Databricks Seattle: Four Years Building the AI-Native Data Intelligence Platform

AI-Native Data Science and Analytics Platform

AI-Native Data Science and Analytics: A New Way to Work With Data

Databricks is quietly rewriting the rules for data science and analytics by making the whole experience AI‑native from the ground up. Instead of analysts and data scientists wrestling with code, tools, and infrastructure, the platform increasingly behaves like a smart teammate that understands data, writes code, and helps build production pipelines.

Lakeflow Designer: Drag, Drop, and Let AI Do the Heavy Lifting

Lakeflow Designer is presented as a new kind of workspace where business users and data teams build analytics with a drag‑and‑drop interface instead of starting from a blank SQL file. Every step in this visual workflow is backed by real SQL under the hood, which can be stored in Git for version control, CI/CD, and collaboration.

In practice, this means a user can visually design a data pipeline or analysis, while the platform automatically generates clean SQL and keeps it in sync. Because Designer is built as an AI‑native experience on top of the full Databricks data intelligence platform, it can use AI to suggest transformations, answer questions, and guide people through their analysis without requiring deep technical skills.

Data Science Agent: From Chatbot to Hands-On Coding Partner

The Data Science Agent represents a major shift in how people interact with data. Instead of being just a conversational assistant that answers questions, the Agent operates in an autonomous workflow mode. In simple terms, Databricks turns an AI assistant into a junior data scientist on demand.

This unlocks productivity: specialists can move faster by offloading routine tasks, while less technical users can still run sophisticated analyses by describing what they want in plain language. For organizations, this capability expands the user base and deepens platform stickiness.

SQL Editor: A Modern Command Center for Analysts

SQL remains the core language of analytics, and the new SQL Editor gives analysts a modern, AI‑enhanced workspace rather than a basic query box. This represents the natural evolution of the analyst workflow: an environment where human expertise guides the questions, while AI accelerates the mechanics of writing and improving SQL.

Rather than replacing analysts, the platform amplifies them, enabling teams to turn raw data into decisions much faster. Across Lakeflow Designer, the Data Science Agent, and the new SQL Editor, Databricks is not just sprinkling AI on top of old tools—it is rebuilding the data science and analytics experience around AI from first principles.

Democratized Intelligent Analytics and AI/BI

Databricks is turning business intelligence into something anyone can use, not just data experts. At the center of this shift is AI/BI with Genie—an AI-powered analytics experience that lets people ask questions in plain language and get real, data-backed answers in seconds.

Instead of wrestling with complex dashboards or learning SQL, users can simply type questions like "How did sales trend last quarter by region?" and let Genie do the heavy lifting. Genie converts natural language into the right queries, scans the underlying data, and returns insights in a format that makes sense to business users.

Genie and the Genie Research Agent: From Questions to Deep Investigation

Genie now supports richer ad hoc analysis, including the ability to upload files, run evaluations and benchmarks, and deliver more accurate answers than previous generations of BI tools. The Genie Research Agent pushes this even further by performing multi-step reasoning and hypothesis investigation.

Where traditional BI might give a static chart, the Genie Research Agent behaves more like a curious analyst, probing the data to uncover deeper patterns and explain why something is happening, not just what the numbers are.

Embedded Analytics: Bringing Live Insights to Customers and Partners

Databricks is pushing analytics beyond internal dashboards and into the products and portals that end users touch every day. With embedded analytics, a dashboard built in Databricks can be dropped directly into a customer-facing or partner-facing application.

The result is that analytics stop being an internal reporting function and become a core feature of products themselves. A SaaS company can give each customer a live, personalized analytics experience directly inside its app, powered by Databricks but branded and delivered as part of its own product.

Serverless Data Warehousing & Open Data Collaboration

Databricks is rewriting the rules of data warehousing with serverless computing, open sharing, and privacy-safe collaboration that makes data feel instant, fluid, and shareable across companies and clouds.

Serverless Spark: Warehousing Without the Warehouse Headaches

Traditional data warehouses demand constant babysitting: sizing clusters, tuning performance, and worrying about overpaying for idle machines. Databricks' Serverless Apache Spark flips that model. Engineers are building a platform that quietly runs millions of virtual machines a day so users never have to think about infrastructure.

The platform studies historical usage patterns and automatically optimizes workloads so compute is always used efficiently. It can scale up or down horizontally and vertically based on what users are actually doing, delivering strong price–performance without manual tuning.

Delta Sharing: A Common Language for Data Across Companies

In modern AI, the most valuable data often lives outside a company's walls. Delta Sharing provides a standard way to share live, governed data across organizations. Instead of emailing files around or building fragile custom APIs, Delta Sharing turns data into a service that can be reliably offered to partners, customers, and internal teams.

Databricks Marketplace: The App Store for Data, Models, and Agents

The Databricks Marketplace serves as a storefront where data providers, model builders, and tool creators can package and offer their assets for others to discover and use. One especially important capability is Model and Agent Sharing, where providers can publish MCP (Model Context Protocol) tools to the Marketplace.

Clean Rooms: Collaborate on Sensitive Data Without Giving It Away

Databricks Clean Rooms enable organizations to collaborate on data without actually handing raw data to each other. Instead, multiple parties can run approved analytics over combined datasets in a controlled, privacy-preserving environment.

Performance and Infrastructure Optimization for Large-Scale AI

Behind the scenes, teams are tuning the platform so that massive AI and analytics workloads feel instant, efficient, and almost effortless to the user. At the core is the Serverless Apache Spark platform, designed to run millions of virtual machines every single day while keeping everything fast and cost-efficient.

Custom Operating System: Booting VMs at Warp Speed

Databricks has crafted a custom, lightweight operating system tailored specifically for its workloads. The result: virtual machines that boot up to seven times faster than before. When an organization launches a new AI experiment or huge data job, the underlying machines are ready almost instantly.

High-Speed Infrastructure: 10 Tbps Container Registry

A highly scalable container registry can deliver binaries at 10 terabits per second—a supercharged software distribution center that can serve tens of millions of container images every day without slowing down. This high-throughput registry turns potential bottlenecks into highways, keeping fleets of compute nodes fed with what they need at staggering speed.

Monetization Systems: The Financial Engine

The "Money Team" turns advanced Data + AI products into a sustainable, fast-scaling business across every major cloud. Rather than treating billing and pricing as an afterthought, Databricks has built a financial engine as modern and intelligent as its AI platform.

Cross-Cloud Integrated Rating Engine

At the core is a breakthrough system: a cross-cloud integrated rating engine that can handle trillions of usage events. Every time a customer runs a workload, this engine tracks and translates that activity into accurate, real-time financial data across multiple cloud providers.

This means Databricks can understand, price, and bill usage at massive scale and high speed, no matter which cloud a customer chooses. It removes friction for customers and gives the company precise control over monetizing new products.

Enabling True Democratization

One tangible outcome is the industry's only truly free trial for Databricks, with no payment information required. This democratizes access for students, individual developers, and smaller teams who want to learn modern data and AI without budget constraints.

The Money Team has built sophisticated fraud detection, automated resource limits, and self-service account management to make this possible at scale. For customers, this means clearer bills, better cost controls, and confidence that Databricks can scale with their ambitions.

Through this comprehensive approach, Databricks is not just making data warehouses faster—it is turning the entire data and AI stack into a shared, serverless, and collaborative fabric that any team can tap into to build the next generation of intelligence applications.

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

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DatabricksZaheera Valani