Over the past decade, we’ve watched data infrastructure evolve from on-prem monoliths to highly modular, cloud-native platforms. But let’s rewind the clock a bit further to see how we got here—and why Apache Polaris (Incubating) just might be the missing piece for truly scalable, governed data lakehouse architectures.
Back in the Hadoop era, the goal was clear: decouple storage and compute to create more scalable and cost-effective data platforms. The concept of the “data lake” emerged, and with it came the hope of unifying structured and unstructured data in one place. But managing datasets at scale, particularly tables, was anything but easy. Apache Hive brought us the idea of a table abstraction over files, but it was tightly coupled to Hadoop’s execution engine and brittle when things got complex.
Hive’s metastore worked, but let’s be honest—it wasn’t built for today’s decentralized, multi-engine, cloud-native world.
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