Databricks Seattle: Powering the AI-Native Data & Intelligence Platform

AI-Powered Data Science and Analytics Platform

AI-Powered Data Science and Analytics: Turning Anyone into a Data Pro

The modern data landscape is being quietly revolutionized through a new generation of AI-powered tools that transform complex analytics into something natural and intuitive. Advanced platforms now offer comprehensive solutions including visual pipeline builders, intelligent data science agents, and modern SQL editors that democratize data work for users regardless of their technical background.

Lakeflow Designer: Data Pipelines You Can Draw, Not Code

Visual pipeline creation represents a fundamental shift from traditional coding approaches. Through low-code, drag-and-drop interfaces, users can build sophisticated data workflows by connecting visual elements that represent data flow and transformation processes. This intuitive approach masks the underlying complexity while maintaining enterprise-grade reliability.

Every visual step generates clean SQL files automatically, ensuring transparency and maintainability. Business analysts can sketch data flows in minutes while engineers receive the reliability and transparency needed for mission-critical operations. AI-native architecture provides context-aware suggestions and intelligent responses throughout the development process.

Data Science Agent: An Autonomous Partner, Not Just a Chatbot

Advanced AI agents have evolved beyond simple question-answering into active collaboration partners. These systems can autonomously inspect data tables, write and execute code, run experiments, and deliver comprehensive results. Users can request complex analyses like customer churn modeling, and the agent handles the heavy lifting from data exploration through model development.

This represents a paradigm shift from AI that discusses work to AI that actively performs analysis alongside human partners. The agent capability extends the reach of every data scientist by providing expert-level assistance for routine and complex tasks alike.

Modern SQL Editor: Superpowers for SQL Analysts

Contemporary SQL environments combine traditional query editing with collaborative features and AI enhancement. These unified workspaces provide speed optimization, team collaboration tools, and intelligent query suggestions within a single platform. The experience transforms simple editors into comprehensive analytics cockpits.

Analysts work in live, shared environments where AI suggests query improvements, teammates can collaborate in real-time, and insights emerge faster than traditional workflows allow. This eliminates the isolation and inefficiency of single-user environments.

Democratized Intelligent Analytics and AI/BI

Business analytics is experiencing a fundamental transformation as AI-powered intelligence becomes accessible to all users, not just data specialists. Modern platforms blend traditional dashboards with cutting-edge AI agents and interactive embedded analytics to create comprehensive analytical experiences.

Genie: Natural Language Analytics

Natural language interfaces allow users to ask questions in plain English rather than learning complex query languages. Systems automatically locate and analyze appropriate data sources, returning clear visual answers to questions about sales trends, product performance, or operational metrics.

Recent advances have dramatically improved accuracy and response quality, making conversational analytics as approachable as everyday discussion. This accessibility expands analytical capabilities throughout organizations by removing technical barriers to data insight.

Genie Research Agent: AI Business Analysis

Advanced research agents perform comprehensive analytical investigations similar to human business analysts. Rather than providing single-chart responses, these systems conduct multi-step investigations that segment data, compare cohorts, and evaluate potential drivers of business phenomena.

Complex questions trigger sophisticated analytical workflows that explore, test, and explain findings with transparent reasoning. This elevates AI from simple answer generation to genuine analytical partnership capable of deep business insight.

Embedded Analytics: Live Insights Where Users Work

Embedded analytical capabilities integrate directly into existing applications and workflows, bringing insights to customers, suppliers, and partners where they already operate. Organizations can embed real-time dashboards, enable self-service analytics, and provide AI-powered insights within their own products.

Consumption-based pricing models enable safe scaling to thousands of users without unpredictable costs. This transforms analytics from limited internal tools into features that enhance any digital product or service offering.

Data Warehousing in the Age of AI: Serverless and Collaborative

Data warehousing infrastructure is being completely reimagined for AI-era demands. Modern platforms function like always-on power grids for data and AI workloads, providing fast, elastic, and transparent performance without manual tuning or capacity planning.

