This article is part of a series on deploying a production‑ready Apache Druid cluster on Kubernetes. […]
This article is part of a series on deploying a production‑ready Apache Druid cluster on Kubernetes. […]
Series: Installing a Production-Ready Apache Druid Cluster on Kubernetes — Part 1: Infrastructure Foundation This is […]
Time series data of Apache druid is the killer map for Model Context protocol server
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.
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.
Revolutionize enterprise AI with agentic RAGs. This guide explores a 15-step pipeline and offers insights for enterprise AI implementation.
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.
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.
Healthcare and AI integration featuring a futuristic hospital scene with a robot assisting a doctor, showcasing advanced medical technology and AI-driven patient care.
An in-depth analysis of the intricate challenges of vector-only Retrieval-Augmented Generation (RAG) pipelines, spotlighting these issues […]
Explore the NLWeb search prototype’s query processing pipeline in this detailed NLWeb example, showcasing how it handles "Find vegetarian recipes for Diwali" with prior context. Learn from flowcharts and insights drawn from the NLWeb GitHub repository, illustrating the system’s use of AI, Schema.org, and vector search to deliver precise results.
Kraken illustration of NLWeb, featuring the Kubernetes (K8s) logo prominently displayed. The octopus-like creature with eight tentacles is set against a deep blue underwater background, symbolizing NLWeb's strength and versatility in web development and tech solutions. Perfect for searches related to NLWeb, Kubernetes, and innovative technology.
NLWeb is revolutionizing web development by integrating advanced machine learning and AI, as showcased in recent insights. Picture NLWeb as a powerful kraken, its tentacles infused with the Kubernetes (K8s) logo, symbolizing the robust infrastructure behind these AI-driven solutions. NLWeb delivers dynamic, intelligent websites that adapt and evolve, offering unparalleled user experiences.
Augmented reality view of a tech workspace featuring the Markdown document "How JSON-LD and Schema.org Can Improve RAGs and NLWeb" displayed as holographic panels. A glowing brain hologram with pulsating synapses floats in front of a laptop screen, with radiant lines connecting the brain to the content, symbolizing AI-driven understanding. The NLWeb logo and vector search visuals overlay the screen, while a knowledge graph hologram connects a notebook labeled "Project X" on the desk to digital entities. This futuristic AR scene illustrates transforming markdown to JSON-LD for AI training data, enhancing structured data for NLWeb, and creating a digital AI twin.
Learn how JSON-LD and Schema.org enhance RAG and NLWeb with structured data. Discover howto use markdown for AI training data, boosting SEO, and creating a digital AI twin.