Practical thinking on Data engineering, AI strategy, hiring trends, and building exceptional technical teams — written by the people doing the work.
Legacy data warehouses are failing modern analytics teams. We walked a multi-brand retail group through replacing SAP BW with a full AWS data lakehouse — here's what we learned about the people, process, and politics involved.
Three approaches, three very different tradeoffs. A practical framework for deciding which architecture fits the problem you're actually trying to solve.
Speed matters. But speed without quality is just noise. Here's the exact process we run when a client needs someone fast — and why our conversion rates are nearly double the industry average.
The market has shifted dramatically since 2020. Cloud, AI, and remote-first work have reshaped what employers want — which is actually good news for returners who are willing to upskill strategically.
dbt, Airflow, Redshift, Databricks — everyone's talking about the modern data stack. Here's what we've actually implemented for real clients, and the decisions that matter most at each layer.
Most companies aren't failing at AI because of the technology — they're failing at data quality, governance, and internal alignment. Here's a practical self-assessment before you spend a single rupee on an AI project.
The engagement model you choose affects everything — speed, cost, commitment, and quality. Here's what our clients across four countries actually prefer, and when each model makes sense.
No HRMS handled Indian contractors billing internationally with GST compliance. So we built our own. Here's what we learned — and why we're opening it up to other staffing businesses.