Serverless Apache Spark implementations handle millions of virtual machines daily while users focus entirely on insights rather than infrastructure management. Historical usage patterns inform intelligent workload packing and automatic scaling that optimizes performance and cost simultaneously. Provisioning happens in seconds, enabling near-instantaneous progression from concept to execution.

Open Data Collaboration Framework

Modern AI systems require rich external data sources beyond internal company information. Comprehensive data collaboration tools enable secure, governed data sharing across organizations, clouds, and industries while maintaining strict control and privacy standards.

Delta Sharing: Secure Data Exchange

Advanced sharing protocols enable live, secure dataset distribution to thousands of recipients regardless of their technology stack. Full interoperability with open formats eliminates technical barriers while fine-grained access controls ensure precise security management. Organizations can share data generously while maintaining complete oversight.

Data and AI Marketplace

Comprehensive marketplaces function as application stores for data, models, and AI agents. Providers can publish complete analytical assets while consumers discover ready-made solutions that accelerate development. This creates compounding value as each new asset enables multiple downstream use cases.

Clean Rooms: Privacy-Preserving Collaboration

Secure collaboration environments enable multi-party analysis on combined datasets without exposing raw data between participants. Advanced privacy frameworks support complex identity resolution and cross-organization insights while honoring strict regulatory and privacy requirements.

Infrastructure Optimization for Large-Scale AI

High-performance computing infrastructure specifically designed for AI workloads represents a fundamental reimagining of cloud computing foundations. Specialized teams focus on machine startup speed, data movement efficiency, and model execution optimization at massive scale.

Ultra-Fast Compute Provisioning

Customized operating systems boot virtual machines in seconds rather than minutes, enabling rapid experimentation and iteration for AI-heavy workloads. Specialized container runtimes optimize Apache Spark performance while high-speed model loading systems eliminate wait times for large language model deployment.

Scalable container registries distribute binaries at enormous throughput levels, ensuring code and model artifacts arrive precisely when needed. This infrastructure acceleration makes thousands of concurrent workloads feel effortless to end users.

Intelligent Resource Management

Predictive systems analyze usage patterns to optimize resource allocation and pre-warm compute capacity before demand peaks. Dynamic scaling adjusts both horizontally and vertically in real-time based on actual user activity, delivering optimal price-to-performance ratios without requiring infrastructure expertise.

Sophisticated workload packing maximizes resource utilization while maintaining performance guarantees. This intelligent approach transforms raw cloud compute into AI-optimized engines that start quickly, run efficiently, and scale predictably.

Monetization and Financial Systems: Sustainable Business Infrastructure

Sustainable technology platforms require sophisticated financial systems that convert powerful capabilities into viable business models without hampering innovation or user experience. Advanced teams engineer comprehensive solutions at the intersection of business and technology.

Cross-Cloud Rating Engine

Integrated rating systems process trillions of usage events across 85+ regions, operating at speeds significantly faster than industry standards. These platforms unify multiple cloud environments under single billing and metering systems while providing real-time financial signals without friction or delay.

Global, real-time metering tracks every aspect of platform activity and converts it into clean revenue data. This enables transparent pricing, accurate forecasting, and reliable financial planning that scales with business growth rather than creating operational burden.

Risk Management and Growth Enablement

Advanced admission control systems enable truly free trials without credit card requirements by carefully managing resource allocation, usage monitoring, and abuse prevention. Sophisticated controls balance generous user access with platform protection and cost management.

These capabilities support user education, product adoption, and long-term growth by lowering barriers to entry while maintaining platform security and financial sustainability. The result democratizes access to advanced data and AI capabilities for users worldwide.

Building the Future of AI Agents and Intelligence Apps: Celebrating 4 years of Databricks Seattle R&D

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DatabricksZaheera Valani