The Great Lock-In Convergence

In recent months, three major vendors – Snowflake, Databricks, and Microsoft – have launched Postgres-flavored databases with custom storage layers and scale-out architectures. At first glance, these offerings might seem like welcome developments for developers seeking to harness the power of Postgres in cloud-native environments. However, a closer examination reveals that these new databases are not merely improvements on traditional Postgres but deliberate attempts to create lock-in scenarios.

The idea behind these converged platforms is to combine operational and analytical workloads within a single platform. Proponents tout this convergence as the future of data management, promising unprecedented scalability and efficiency. However, what’s often glossed over in vendor marketing materials is the loss of flexibility and control that comes with adopting these solutions. By choosing one of these Postgres variants, developers essentially surrender their ability to freely migrate or switch between platforms.

Snowflake Postgres may be the most “Postgres-like” of the three, but it remains a proprietary offering tied to Snowflake’s pricing and ecosystem. In contrast, Lakebase is an intriguing option for developers, particularly those already invested in Databricks. Its branching model and point-in-time recovery features are genuine innovations. However, its limitations and compatibility issues with stock Postgres should not be underestimated.

Azure HorizonDB boasts impressive performance numbers but raises questions about the true nature of “wire-compatible” Postgres. Microsoft’s from-scratch storage engine may offer significant gains in scalability, but it’s a trade-off for developers who rely on extension and tooling surfaces that are not yet fully supported.

The marketing pitch around these new databases often focuses on their ability to achieve operational and analytical convergence within the platform you’ve already paid for. However, this narrative overlooks the fact that each of these solutions is tied to a specific vendor ecosystem, with its own set of costs, limitations, and lock-in risks. The promise of cross-platform compatibility is an illusion, as the actual decision question revolves around which vendor’s ecosystem you’re already committed to.

The choice between these Postgres variants ultimately comes down to one critical factor: your existing data platform commitments. If you’re already invested in Snowflake or Databricks, sticking with their respective offerings makes sense. However, for those without a pre-existing data platform, running actual Postgres on actual instances or using managed services like Aurora or Cloud SQL might be the more sensible choice.

The synchronized effort by three major vendors to create lock-in scenarios within their respective ecosystems is the most interesting development here. This convergence of shared-storage scale-out architectures into a single category raises questions about the future of Postgres and its derivatives. Will these vendors continue to push the boundaries of what’s possible with cloud-native data platforms, or will they prioritize maintaining control and market share?

As developers, it’s essential to remain vigilant and not be swayed by the promise of operational and analytical convergence. The real story here is one of lock-in and vendor relationships. Be cautious of the “you’re either in or out” narrative that surrounds these new databases. Remember that true flexibility and control are still available with traditional Postgres, managed services, or a combination of both.

The era of data platform convergence has begun, but it’s not without its risks. As we move forward, let us not forget the value of choice and the importance of preserving our ability to adapt and evolve in an ever-changing technological landscape.