Amazon Aurora Deep Dive Series: From Monolith to Modular - Inside Amazon Aurora’s Cloud-Native Database Architecture

An Aurora Deep Dive Series by Rathish Kumar B - Part 2 

Amazon Aurora reimagines the database as a set of decoupled, distributed services—each built to scale, fail, and recover independently.

In our previous article we discussed why monolithic databases hit scalability and availability limits as workloads grow. Traditional RDBMS engines bundle query processing, transaction management, caching and storage into one tightly-coupled system. In such a monolithic design, every SQL write passes through a single process that parses the query, locks data, updates in-memory buffers, logs changes, and flushes to disk. By definition, “monolithic” means all functionally distinguishable components (parsing, processing, logging, etc.) are interwoven rather than separate. This coupling creates bottlenecks: for example, all sessions share one buffer pool and one write-ahead log (WAL) stream on the same machine. The rest of this article examines the traditional SQL transaction path and its tradeoffs, and then shows how Aurora breaks these layers apart into cloud-native services for greater throughput and resilience.

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Amazon Aurora Deep Dive Series: The Scaling Bottleneck - Why Traditional Databases Fail and How Aurora Wins

An Aurora Deep Dive Series by Rathish Kumar - Part 1 

Scaling a database sounds simple—until you're staring down a production outage. 

The reality is that for decades, the very design of our databases has been at odds with the demands of modern, high-growth applications.

Most traditional database systems begin with a monolithic architecture. In this model, everything—compute, memory, and storage—is tightly coupled and resides on a single server. This all-in-one approach is straightforward when you're starting small. But as your traffic and data volumes explode, that single server inevitably becomes a bottleneck. The first, most common response is to scale vertically by upgrading to a bigger, more powerful server. However, this strategy quickly runs into hard physical and cost limitations. Moreover, you're left with a critical single point of failure, where one hardware issue can bring your entire application to a halt.

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