Observed Signal · Apr 4, 2026 · Technical Guide · Source: DEV Community · Impact: 1/5 · Sentiment: Neutral
Beginner Guide: Getting Data Into Power BI
This tutorial explains how beginners can import and connect diverse data sources into Power BI. It outlines Power Query as the core ETL tool (Extract, Transform, Load) and describes the general workflow: Home → Get Data → choose a connector → preview → Load or Transform Data. The post gives step-by-step examples for common sources including Excel, CSV, PDF, JSON (and web APIs), SharePoint Folder, MySQL, SQL Server, and Azure Analysis Services. It explains connector counts (100+ available), authentication prompts, and the two main connection modes—Import (copy data in) and DirectQuery (live queries). The guide emphasizes checking sources, choosing the right connection mode, and using Power Query to clean and combine scattered data before building reports.
Practical beginner tutorial on Power BI data ingestion; useful for analysts but not industry-shifting for AdTech/MarTech.
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Key Takeaways & Evidence Grounding
- Power Query is the primary ETL tool inside Power BI for extracting, transforming, and loading data.
- Power BI provides connectors for over 100 data sources and supports Import and DirectQuery connection modes.
- The article includes step-by-step instructions for connecting Excel, CSV, PDF, JSON (APIs), SharePoint Folder, MySQL, SQL Server, and Azure Analysis Services.
- Microsoft offers free training and guidance on source-checking, connection mode selection, and performance considerations.
Connected Companies & Entities
2 Entities mappedOntology Mapping & Concepts
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Power BI: Connect, Preview and Transform Any Data
This technical how-to guides data analysts through connecting nine common data sources to Power BI and preparing them with Power Query. It covers connection steps for Excel, CSV, PDF, JSON, SharePoint folders, SQL Server, MySQL, web endpoints, and Azure Analysis Services, and shows how to launch Power Query, preview and profile data (Column Quality, Distribution, Profile), expand hierarchical JSON, authenticate to SharePoint/Azure sources, choose Import vs DirectQuery for databases, and apply transformations before loading to the data model. The article emphasizes profiling data quality, documenting applied transformations, and preserving relationships when combining multiple sources into a single Power BI model.
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 Guide
This article explains core concepts of data modeling in Power BI, Microsoft's business intelligence and visualization tool. It defines fact tables (central, transaction/measure stores with foreign keys), dimension tables (descriptive tables used for filtering and grouping), and schema types (star and snowflake). The piece covers relationships between tables, primary and foreign keys, and cardinality types (one-to-many, one-to-one, many-to-many). It provides examples using a hypothetical student dataset to illustrate descriptive columns, numeric measures, repeated values, and key mappings. The focus is practical: how tables are organized and connected to build efficient BI models in Power BI.
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