Course Content
01 Introduction
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001 Introduction_part1
01:18
001 Introduction_part2
01:18
003 The Udemy Interface
02:00
005 What are we going to learn
06:55
02 Part 1 Level 1 Design and build semantic models
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001 Creating semantic model
06:00
002 29 1st Normal form
09:12
003 29 2nd and 3rd Normal form
06:40
004 27, 40 Implementing a star schema for a semantic model
10:39
006 Practice Activity Number 1 – The Solution
06:00
03 Part 1 Level 2 Calculation groupsitems, field parameters, and custom theme
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001 43a Enabling the calculation groups preview feature
05:39
002 43a Creating our first calculation group and item
08:16
003 43a Adding additional calculation items
07:01
004 43a, 43b Using our calculation group, including dynamic strings
05:21
005 43c Implement field parameters
06:05
006 4 Create a custom Power BI report theme
03:37
008 Practice Activity Number 2 – The Solution
08:30
04 Part 1 Level 3 DAX functions
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001 42a Using DAX variables
05:19
002 42d DAX Windowing functions ROWNUMBER
06:27
003 42d DAX Windowing functions RANK
04:15
004 42d DAX Windowing functions INDEX
02:56
005 42d DAX Windowing functions OFFSET
03:29
006 42d DAX Windowing functions WINDOW
03:10
008 Practice Activity Number 3 – The Solution
06:50
05 Part 1 Level 4 Tabular Editor 2 and DAX Studio
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001 39 Identify use cases for DAX Studio and Tabular Editor 2 (TE2), installing TE2
07:21
002 50 Optimize a semantic model by using Tabular Editor 2 (BPA)
05:20
003 46 Implement object-level security (OLS)
06:08
004 49 Improve DAX performance by using DAX Studio
06:13
005 49 Using the VertiPaq Analyzer
07:02
006 ALM Toolkit
08:11
007 Translating metadata
02:42
06 Part 1 Level 5 Relationships and performance improvements
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001 41 Resolve many-to-many relationships – Joint Bank Accounts
04:11
002 43b Implement dynamic strings
05:06
003 41 Resolve many-to-many relationships – creating a Bridge Table
04:51
004 48 The Optimize menu
07:16
07 Part 1 Level 6 Manage the analytics development lifecycle
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001 10 Creating a Premium Per User (PPU) workspace and Azure DevOps repos
06:59
002 10 Implement version control for a workspace
07:57
003 11 Create and manage a Power BI Desktop project (pbip)
04:26
004 12 Plan and implement deployment solutions
05:50
005 12 Managing deployment solutions
06:24
08 Part 1 Level 7 Other Power BI Analytics topics
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001 44 Design and build a large format semantic models
04:40
002 13 Perform impact analysis of downstream dependencies
04:13
003 14 Deploy and manage semantic models by using the XMLA endpoint
09:57
004 15a Creating and updating Power BI template (pbit) files
02:59
005 15b Creating and updating Power BI data source (pbids) files
03:01
006 15c Creating and updating shared semantic models
03:44
007 45 Create an Azure SQL Database, and Design and build a composite model
09:48
008 45 Adding aggregations
09:02
09 Part 1 – Practice Test for advanced Power BI semantic models
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10 Part 2 Section 1 – A look around Fabric
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001 8 Creating a Fabric capacity and configure Fabric-enabled workspace settings
07:07
002 1, 9 Identify requirements for a Fabric solution and manage Fabric capacity
07:03
003 A quick tour of Fabric
08:01
11 Part 2 Section 2 – Using data pipelines and dataflows
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001 16b, 22b Ingest data by using a dataflow
07:14
002 16b, 22b Add a destination to a dataflow
06:44
003 25a Saving as a template and scheduling a dataflow
04:00
004 16a, 23 Ingest data by using a data pipeline, and adding other activities
07:05
005 22a Copy data by using a data pipeline
09:30
006 24 Schedule data pipelines and monitor data pipeline runs
04:27
007 Exploring sample data (including copy data assistant) + data pipeline templates
01:57
008 22d Implement Fast Copy when using dataflows
03:12
010 Practice Activity Number 4 – The Solution
09:05
12 Part 2 Section 3 – Loading and saving data using notebooks
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001 21 Ingesting data into a lakehouse using a local upload
05:37
002 21 Choose an appropriate method for copying to a Lakehouse or Warehouse
03:04
003 16c, 22c Ingesting data using a notebook, and copying to a table
09:00
004 16c, 22c Saving data to a file or Lakehouse table
06:42
005 Loading data from a table in PySpark and SQL, and manipulating the results
07:17
007 Practice Activity Number 5 – The Solution
03:34
13 Part 2 Section 4 – Manipulating dataframes – choosing columns and rows
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005 34 More advanced filtering
07:28
14 Part 2 Section 5 – converting data types, aggregating and sorting dataframes
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001 33 Converting data types
06:09
002 16c, 33 Importing data using an explicit data structure
03:59
003 33 Formatting dates as strings
06:42
004 30, 34 Aggregating and re-filtering data
04:37
005 Sorting the results
05:52
006 Using all 6 SQL Clauses
04:49
15 Part 2 Section 6 – Transform data in a lakehouse
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001 31a Merging data
08:21
002 32a Identifying and resolving duplicate data
05:12
003 31b Joining data using an Inner join
06:32
004 31b Joining data using other joins
06:45
005 32b, 32c Identifying missing data or null values
07:33
007 28a Practice Activity Number 6 – Solution
03:02
008 25b Schedule notebooks
02:40
16 Part 2 Section 7 – Profiling data
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001 54 Profiling data – Creating a bar chart
06:31
002 54 Profiling data – Creating a pie chart
05:34
003 54 Profiling data – Creating a line chart
05:41
17 Part 2 Section 8 – Transform data in a data warehouse
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007 27b Implement Type 1 and Type 2 slowly changing dimensions – Theory
07:06
18 Part 2 Section 9 – Create objects
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001 17 Create and manage shortcuts
05:41
002 18 Implement file partitioning for analytics workloads using a pipeline
08:25
003 18 Implement file partitioning for analytics workloads – data is in a lakehouse
03:11
004 19 Create views, functions and stored procedures
08:21
005 26 Implement a data cleansing process
05:31
006 38 Choose a storage mode for a semantic model, including Direct Lake
02:33
19 Part 2 Section 10 – Optimize performance
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001 35a Identify and resolve data loading performance bottlenecks in dataflows
02:51
002 36a Implement performance improvements in dataflows
03:46
003 35b Identify and resolve data loading performance bottlenecks in notebooks
03:11
004 36b, 37 Implement performance improvements in notebooks, inc V-Order
03:54
005 35c Identify and resolve data loading bottlenecks in SQL queries
03:14
006 36c Implement performance improvements in SQL queries
06:43
007 37 Identify and resolve issues with Delta table file optimized writes
02:42
20 Part 2 Section 11 – Other Fabric topics
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001 2 Recommend settings in the Fabric admin portal
04:13
002 5, 6 Implement workspace and item-level access controls for Fabric items
05:09
003 9 Installing the Microsoft Fabric Capacity Metrics app
03:56
004 9 Using the Microsoft Fabric Capacity Metrics app – Manage Fabric capacity
07:14
21 Congratulations for completing the course
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001 What’s Next
01:33
002 Congratulations for completing the course
01:00
DP-600: Implement Analytics Solutions using Microsoft Fabric
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