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Rahul Agarwal

Founder | Agentic AI...ย โ€ขย 1d

Something big is shifting in database architecture. And most people havenโ€™t noticed yet. For years, databases followed the same assumption: storage and compute lived together. Scaling meant larger machines, careful capacity planning, and migrations scheduled months ahead. Then cloud databases arrived. Managed services like PostgreSQL platforms, and RDS simplified operations dramatically. But the architecture stayed familiar. The database still controlled the storage layer. Now a different idea is emerging. Lakebase changes the relationship between data and compute entirely. Data lives in inexpensive object storage while the database acts as scalable compute. Sounds small at first. But architecturally it changes everything. Operational workloads, analytics pipelines, and AI applications can run on one shared data foundation. The data lake becomes the primary system of record across the entire platform. Databases transform into flexible engines that operate directly on top of that data layer. Data movement shrinks. Many ETL pipelines disappear because systems no longer need endless copies of the same datasets. Infrastructure becomes temporary. Databases can spin up instantly for experiments, workloads, or short-lived applications. This becomes especially important for AI systems. Agents require persistent state for memory, progress tracking, and coordination between tools. But agent activity is unpredictable. Hundreds may appear suddenly. Then disappear minutes later. The database layer must scale instantly, stay inexpensive, and remain easy to create or destroy. Traditional architectures struggle with that pattern. This model handles it naturally. After decades of watching databases evolve, this feels like another major turning point. Data platform architects building systems for the AI era should pay close attention to this.

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