Data Apps & Dashboards
Transform notebooks into interactive dashboards and data apps for insights & decision-making.
Executive Summary
Deepnote is a collaborative data science notebook platform designed to empower data teams to connect, analyze, and share data-driven insights. It provides a powerful environment for data engineering, data science, and machine learning tasks, allowing users to prototype models, perform ad-hoc analyses, and explore data collaboratively. A key feature is the ability to transform these analytical notebooks into interactive dashboards and data applications, facilitating easy sharing and decision-making across organizations. The platform offers extensive built-in integrations with popular databases, data warehouses, and cloud storage services, ensuring seamless data access. Deepnote emphasizes enterprise-grade security and compliance, adhering to stringent standards such as SOC 2 Type II, GDPR, HIPAA, and CCPA, making it suitable for handling sensitive financial data. Its API enables programmatic execution of notebooks, supporting automation of workflows and reporting. Deepnote caters to mid-market and enterprise-level companies, particularly within the BFSI sector, by providing a secure, compliant, and collaborative space for data scientists, analysts, and machine learning engineers to build, deploy, and share their data projects.
Use Cases
- Building interactive data applications and dashboards from notebooks.
- Collaborative data analysis and exploration.
- Prototyping and deploying machine learning models.
- Automating notebook execution for data pipelines and reporting.
- Securely sharing data insights across teams.
Features
Visibility
- Interactive Dashboards: Transform notebooks into interactive data applications and dashboards for sharing insights and decision-making.
- Collaborative Notebooks: Work together in real-time on data analysis, data science, and machine learning projects within a shared notebook environment.
- Data Exploration & Querying: Connect to various data sources and query databases directly within notebooks to explore data structures and insights.
Intelligence
- Machine Learning Prototyping: Develop and prototype machine learning models using various languages and frameworks, including Apache Spark.
- Automated Notebook Execution: Programmatically execute notebooks via API for automating data pipelines, reporting, and other data-driven workflows.
- Advanced Data Analysis: Perform complex ad-hoc analyses and data science tasks with powerful computational capabilities.
Technical Specifications
- Deployment
- SaaS, Private Cloud
- Authentication
- SSO
- API Available
- Yes
Infrastructure
- AWS
AI/ML Stack
- Apache Spark
Integrations
- Amazon S3
- Google Cloud Storage
- Google Drive
- Google Sheets
- Microsoft OneDrive
- Box
- Dropbox
- Azure Blob Storage
- Google BigQuery
- Apache Spark
Security & Compliance
Certifications: SOC 2 Type II, GDPR, HIPAA, CCPA
Encryption: All data is secure and encrypted.
Pricing
- Target Customer
- Mid-Market,Enterprise
- Free Trial
- Yes
About Deepnote
Deepnote is a collaborative, AI-powered data workspace for teams, offering a Jupyter-compatible notebook platform that runs in the cloud. It unifies data workflows through an integrated semantic layer, enabling advanced AI applications, and features an AI data copilot for code generation, chart creation, and data analysis.