## What is AXEL? AXEL (Analytics eXpression Language) is a powerful, DAX-inspired query language designed for business intelligence and data analytics. With 81 functions across 8 categories, AXEL enables you to perform complex data transformations, time intelligence calculations, and advanced analytics with ease. ## 📊 Quick Statistics - **81 Total Functions** - Complete coverage of analytics needs - **8 Categories** - Logically organized for easy learning - **100% DAX Compatible** - Familiar syntax for Power BI users - **Production Ready** - Battle-tested in real-world scenarios ## 🎯 Function Categories Navigate to specific categories to explore functions in detail: ### [📊 Basic Aggregation](/university/axel-aggregation) 13 essential functions for summarizing data: SUM, AVG, COUNT, MIN, MAX, DISTINCTCOUNT, and X-variants (SUMX, AVGX, etc.) ### [🔍 Filter & Context](/university/axel-filter-context) 9 functions for data filtering and context manipulation: FILTER, CALCULATE, ALL, VALUES, and more. ### [📅 Date & Time](/university/axel-date-time) 26 powerful time intelligence functions: Date calculations, period comparisons, and time-based analytics. ### [🔢 Math Operations](/university/axel-math) 6 mathematical functions: ABS, SQRT, ROUND, DIVIDE with safe error handling. ### [📝 Text Functions](/university/axel-text) 7 string manipulation functions: UPPER, LOWER, TRIM, LEN, and type conversions. ### [📋 Table Operations](/university/axel-table-operations) 11 advanced table functions: SUMMARIZE, PIVOT, UNPIVOT, UNION, and more. ### [🚀 Advanced Analytics](/university/axel-advanced) 8 sophisticated functions: RANKX, TOPN, LET variables, and relationship functions. ### [✅ Logical Functions](/university/axel-logical) Essential logical operations including ISBLANK for data validation. ## 🚀 Quick Start Examples ### Calculate Total Sales ```sql SUM([Sales].Amount) ``` ### Year-over-Year Growth ```sql DIVIDE( CALCULATE(SUM([Sales].Amount), YEAR([Sales].Date) = 2024), CALCULATE(SUM([Sales].Amount), YEAR([Sales].Date) = 2023), 0 ) - 1 ``` ### Top 5 Products by Revenue ```sql TOPN(5, [Products], [Revenue], DESC) ``` ### Monthly Average with Filter ```sql CALCULATE( AVG([Metrics].Value), [Metrics].Month = "January" ) ``` ## 🎓 Learning Path ### Beginners Start with **Basic Aggregation** and simple **Filter** functions. Master SUM, COUNT, AVG before moving forward. ### Intermediate Explore **Date & Time** functions and **CALCULATE** for context manipulation. Learn table operations like SUMMARIZE. ### Advanced Master **Time Intelligence**, complex **Table Operations**, and **Advanced Analytics** with RANKX and LET variables. ## 💡 Best Practices 1. **Performance First** - Use simple aggregations (SUM) over iterators (SUMX) when possible 2. **Clear Naming** - Use descriptive names in SUMMARIZE and SELECTCOLUMNS 3. **Error Handling** - Always use DIVIDE instead of "/" operator 4. **Date Ranges** - Explicitly specify date ranges for predictable results ## 📚 Additional Resources - **Sample Dataset**: Monthly Report table with real-world data - **Testing Environment**: Local AXEL endpoint for experimentation - **Community Forum**: Mars University Data Analytics community ## 🏁 Next Steps Ready to dive deeper? Start with: 1. [Basic Aggregation Functions](/university/axel-aggregation) - Master the fundamentals 2. [Filter & Context](/university/axel-filter-context) - Control your data scope 3. [Date & Time Intelligence](/university/axel-date-time) - Time-based analysis ---