Time series data of Apache druid is the killer map for Model Context protocol server
by Chris

Apache Druid MCP Server: Conversational AI for Time Series

While Apache Druid offers unparalleled real-time analytics, its operational complexity often creates a significant bottleneck for data teams. This article introduces the iunera Druid MCP Server, a revolutionary open-source tool that builds a conversational bridge to Druid's powerful engine. Learn how it leverages a Large Language Model (LLM) and the Model Context Protocol (MCP) to translate simple, natural language commands into complex data workflows, removing operational overhead and making advanced analytics accessible to everyone.
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a desnk with a 15-step pipeline graphic for orchestrating enterprise AI brilliance with agentic RAG systems allowing to dock data sources like pipes and enabling user specific datasources within the enterprise.
by Tim

Enterprise AI Excellence: How to do an Agentic Enterprise RAG

Revolutionize enterprise AI with agentic RAGs. This guide explores a 15-step pipeline and offers insights for enterprise AI implementation.
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The title image is a visually engaging graphic (1200x628 pixels, per Search Engine Journal) depicting a stylized, simplified version of the data ingestion pipeline flowchart from the article. It features a clean, dark blue background (#005580) with a central flowchart of 6 steps, arranged vertically, using transparent rectangles with black borders for steps (e.g., “Source Crawling,” “Data Preprocessors”) and white document shapes for outputs (e.g., “Chunked Data”). Black ellipses highlight processing stages (e.g., “Data Embedding”), connected by black arrows with arrowheads. Dashed containers list extensions (e.g., “Vector Embedding Generation,” “Filter Specification”) in Arial font (12px for steps, 11px for extensions). A subtle overlay of interconnected nodes and data flow lines (representing polyglot databases like vector, SQL, graph) spans the background, with icons for text (document), images (photo), and JSON (code brackets). The article title, “Scalable Polyglot Data Ingestion Framework for AI-Driven Search Ecosystems,” is overlaid in bold, white Arial font (24px) at the top, with a tagline “Enabling Vector, SQL, and Graph Indexing” in smaller text (16px) below.
by Tim

Polyglot Knowledge RAG Ingestion Concept for Enterprise Ready AIs

Explore a scalable polyglot data ingestion framework for AI-driven search ecosystems, supporting vector, SQL, and graph indexing. A flowchart details 6 steps for preprocessing and embedding, enabling robust RAG search.
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Healthcare and AI integration featuring a futuristic hospital scene with a robot assisting a doctor, showcasing advanced medical technology and AI-driven patient care.
by Tim

Unleashing RAGs from Vector Search Shackles in Healthcare

An in-depth analysis of the intricate challenges of vector-only Retrieval-Augmented Generation (RAG) pipelines, spotlighting these issues […]
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