This is a sharp point, and it generalizes well beyond India. Any team where the infra bill is high runs into the same math.
The NoSQL default usually comes from optimizing for the wrong stage. Schema flexibility feels valuable in month one, when the data model is still moving. But the cost shows up in month eighteen, when you're doing joins in application code, maintaining denormalized copies by hand, and debugging a read-after-write bug that only reproduces under load. That's engineering time, and engineering time is the most expensive thing a small team spends.
Where NoSQL genuinely earns its place is when the access pattern is known, narrow, and fixed.
Better performance - whether on chips, supercomputers, mobile phones, or the cloud - isn't solely about faster software. It fundamentally involves a drastic re-evaluation of how hardware performs calculation. Today's digitally driven approach is achieving enhanced performance through a switch to parallelism, moving from serialized to massively parallel, distributed task execution that enables many operations per moment instead of one at a time, dramatically reducing execution latency through improved data dependency, better utilization of hardware resources, and minimizing the critical path to digital circuit completion.
Hardware achieves the speedup by minimizing data dependency while it adds parallelism through hardware design changes like flow transformation, better memory hierarchy, pipelining, utilizing instruction-level parallelism (ILP) and data-level parallelism (DLP), adding replicas of computational resources or specialized hardware (e.g., AI chip, GPU, TPU, FPGA).
Hardware also uses high-bandwidth memory (HBM), cache efficiency, nearer-memory computing, and memory prefetches to mitigate these concerns by fetching data closer to the computation cores or enabling the computation itself to move to data in memory, all to reduce the system bottleneck. The next paradigm in computer is built on this - heterogeneous systems that integrate together high-performance processing units from general-purpose CPUs, GPUs, A.I., Edge, Quantum, and HPC into single processing units. Further AI assistance with Compiler technology (for optimization and analysis), resource scheduling, physical design automation, placement/routing of circuits, and the runtime to adapt the processing for the problem at hand enable us to seamlessly manage, allocate, and balance workload on any type of processing unit with the highest degree of efficiency possible for throughput and energy consumption, and on a large scale. The hardware-software code design of algorithms, system architecture, memory, and resource orchestration becomes the key to unlocking massive increases in computational speed, energy efficiency and overall scalability for our demanding AI driven future including quantum assisted computation, autonomous systems, and the digital twin.
The cost implications of these choices hit hard in Indian startups where every rupee counts.
I keep seeing teams default to NoSQL for flexibility, then pay heavily in operational complexity and inconsistent reads.
A well-chosen SQL database with careful indexing often outlasts the hype.
This is a sharp point, and it generalizes well beyond India. Any team where the infra bill is high runs into the same math.
The NoSQL default usually comes from optimizing for the wrong stage. Schema flexibility feels valuable in month one, when the data model is still moving. But the cost shows up in month eighteen, when you're doing joins in application code, maintaining denormalized copies by hand, and debugging a read-after-write bug that only reproduces under load. That's engineering time, and engineering time is the most expensive thing a small team spends.
Where NoSQL genuinely earns its place is when the access pattern is known, narrow, and fixed.
Thank you so much
Better performance - whether on chips, supercomputers, mobile phones, or the cloud - isn't solely about faster software. It fundamentally involves a drastic re-evaluation of how hardware performs calculation. Today's digitally driven approach is achieving enhanced performance through a switch to parallelism, moving from serialized to massively parallel, distributed task execution that enables many operations per moment instead of one at a time, dramatically reducing execution latency through improved data dependency, better utilization of hardware resources, and minimizing the critical path to digital circuit completion.
Hardware achieves the speedup by minimizing data dependency while it adds parallelism through hardware design changes like flow transformation, better memory hierarchy, pipelining, utilizing instruction-level parallelism (ILP) and data-level parallelism (DLP), adding replicas of computational resources or specialized hardware (e.g., AI chip, GPU, TPU, FPGA).
Hardware also uses high-bandwidth memory (HBM), cache efficiency, nearer-memory computing, and memory prefetches to mitigate these concerns by fetching data closer to the computation cores or enabling the computation itself to move to data in memory, all to reduce the system bottleneck. The next paradigm in computer is built on this - heterogeneous systems that integrate together high-performance processing units from general-purpose CPUs, GPUs, A.I., Edge, Quantum, and HPC into single processing units. Further AI assistance with Compiler technology (for optimization and analysis), resource scheduling, physical design automation, placement/routing of circuits, and the runtime to adapt the processing for the problem at hand enable us to seamlessly manage, allocate, and balance workload on any type of processing unit with the highest degree of efficiency possible for throughput and energy consumption, and on a large scale. The hardware-software code design of algorithms, system architecture, memory, and resource orchestration becomes the key to unlocking massive increases in computational speed, energy efficiency and overall scalability for our demanding AI driven future including quantum assisted computation, autonomous systems, and the digital twin.