensure reliability and security across the data pipeline.",
"Databricks unifies majority of engineering platforms. Last but not the least, Databricks Notebook concept is awesome. In the same Databricks notebook, one can have code in multiple languages (Python, Scala, SQL etc.) and each can be flipped at runtime. Built-in collaborative notebooks with support for multiple languages (Python, Scala, SQL, R) and real-time co-authoring make it easier for teams to iterate together quickly.",
"I love Databricks. What I like best about Databricks is its seamless integration of big data processing and AI. The notebook-based interface makes collaboration easy, and the use of Spark ensures fast performance. Delta Lake also provides reliable data versioning and management, which is extremely helpful in enterprise environments."
],
"ratings": [
"4.6 out of 5 on G2 ([g2.com](https://www.g2.com/products/databricks-lakehouse-platform/reviews?utm_source=openai))",
"4.6 out of 5 on G2 ([g2.com](https://www.g2.com/products/databricks-data-intelligence-platform/reviews?utm_source=openai))",
"4.6 out of 5 on G2 ([g2.com](https://www.g2.com/sellers/databricks-inc?utm_source=openai))",
"Top Healthcare Software Product on G2 ([g2.com](https://www.g2.com/best-software-companies/top-healthcare?utm_source=openai))"
],
"support_channels": [
"email",
"phone",
"chat",
"ticketing",
"community",
"24x7"
],
"training_options": [
"documentation",
"webinars",
"live_online",
"onsite",
"certification"
],
"release_year": "2013",
"integration_partners": [
"AWS",
"Azure",
"Google Cloud",
"SAP",
"MLflow",
"Apache Spark",
"Delta Lake"
],
"id": "SW1201",
"slug": "databricks",
"type": "product",
"version": "1.0",
"last_updated": "2025-10-11",
"links_json": {
"self": "https://www.healthaidb.com/software/databricks.json"
}
}