When Databricks announced Lakebase, most people dismissed it as just another product. Even Databricks markets it as "the backend for AI Agents and Data Apps."
This messaging puzzles me. It's the same pattern they followed with Delta Lake in 2019, positioning it as "bringing reliability to Big Data." But Delta Lake was actually much simpler: a transactional layer on top of an immutable object store. That's it. Yet this simple concept solved a massive architectural challenge—enabling storage and compute separation for data warehouses at scale.
Storage and compute separation became the foundation for everything that followed: data sharing, unified storage formats, multiple query engines. But separation creates a problem: latency. For analytical workloads, this latency is manageable. For transactional and operational analytics? It's a dealbreaker.
Here's why this matters: the traditional divide between applications and analytics is disappearing. As businesses demand data-driven decisions in real-time operations, analytical platforms are being pulled into the critical path of business processes. Data platforms are no longer backend systems—they're front and center.
This shift demands sub-second response times that storage and compute separation simply can't deliver. You need a fused engine. You need a database.
That's what Lakebase really is.
But consider the bigger picture. In most enterprises, data follows a predictable journey: applications generate data, it's ingested into a central data lake, curated, and used for analytics. Then those insights flow back to business applications for decision-making.
What if you could collapse this entire cycle? What if applications and analytics ran on the same backend?
Object stores and Delta Lake can't solve this. You need a true database for applications. But the heavy lifting of moving data between systems can be simplified—what Databricks calls "zero-ETL." It's not actually zero-ETL; someone still runs the ETL. Databricks just handles it for you.
This positions Databricks as something entirely different. They're no longer just an analytics company. They're becoming the AWS of enterprise data—where your applications run on Databricks, your analytics run on Databricks, and low-code solutions make it accessible to business users.
The data producers become the ones running end-to-end pipelines and analytics.
But this approach creates its own set of challenges. Let's explore those next.
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