Observed Signal · Oct 5, 2026 · Market Signal · Source: Ragic · Impact: 2/5
5 Common Ragic Beginner Questions Answered
This article answers 5 common questions new Ragic users ask about building, customizing, and managing their databases, from editing sheets and arranging fields to handling complex workflows and customization costs.
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Power BI Data Modeling: Joins, Relationships, Schemas
A technical guide explaining core data modeling concepts in Microsoft Power BI. The article covers using joins in Power Query Editor (six join types), creating model-level relationships in Model View (one-to-many, many-to-many, one-to-one), cross-filter direction (single vs. bidirectional), and the impact of schema choices (star vs. snowflake) on performance and maintainability. The author emphasizes choosing intentional joins, favoring star schema for most Power BI projects, and building clean relationships to simplify DAX, improve performance, and ensure accurate reports.
Power BI Data Modeling: Schemas, Relationships, Joins
This technical tutorial explains core Power BI data modeling concepts for building fast, accurate reports. It defines tables, relationships, and DAX measures; contrasts fact tables (transactional, long) with dimension tables (descriptive, short); and recommends the Star Schema as the best practice in Power BI while describing Snowflake trade-offs. The guide covers relationship basics (primary/foreign keys), cardinality types (1:N, N:1, 1:1, N:N and bridge tables), join types (inner, left, cross), cross-filter direction (single vs bidirectional), and active vs inactive relationships with the DAX USERELATIONSHIP() function. It includes a complete star-schema example, cheat sheet of golden rules, and practical tips for performance and maintenance.
RAGFlow + MCP: Deploying Measured RAG as Assistant
This developer article explains how to turn an evaluated RAG (retrieval-augmented generation) configuration into a production document assistant using open-source tools. The author recommends RAGFlow — an open-source document RAG platform — for parsing documents (preserving tables, OCR, heading hierarchy), indexing, and serving knowledge bases. RAGFlow can run as an MCP (Model Context Protocol) server so MCP-enabled clients (e.g., Claude, Cursor) can query a team's self-hosted knowledge base with source-cited answers. The piece outlines a two-step workflow: use measurement tools (AutoRAG, RAGBuilder) to find optimal RAG settings, then build knowledge bases in RAGFlow and connect them via MCP, keeping data self-hosted for privacy/compliance.
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