Architecture on Kianoosh's Blog

The Death of the Data Monolith: An Introduction to Data Mesh

Sat, 03 Jan 2026 00:00:00 +0000

The People Problem of Data Platforms

In my previous post, I discussed how the Lakehouse architecture addresses the technical gaps between Data Lakes and Warehouses. But even with state-of-the-art components, will we automatically have a successful data platform? OF COURSE NOT!

Engineering is easy compared to people. Who is responsible for which part? How does the organization scale as the business scales? These are critical questions an architect must answer before the wrong structure defines the architecture for them (the Conway’s Law reverse maneuver).

The Architecture of Evolution: Why the Lakehouse is Inevitable

Thu, 01 Jan 2026 00:00:00 +0000

The Structural Bottleneck

In an earlier post, I touched on why Data Lakes emerged. But to understand where the industry is going next, we need to understand the history of how we got here.

Great architecture isn’t about chasing new tools; it’s about recognizing the structural bottlenecks that force systems to evolve.

The paper “Lakehouse: A New Generation of Open Platforms” (CIDR 2021) is a brilliant breakdown of this evolution. It maps the shift from Gen 1 (Warehouses) to Gen 2 (Lakes + Warehouses) and explains why the industry is inevitably moving to Gen 3 (Lakehouse).

The Lakehouse: Bridging the Gap

Sat, 27 Dec 2025 00:00:00 +0000

The Great Divide

For years, data architecture has been defined by a strict trade-off.

On one side, you have Data Lakes: flexible, cheap, and great for unstructured data. On the other, Data Warehouses: providing strong schemas, high performance, and ACID transactions.

Both are valuable. But typically, this forces teams to maintain two separate systems. This introduces “glue” complexity, significant latency, and the nightmare of data duplication.

Enter the Lakehouse

Recently, I’ve been looking into an architecture that attempts to bring the best of both worlds together: the Lakehouse.