Optimizing DAX Video Course By Sqlbi

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Optimizing DAX Video Course By Sqlbi provides instruction on enhancing the efficiency of queries and measures in DAX.

DAX serves as the primary programming language for Power Pivot in Excel, Power BI Desktop, and SSAS Tabular models in Microsoft SQL Server Analysis Services.

This video course is designed for Power BI users and Analysis Services developers who possess prior knowledge of the DAX language and are interested in enhancing performance by optimizing DAX formulae and models.

The objective of the video course is to equip you with the ability to identify bottlenecks in DAX calculations using Performance Analyzer and DAX Studio.

This will empower you with the knowledge to enhance performance by utilizing various DAX formulae or modifying the data model, if necessary.

Optimizing DAX Video Course By Sqlbi has more than 35 hours of lectures that go into the formula engine, the VertiPaq storage engine, and DirectQuery over SQL. By early 2024, further hours of lectures will be included, specifically focusing on the composite models part.

Viewing the films is possible at your convenience, and the system will diligently monitor your advancement.

Curriculum

Introduction

•  Introduction 
•  Prerequisites 
•  Sections and naming conventions

Download software, demos, and book

•  Software prerequisites
•  Hardware prerequisites
•  Download the demo files
•  Optimizing DAX book

Introducing optimization with examples

•  Introducing optimization with examples
•  Introduction
•  Optimizing DAX 
•  Optimizing the model
•  Conclusions
•  Links to external content

Introducing the Tabular query architecture

•  Introducing the Tabular query architecture
•  Introduction
•  Introducing the formula engine
•  Introducing VertiPaq and DirectQuery architectures
•  Introducing the VertiPaq storage engine
•  Introducing the DirectQuery over SQL storage engine
•  Introducing DirectQuery over AS
•  Introducing data islands and cross-island query resolution
•  Different types of models

Using the Power BI Desktop performance analyzer

•  Using the Power BI Desktop performance analyzer
•  Introduction
•  Running Performance Analyzer
•  Understanding the numbers reported by Performance Analyzer
•  Optimizing queries or measures?
•  What can be optimized
•  Saving performance data
•  Conclusions
•  Links to external content

Using DAX Studio

•  Using DAX Studio
•  Introduction
•  Installing DAX Studio
•  Introducing the metrics of a database
•  Introducing All Queries
•  Capturing Excel queries (MDX)
•  Introducing Load Performance Data
•  Introducing Query Plan and Server Timings
•  Links to external content

Introducing query plans

•  Introducing query plans
•  Introduction
•  Introducing the logical query plan
•  Introducing the physical query plan
•  Introducing storage engine queries
•  Query plans in DirectQuery
•  Conclusions

Understanding the DAX Formula Engine

•  Understanding the DAX Formula Engine
•  Introduction
•  Understanding data caches
•  Understanding materialization
•  Understanding callbacks
•  Formula engine with different storage engines
•  Understanding vertical fusion
•  Understanding horizontal fusion

Examples of formula engine calculations

•  Sales of best products
•  Top three colors
•  Measuring performance
•  Gathering important timings from the query plan
•  Analyzing query plans and timings
•  Conclusions

Understanding query plans

•  Understanding query plans
•  Introduction
•  Query plan structure
•  Query plan operator types
•  Interactions between the formula engine and the storage engine
•  Common query plan operators

Examples of query plans

•  Comparing SUM versus SUMX
•  IF versus IF.EAGER
•  Filtering with DAX versus using relationships
•  Understanding SWITCH optimization
•  Conclusions

Optimizing the formula engine

•  Optimizing the formula engine
•  Introduction
•  Optimizing datacache use
•  Sales of best products
•  Running total of sales and ABC analysis
•  Year-over-year customer growth as a percentage
•  Conclusions

Understanding the VertiPaq engine

•  Understanding the VertiPaq engine
•  Introduction
•  Using VertiPaq Analyzer
•  Tabular data types
•  Introduction to the VertiPaq columnar database
•  Understanding VertiPaq compression
•  Understanding segmentation and partitioning
•  Understanding the importance of sorting
•  Understanding VertiPaq relationships
•  Understanding attribute hierarchies

Optimizing VertiPaq model examples

•  Sales amount versus quantity and net price
•  Storing currency conversion data
•  Date time versus date and time
•  Conclusions

Understanding VertiPaq relationships

•  Understanding VertiPaq relationships
•  Introduction
•  Regular, unidirectional one-to-many relationships

•  Regular, one-to-one relationships
•  Limited, many-to-many cardinality relationships
•  Conclusions

