Now, let's move to the next important Power BI topic: *💧 Power BI Dataflows – Must-Know for Data Pros!* *🚀 What Are Power BI Dataflows?* Cloud-based *ETL* (Extract, Transform, Load) service to prepare and transform data *independently* of a report. Uses *Power Query Online* (same M language as Power BI Desktop) and stores data in Power BI Service. *🧠 Why Use Dataflows?* • Reuse logic across reports • Reduce transformation duplication • Enable centralized governance • Share cleaned data • Support large datasets + incremental refresh *🛠️ Key Features:* 1️⃣ *Reusability* – One-time transformation, multiple report use 2️⃣ *Cloud Storage* – Data saved in Azure Data Lake Gen2 (Premium) 3️⃣ *Scheduling* – Auto-refresh without opening PBIX 4️⃣ *Linked Entities* – Reference other dataflows like query chaining 5️⃣ *CDM Support* – Maintain semantic consistency *📌 When to Use Dataflows:* ✔ Same data logic used across reports ✔ Central governance needed ✔ Scaling data prep ✔ Enabling self-service BI *💼 Example Scenario:* Your team builds separate reports for Sales, Marketing, and Finance. Rather than repeat data cleaning steps, do this: ✅ Create a dataflow for *Customer* and *Product* tables ✅ Use that single dataflow in all your reports *🧾 Limitations:* ⚠️ Needs Power BI Pro or Premium ⚠️ Not ideal for real-time updates ⚠️ Slight learning curve *✅ Summary Table* • *Purpose:* Cloud-based data prep • *Stored In:* Power BI Service / Azure • *Tool:* Power Query Online • *Main Benefit:* Centralized, reusable transformation • *Best For:* Sharing cleaned data across reports *React ❤️ for more!*