Hi @RIDBX, How are you doing today?
Great question! As per my understanding, as of today, when people say “Databricks full stack development,” they usually don’t mean a new framework — it just refers to the end-to-end development lifecycle using Databricks tools. Think of it like covering everything: data ingestion (Autoloader, notebooks), transformation (PySpark, DLT, DBT), orchestration (Workflows), storage (Delta Lake, Unity Catalog), and presentation (Dashboards, LakeView). So yes, your current skills with PySpark, DBT, DLT, and medallion architecture are definitely part of this "full stack." Additional helpful tools might include CI/CD with Databricks Repos, MLflow for machine learning, and possibly GenAI capabilities for intelligent assistants, model deployment, or automation. While there isn’t one single guide that says “Databricks Full Stack,” you can piece it together from official Databricks documentation and blogs on architecture best practices. GenAI strategies, like using MosaicML or Databricks Foundation Models, are becoming part of the stack too — especially for teams building intelligent data products. So it’s more about combining tools smartly than learning something totally new.
Regards,
Brahma