DAX Optimization and Performance
DAX Optimization and Performance, available at $64.99, has an average rating of 4.82, with 33 lectures, based on 67 reviews, and has 546 subscribers.
You will learn about The Importance of Proper Data Modeling and Star Schemas Dimension and Fact Tables (Normalization and Denormalization) The VertiPaq Engine and Compression Techniques Formula Engine, Storage Engine and xmSQL Using DAX Studio for Performance Tuning using a 100M Row Fact Table Data Caches The Dreaded CallbackDataId Common Optimization Scenarios and Techniques Practice Exercises and a Quiz This course is ideal for individuals who are Advance beginner, intermediate and experienced DAX developers It is particularly useful for Advance beginner, intermediate and experienced DAX developers.
Enroll now: DAX Optimization and Performance
Summary
Title: DAX Optimization and Performance
Price: $64.99
Average Rating: 4.82
Number of Lectures: 33
Number of Published Lectures: 33
Number of Curriculum Items: 33
Number of Published Curriculum Objects: 33
Original Price: $24.99
Quality Status: approved
Status: Live
What You Will Learn
- The Importance of Proper Data Modeling and Star Schemas
- Dimension and Fact Tables (Normalization and Denormalization)
- The VertiPaq Engine and Compression Techniques
- Formula Engine, Storage Engine and xmSQL
- Using DAX Studio for Performance Tuning using a 100M Row Fact Table
- Data Caches
- The Dreaded CallbackDataId
- Common Optimization Scenarios and Techniques
- Practice Exercises and a Quiz
Who Should Attend
- Advance beginner, intermediate and experienced DAX developers
Target Audiences
- Advance beginner, intermediate and experienced DAX developers
DAX can seem simple when just learning but then gets progressively more difficult and it is very hard to master. There are so many subtilties. This makes trying to figure out why your DAX code is underperforming, and fixing it, overwhelming at times. This course attempts to make this process easier. The course starts with the importance of proper data modeling and why a Star schema is so critical. We then cover the concepts of normalization and denormalization along with the use of dimension and fact tables. We cover in depth the importance of using the VertiPaq Analyzer when exploring the makeup of your data model.
The course then jumps into the VertiPaq engine and the techniques used to compress data. We cover why choosing the correct data types in your model is so critical and the importance of data cardinality in how well data is compressed, which directly affects the performance of all your DAX code.
We then explore the two engines that make up VertiPaq, the formula engine and storage engine. SQL Server Profiler and DAX Studio are introduced as two tools you can use to perform performance monitoring. DAX Studio is used heavily and many DAX Studio examples are used in demonstrating how important the tool is for performance monitoring and evaluation utilizing the server timings feature. We cover xmSQL and how useful it is in getting a handle on what the engines are doing when executing your DAX code. Multiple threads and the data cache are what makes the storage engine blazingly fast and we cover this in detail. The CallbackDataId is extensively covered and why we want to avoid it and how to avoid it.
We wrap up with at least a half dozen common optimization problems / scenarios and how to address them.
Course Curriculum
Chapter 1: Introduction
Lecture 1: Introduction
Lecture 2: Important! – Before Getting Started
Chapter 2: It All Starts With Data Modeling and the Star Schema
Lecture 1: Introduction
Lecture 2: Normalization and Denormalization
Lecture 3: Dimensions and Facts
Lecture 4: Why a Star Schema?
Chapter 3: The VertiPaq Engine
Lecture 1: Introduction
Lecture 2: Compression – Value Encoding
Lecture 3: Compression – Hash Encoding
Lecture 4: Compression – Run Length Encoding
Chapter 4: Understanding and Measuring Performance
Lecture 1: We're Working with Two Engines
Lecture 2: SQL Server Profiler
Lecture 3: DAX Studio
Lecture 4: The VertiPaq Analyzer
Lecture 5: VertiPaq Analyzer – A Practical Use Case
Lecture 6: Storage Engine Queries and xmSQL
Lecture 7: Data Cache
Lecture 8: CallbackDataID
Chapter 5: Common Optimization Scenarios
Lecture 1: Should I use SUM or SUMX?
Lecture 2: Checking for Divide by Zero
Lecture 3: More Complex IF Statements
Lecture 4: CALCULATE Does not Need a FILTER Function (Usually)
Lecture 5: Use Variables to Improve Performance and Maintainability
Lecture 6: Be Careful with SUMMARIZE
Lecture 7: Performance Analyzer
Chapter 6: Exercises and Quiz
Lecture 1: Exercise 1
Lecture 2: Exercise 2
Lecture 3: Exercise 3
Lecture 4: Exercise 4
Lecture 5: Exercise 5
Lecture 6: Exercise 6
Lecture 7: Quiz – Group 1 Questions
Lecture 8: Quiz – Group 2 Questions
Instructors
-
Randy Minder
Lead BI Developer / Power BI / Microsoft Fabric
Rating Distribution
- 1 stars: 0 votes
- 2 stars: 0 votes
- 3 stars: 1 votes
- 4 stars: 18 votes
- 5 stars: 48 votes
Frequently Asked Questions
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