Dhaval Shah

The Scene A CTO of a small FinTech company had scheduled a meeting with me along with senior folks from his engineering team.

What happens when senior leadership ships a "soft launch" to production without architectural review - and why every architect needs a battle-tested framework for saying no.

Background This is the fifth post in a series on AI-augmented software engineering across the disciplines that matter most for production grade enterprise systems.

Background An older post on this blog covered the technical details of this incident - the GC types, the flags, the before-and-after metrics.

Background My earlier article on AI augmented Fintech post-mortem review walked through an Oracle JSON storage problem: a payment transaction table stored as JSON, a query that needed to filter on a nested field, and the chain of fixes - that the team eve…

Background In Part 1, we established that access patterns & not entities should drive your design.

Background My old article compared Virtual Thread based implementations of Spring Core Reactor and JDK 21, building on the comparative analysis before it.

Background In the first part we established the foundational principle that separates systems which survive scale from systems that collapse under it - design for access patterns, not for entities.

Background Architecture decisions rarely break at design time.

Background This multi part series walks through the real architectural decisions and real mistakes behind designing a globally distributed KYC User Profile system - a system that should be capable of serving billions of users at 10M+ RPD with sub-500 ms P…

I am a Principal Software Architect with more than 2 decades of experience designing and scaling distributed platforms.

Background In the previous article, we explored resiliency and fault tolerance as critical enablers of high‑volume inter‑cloud data transfer.

Background Parts 1 and 2 established the what and the how fast — a stateless, chunk-driven pipeline with deterministic block IDs, reactive backpressure, and an adaptive concurrency controller functions under real cloud conditions.

Background In Part 1 we established RICS's foundational architecture — a stateless, chunk-driven pipeline with a hard separation between the data plane and the metadata-driven control plane.

Background Imagine you need to move terabytes or petabytes of large files from AWS S3 to Azure Blob Storage, and that too with Reliability & High Throughput The naïve approach (download the whole file, upload whole file) collapses under real‑world realiti…

Let's work together and fix whats' slowing you down Independent consulting architect — 20+ years scaling fintech, SaaS, and payment platforms.

Introduction If you’re here, chances are, something in your product or platform or MVP isn’t moving the business forward the way you expected - New features take longer to ship because the tech stack is hard to manage Systems slow down or break under traf…

Background As software engineers we spend so much of our time building these beautiful, complex data pipelines.

Background My previous article focussed on comparing solutions for performing parallel execution using Spring Core Reactor and JDK 21.

Background If you've ever had to implement business logic that involves concurrent execution along with heavy IO operations, you'll know it can be challenging!

Background For someone like me who's been an ardent fan of Rafael Nadal since 2008, hearing the news of his upcoming retirement absolutely shatters my heart.

Background In today’s world of distributed computing, network is the backbone of high-performance, low-latency enterprise applications.

Background Lot of my pet projects have been built using Spring Boot 2.x version.

Background As I concluded my year-end break in 2022, I engaged in a profound professional introspection.

Background Recently I stumbled upon a tweet on my twitter timeline, that discussed about approaches / strategies that can be adopted to improve performance of enterprise applications.

Background In this current era, Distributed Architecture has become de-facto architectural paradigm, which necessitates implementation of loosely coupled Microservices which would talk with each other via REST API Message Oriented Middleware As far as Mes…

Background In today's contemporary world of enterprise software where massively used applications are expected to scale and run seamlessly at extreme high loads e.g.

Background In couple of my previous articles I not only tried helping understand fundamentals of JVM memory management, but also delved deeper to understand how Garbage Collection works along with its optimization.

Background In one of my previous post we saw tangible advantages (w.r.t throughput, latency and resource utilization) of refactoring existing Microservice application from imperative to reactive constructs.

Background In my previous post I tried demonstrating how to implement an optimal and performant REST client using RestTemplate In this article I will be demonstrating similar stuff but by using WebClient.

Background In this contemporary world of enterprise application development, Microservice Architecture has become defacto paradigm.

Background As software industry is embracing the new Microservice Architecture paradigm, myriad applications have been built with Spring Boot framework.

Now that twenty-twenties i.e. 2020 has just commenced, I just thought to reflect upon the bygone year 2019 as a casual and occasional blogger.

In one of my previous post I elaborated basics of JVM memory and also explained how garbage collection works.

Background From first part of blog we gathered understanding about basics of Chaos Engineering.

Human body is vulnerable to lot of diseases.

Personally speaking, I am of the belief that only way to excel (professionally) is to collaborate and learn from experts.

Spring Boot helps developers to implement enterprise grade applications which can be pushed to production in no time.

Software engineers have been ardently following Test Driven Development (TDD) as an XP practice for having necessary safety nets.

Everyone of us as Software Engineers would have experienced memory leaks, OOM errors in our Java/JVM applications?

Background As per the current trends, Microservice Architecture has become a common paradigm using which enterprise applications are built.

Considering the extensive usage of Spring Boot for building Cloud Native Architecture, I embarked on the journey of utilizing it in my reference Cloud Native application.

Whenever the buzz word 'Cloud Native Architecture' gets to our ears, first thing that comes to our mind is - It sounds too jazzy and latest and would have something to do with technology since it has a word called 'Architecture' within it :).

This is the concluding blog of 2 part series on 'Anatomy of Test Driven Development'.

Since we being one of the most intellectual and so called logical species on this earth, we need proper rationale behind each and every action that we do in our personal and professional life.

Recently I was required to identify a memory leak in one of the enterprise application running in production.