Analyzing VertiPaq storage engine queries

•  Introduction
•  Analyzing simple xml SQL queries
•  Introducing basic VertiPaq functionalities
•  Introducing batches
•  Understanding internal and external SE queries
•  Understanding distinct count in SQL

Understanding VertiPaq joins and filters

•  Introducing VertiPaq joins
•  Introducing bitmap indexes
•  Introducing reverse joins

Understanding VertiCalc and callbacks

•  Understanding VertiCalc and callbacks
•  Understanding CallbackDataID
•  Understanding EncodeCallback
•  Understanding LogAbsValueCallback
•  Understanding RoundValueCallback
•  Understanding MinMaxColumnPositionCallback
•  Understanding Cond
•  Understanding the VertiPaq cache
•  Choosing the correct data type for VertiPaq calculations
•  Conclusions

Optimizing common DAX constructs

•  Optimizing common DAX constructs
•  Introduction
•  Optimizing nested iterations
•  Understanding the effect of context transition
•  Different ways of performing a distinct count
•  Optimizing LASTDATE calculations
•  Avoid using SUMMARIZE and clustering
•  Optimizing division by checking for zeroes
•  Reducing the extent of the search by removing blanks
•  Optimizing time intelligence calculations
•  Distinct count over large cardinality columns
•  Conclusions
•  Links to external content

Moving and applying filters to tables

•  Moving and applying filters to tables
•  Introduction

Different filters in CALCULATE

•  Different filters in CALCULATE
•  Analyzing single-column filters
•  Analyzing multiple-column filters
•  Analyzing filters over multiple tables

•  Understanding sparse or dense filters
•  Filter columns, not tables

Modeling many-to-many relationships

•  Modeling many-to-many relationships
•  Testing the bidirectional model
•  Testing the star model
•  Testing the snake model
•  Testing the advanced snake model
•  Conclusions

Optimization examples for VertiPaq

•  Optimization examples for VertiPaq
•  Introduction
•  Reducing nested iterations
•  Optimizing complex filters in CALCULATE
•  Running faster than Fusion
•  Currency conversion
•  Optimizing cumulative totals
•  Average price variation of products over stores
•  Optimizing number of days with no sales
•  Computing open orders
•  Optimizing SWITCH and nested measures
•  Conclusions

Understanding security optimizations

•  Understanding security optimizations
•  Introduction
•  Testing security conditions and their performance impact
•  Understanding when and where security is enforced
•  Understanding cached bitmap indexes and embedded filters
•  Optimizing dynamic security
•  Optimizing static security on the fact table
•  Optimizing dynamic security on the fact table
•  Conclusions

Understanding DirectQuery over SQL 

•  Understanding DirectQuery over SQL
•  Introduction
•  Reading SQL code in this course
•  Reading the numbers in DAX Studio
•  Callback operations
•  Calculated tables
•  Calculated columns
•  How caching works in DirectQuery over SQL
•  Understanding latency to send queries to the remote server
•  Max number of rows in a data cache

Different types of relationships

• Regular one-to-many relationships
• Limited many-to-many relationships
• One-to-one relationships
• DirectQuery over SQL max parallel queries 
• Using different data islands
• Introducing aggregations and hybrid tables
• Conclusions

Optimizing DirectQuery over SQL 

•  Optimizing DirectQuery over SQL
•  Introduction

Building an SQL data model for Analysis Services

•  Introduction
•  Designing indexes
•  Using columnstore indexes
•  Choosing column data types
•  Do not use Power Query transformations

Optimizing relationships

•  Choosing the best data type for relationships
•  Relying on referential integrity
•  Using COMBINEVALUES to implement multi-column relationships

Using aggregations

•  Introducing aggregations
•  Introducing VertiPaq aggregation and Dual storage mode
•  Designing aggregations for simple calculations
•  Designing aggregations for row-level calculations
•  Designing aggregations for distinct count
•  Aggregations are not VertiPaq aliases of DirectQuery tables
•  Manually activating aggregations in DAX
•  Using automatic aggregations

Using hybrid tables

•  Introducing hybrid tables
•  Hybrid tables and distinct count
•  Creating hybrid tables with incremental refresh
•  Conclusions

Optimization examples for DirectQuery 

•  Optimization examples for DirectQuery
•  Introduction
•  Optimizing LASTDATE calculations
•  Optimizing division by checking for zeroes
•  Optimizing time intelligence calculations
•  Computing distinct counts
•  Conclusions

Conclusion

•  Conclusion

Enroll today to begin your journey of exploration and improvement in “Optimizing DAX Video Course By Sqlbi”

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