Building an LLM-powered prototype is easy. A few lines of code, a hosted model, and you have an agent that classifies tickets, summarizes documents, or answers questions. The hard part comes after: me...
Introduction In Part 1 of this series, Integration Testing for LakeFlow Jobs with Pytest and Databricks Connect, we built a blueprint for testing LakeFlow Jobs: deploy the job, trigger it from pytest,...
As organizations scale their AI initiatives on Databricks, a common pattern emerges: one team builds and maintains a vector store, while other teams across different workspaces need to query it. Perha...
If you've been deploying Databricks Apps from Git, you know the workflow: push your code, open the Deploy dialog, enter a Git reference, and hit Deploy. It works, but requires a series of manual steps...
Establishing a trusted Continuous Integration/Continuous Deployment (CI/CD) process is crucial for effectively managing the lifecycle of your data and AI workloads in Azure Databricks. However, with n...
