Post by Prateek Jalgaonkar

🚀 How I Think About Building a Domain Data Accelerator Over the last few projects, I’ve learned that delivering analytics fast isn’t just about pipelines or tools — it’s about reusability and domain understanding. A data accelerator is essentially a pre-built, domain-focused framework that helps clients go from raw data to insights much faster. Here’s how I approach building one 👇 🔹 Start with domain knowledge Understand business processes, KPIs, and pain points first (for example, in RCM: Charges, Payments, Denials, AR metrics). 🔹 Design a canonical data model Create reusable fact and dimension tables that work across clients. Client-specific differences are handled via schema mapping, not model redesign. 🔹 Standardize ingestion & CDC Build config-driven ingestion and incremental logic so onboarding new clients requires minimal code changes. 🔹 Build a semantic layer Define reusable metrics, calculations, and hierarchies using tools like AtScale/dbt so business users get consistent, trusted numbers. 🔹 Deliver ready dashboards Pre-built executive and operational dashboards that can be customized per client without rebuilding everything. 💡 The goal is simple: Reduce onboarding time, ensure metric consistency, and scale analytics across multiple clients. This approach has helped me think beyond pipelines and focus on building production-grade, reusable data products. Would love to hear how others are approaching accelerators or reusable analytics frameworks! #DataEngineering #Analytics #SemanticLayer #DataModeling #CloudData #LearningJourney