Microsoft Fabric Analytics Engineer Associate Study Guide. Hands-On Practice and Expert Tips for Acing the DP-600 Certification Exam Brian Břnk, Valerie Junk

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Spis treści książki

• Foreword
• Preface
  • Who Should Take the Exam
  • The Official Curriculum
  • The Purpose of This Book
  • Book Summary
  • Conventions Used in This Book
  • OReilly Online Learning
  • How to Contact Us
  • Acknowledgements
• 1. Introduction to Microsoft Fabric and the Certification Content
  • Key Components and Architecture
  • A Brief History of the Certification Content
  • End-to-End Analytics Overview
  • Setting Up a Fabric Environment
    • Fabric Capacity
    • Creating a Fabric-Enabled Workspace
    • Configuring a Workspace
  • Summary
• I. Preparing Data
• 2. Getting Data
  • Discovering Data with the OneLake Catalog and Real-Time Hub
    • The OneLake Catalog
    • The Real-Time Hub
    • OneLake Catalog versus Real-Time Hub
  • Creating Connections and Ingesting or Accessing Data
    • Creating Shortcuts to Data from Inside and Outside Fabric
    • Dataflow Gen1 and Dataflow Gen2
      • Dataflow Gen1
        • Defining new tables
        • Linking tables from other dataflows
        • Importing a model
      • Dataflow Gen2
    • Data Connections from Power BI
    • Data Connections to Power BI
      • KQL Queries to Power BI
        • KQL queries in Power BI Desktop
        • KQL Queries in Power BI web experience
  • Choosing a Lakehouse, Warehouse, or Eventhouse
    • Lakehouse
    • Warehouse
    • Eventhouse
  • Summary
• 3. Transforming Data
  • Creating Views, Functions, and Stored Procedures
    • Views
    • Functions
      • Scalar functions
      • Table-valued functions
    • Stored Procedures
  • Enriching Data
    • Enriching Data in the Fabric Warehouse with SQL
    • Enriching Data in the Fabric Lakehouse with Notebooks
    • Enriching Data in the Fabric Lakehouse with Dataflow Gen2
  • Refining Data
    • Joins for Combining Data
    • Joins in a Fabric Warehouse
    • Joins in a Fabric Lakehouse Using Notebooks
    • Joins in a Fabric Lakehouse Using Dataflow Gen2
  • Summarizing Data
    • Aggregations to Summarize Data
    • Aggregation in a Fabric Warehouse with SQL
    • Aggregation in a Fabric Lakehouse with Notebooks
    • Aggregation in a Fabric Lakehouse with Dataflow Gen2
    • Filters and Focusing on Relevant Data
  • Filtering Data
    • Filtering in a Fabric Warehouse with SQL
    • Filtering in a Fabric Lakehouse with Notebooks
    • Filtering in a Fabric Lakehouse with Dataflow Gen2
  • Data Types
  • Handling and Resolving Duplicate, Missing, and NULL Values
  • Deduplication: Removing Duplicate Data
    • Deduplication in a Fabric Warehouse with SQL
    • Deduplication in a Fabric Lakehouse with Notebooks
    • Deduplication in a Fabric Lakehouse with Dataflow Gen2
  • Handling NULLs and Missing Values
    • Handling NULLs in a Fabric Warehouse with SQL
    • Handling NULLs in a Fabric Lakehouse with Notebooks
    • Handling NULLs in a Fabric Lakehouse with Dataflow Gen2
  • Star Schema for Fabric Lakehouse and Warehouse
    • Star Schema versus Snowflake Schema
    • Implementing a Star Schema in a Fabric Lakehouse
      • Implementing with PySpark notebooks
      • Implementing with Dataflow Gen2
    • Implementing a Star Schema in a Fabric Warehouse with SQL
    • General Principles of Dimensional Modeling
  • Denormalization of Data
    • Denormalizing Tables with Power Query
  • Summary
• 4. Querying and Analyzing Data
  • The Visual Query Editor
    • Working with Tables Using the Visual Query Editor
      • Joins
      • Fuzzy joins
    • Switching from Visual Query Editor to T-SQL Statements
    • Cross-Lakehouse and Cross-Warehouse Queries
    • Saving a Query or a Querys Output
      • Saving a query as a view
      • Saving the output of a query to a table
    • Limitations
  • Lineage and Debugging
    • Clicking on Each Box with Steps to View Query Results
