The hidden costs of traditional databases often force developers to create custom infrastructure for managing workflows. This is a critical resource conflict. Instead of focusing on building new products or advancing AI initiatives, the developers spend more time solving operational problems.
As enterprises move deeper into AI, this issue is pushing data teams outside their core goals. Agentic AI and real time decision systems demand databases that can handle rapid transactions while remaining closely connected to analytics and ML environments.
Databricks aims to overcome this architectural bottleneck with Lakebase – a new operational database designed to connect transactional workloads directly with the lakehouse. It is built as a serverless Postgres offering for AI applications with the goal of simplifying infrastructure for agentic workflows.
According to Devin Pratt, Research Director at IDC, “For decades, the architectural ‘tax’ of keeping operational and analytical data separate has slowed enterprise innovation. By decoupling the storage layer and integrating directly with the data lake, Lakebase is positioned as a new class of operational database that treats infrastructure as a flexible, on-demand service.”
Databricks is also changing how users interact with data through Genie – an AI powered assistant that lets people ask questions about company data in plain language instead of writing SQL or building dashboards.
Ali Ghodsi is the CEO and co-founder of Databricks
These two developments signal a shift in how Databricks wants operational data to live alongside analytics. If the operational workflows also can sit closer to the lakehouse itself, that allows teams to spend less time building connections and pipelines. With Lakebase, Databricks is moving beyond analytics into areas traditionally handled by application databases.
Genie tackles a different problem. We know there is no shortage of data, but access is still stuck with analysts or technical teams. Everyone else has to wait or go through dashboards. Not the most efficient way to operate. With Genie the idea is simple – make data easier to use without losing the structure or governance companies rely on.
Lakebase and Genie are really two parts of the same strategy. Lakebase deals with how data gets stored and used by AI applications. Genie deals with how humans actually interact with that data day to day. One simplifies the backend. The other simplifies the front end.
“We’re seeing overwhelming investor interest in our next chapter as we go after two new markets,” said Ali Ghodsi, co-founder and CEO of Databricks. “With this new capital, we’ll double down on Lakebase so developers can create operational databases built for AI agents. At the same time, we’re investing in Genie to let every employee chat with their data, driving accurate and actionable insights.”
These launches arrive at a moment when Databricks is drawing major investor attention. The San Francisco-based company shared that it has crossed a $5.4B revenue run rate, with more than 65% YoY growth during the last quarter. It is also closing new investments worth over $7B, including $5B in equity financing at a $134B valuation and about $2B in additional debt capacity. The company says this new capital will be used to accelerate Lakebase, Genie, and help it advance its AI goals.
It’s not just Databricks’ growth rate that is attractive to investors, it is also the opportunity it offers to expand beyond analytics infrastructure into operational and interaction layers that are crucial for AI applications. This is why we see other vendors in this space keep on combining operational databases and AI under a single environment.
“Databricks is a generational company that has become a backbone for enterprise data and AI, helping organizations across critical sectors seize opportunities and overcome challenges,” said Todd Combs, Head of the Strategic Investment Group for JPMorganChase’s Security and Resiliency Initiative. “This initial investment reflects the strength of Databricks’ secure platform and continues to support their innovative, production‑scale applications that serve customers around the world.”
The launch of Lakebase and Genie is a move by Databricks to expand beyond its lakehouse roots into a broader AI data platform. The approach still depends on enterprise adoption, since combining operational databases and analytics introduces architectural and workflow challenges of its own. However, with fresh capital and strong growth, Databricks is betting on a future where enterprise AI needs to depend on tightly connected data and infrastructure.
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Author: Ali Azhar
