Building a Company Enrichment Agent with Nimble, Databricks, LangChain, and Claude Sonnet A company's AI enrichment agent is a critical tool for sales, marketing, and research teams. Whether you're bu...
Overview Manufacturers price millions of parts across product families, geographies, sales units, and channels. These differences often require dozens of specialized ML models—each optimized for a par...
Building agents with the Model Context Protocol on Databricks In the past year, AI applications and their capabilities have advanced significantly from LLM prompting to retrieval-augmented generation ...
Databricks just released a button to fix all legacy issues in your workspace. Actually, they didn't. But the fact that you felt a momentary surge of hope—or a cynical eye-roll—proves exactly where you...
The Gap in Agent Development Databricks has made it straightforward to deploy AI agents—Model Serving endpoints, automatic MLflow tracing, Unity Catalog integration. But there's a gap between "deploye...
Building a Research Paper Curator for Knowledge Assistants Document-heavy workflows such as research analysis, contract review, and support ticket processing, are a natural fit for AI agents. Building...
If this is the first time you’re hearing about Sinks in Lakeflow Declarative Pipelines (LDP), we highly recommend reading Introducing the SDP Sink API, which explores the recently launched Sinks API, ...
Introduction: The Data Quality ParadoxWhat We BuiltThe Technology StackArchitecture Deep DiveThe Four-Layer ArchitectureLayer 1: UsersLayer 2: Databricks Apps PlatformLayer 3: Compute LayerLayer 4: Da...
IntroductionThe Challenge with Traditional Streaming ArchitecturesHow Zerobus Ingest Enables Real-Time Ingestion in DatabricksWhy Zerobus Matters for Modern Data ArchitecturesKey Component and Archite...
Beyond ADLS Limitations: Making File Arrival Triggers Work for Existing File Updates Using a Flag File MechanismThe Flag File MechanismThe Root Problem: Triggers Only Work on “Create”, Not “Modify” Ev...
