There are some great names in software engineering and this is most definitely one of them. The lakehouse architecture promised to combine the best of data warehouses and data lakes. In practice, it delivered quite a lot of complexity. We…
When WebAssembly launched in 2017, the pitch was clear – run native code in browsers at near-native speed. I sat next to an engineer at the time who was incredibly excited about this, but his explanation made no sense at…
Every data platform eventually faces the same question: How do we actually get data from point A to point B? It’s a deceptively simple question with dozens of possible answers, each with its own trade-offs in terms of complexity, reliability,…
In industries like manufacturing and logistics, digital twins are dynamic digital replicas of physical systems. These are are well established. For example, here is NVIDIA presention of how digital twins can drive industrial innovation: SOURCE NVIDIA And it makes sense.…
Data architectures need organizing principles that help teams understand where data lives, what quality to expect, and how transformations progress. And what better than a catchy description. Without clear structure, data lakes devolve into swamps where nobody knows which datasets…
Data Lakes promised everything. Store all your data in one place, in any format, ready for any workload. The reality was horrific. Data Lakes became data swamps, filled with inconsistent data, failed jobs leaving partial writes, no way to roll…
The question of REST versus GraphQL has become one of those debates in software engineering where everyone has an opinion, most of it strong (if sometimes wrong), and much of it colored by their most recent project experience. Like most…
Your production database contains millions of customer records with real names, addresses, credit card numbers, social security numbers, and medical histories. Your developers need realistic data to test new features. Your analytics team needs representative datasets to validate models. Your…
The term serverless is somewhat misleading because servers obviously exist somewhere. This is not magic. For decades, deploying a database meant answering impossible questions. How much capacity do you need? How many CPU cores, how much memory, how much disk?…
In the late 1990s and early 2000s, the rapid growth of the web changed database design forever. Traditional monolithic, single-node relational databases, the backbone of enterprise applications for decades suddenly faced workloads that spanned continents, scaled to millions of concurrent…
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