    • Clicking on Each Step in Each Query to View Results of That Step
    • Viewing the Data Source Query
    • Viewing the Query Plan
  • Manipulating Data with SQL
    • Where to Find the SQL Language
    • The T-SQL Surface Area
  • Manipulating Data with KQL
    • Auto-Creating a KQL Queryset While Creating an Eventhouse
    • Creating a Standalone KQL Queryset
  • Using T-SQL in the Kusto Database
  • Structure of a KQL Statement
    • Starting a New Line of Code
    • Funneling Data with KQL Statements
    • The Simplest KQL Statement
  • Common KQL Operators
    • Counting Rows
    • Getting a Subset of Rows of Data and the First n Rows
    • Getting a Column Subset of Data
    • Getting a List of Unique Values
    • Filtering a Results Set
    • Sorting a Results Set
    • Row-Based Calculations
    • Performing Aggregations
    • Creating Joins
      • Join approaches
        • The join approach
        • The lookup approach
    • Creating Visualizations
  • Summary
• II. Implementing and Managing Semantic Models
• 5. Designing and Building Semantic Models
  • Choosing a Storage Mode
    • Import Mode
      • Strengths
      • Weaknesses
    • DirectQuery Mode
      • Strengths
      • Weaknesses
    • Direct Lake Mode
      • Strengths
      • Weaknesses
    • Dual Mode
      • Strengths
      • Weaknesses
  • Star Schemas for Semantic Models
  • Creating Relationships in a Semantic Model
    • Cardinality: Different Types of Relationships
    • Cross-Filter Direction
      • Single
      • Both
    • Active and Inactive Relationships
    • Disconnected Tables
  • Implementing Calculation Groups, Field Parameters, and Dynamic Format Strings
    • Calculation Groups
      • Creating a calculation group
      • Dynamic format strings for calculation Items
    • Field Parameters
      • Creating field parameters
      • Modifying field parameters
      • Limitations
    • Dynamic Format Strings
      • Creating dynamic format strings
      • Benefits and limitations
  • Large Semantic Model Storage Format
  • Designing and Building Composite Models
    • Creating a Composite Model
    • Benefits and Limitations
  • Summary
• 6. Optimizing Enterprise-Scale Semantic Models
  • Understanding Performance Metrics in DAX Queries and Report Visuals
    • DAX INFO.VIEW Functions
    • DAX Query View Examples
  • Performance Testing in Power BI: Performance Analyzer
    • How the Performance Analyzer in Power BI Works
  • Implementing Improvements in Performance for DAX
    • Using Variables to Improve DAX Formulas
    • Counting Table Rows: COUNTROWS versus COUNT
    • Converting NULL to 0
    • Using DIVIDE Instead of the / Operator
    • Are IFERROR and ISERROR as Helpful as They Look?
    • Special DAX Functions: ORDERBY, PARTITIONBY, and MATCHBY
      • ORDERBY
      • PARTITIONBY
      • MATCHBY
  • Direct Lake
    • Implementing and Configuring Direct Lake
    • DirectQuery as a Fallback for Direct Lake
    • Refreshing Direct Lake Models
    • Incremental Refresh for Semantic Models
      • Requirements
      • Implementing Incremental Refresh in Power BI
      • Publishing
      • Limitations
  • Summary
• III. Maintaining a Data and Analytics Solution
• 7. Implementing Security and Governance
  • Implementing Workspace-Level Access Controls
    • Configuring Role Access in a Workspace
    • Changing Existing Roles in a Workspace
    • Adding People or Groups to a Role in a Workspace
  • Implementing Item-Level Access Controls
    • Permission Types
    • Configuring Item-Level Access for Users or Groups
    • Permissions for an Item
  • Controlling Access to Compute
  • Implementing Row-Level, Column-Level, Object-Level, and File-Level Access Control
  • Power BI Security
    • Row-Level Security in Power BI
      • Fixed row-level security
      • Dynamic row-level security
  • Warehouse Security
    • Row-Level Security in a Warehouse
    • Column-Level Security in a Warehouse
    • Object-Level Security in a Warehouse
  • Lakehouse Security
  • Applying Sensitivity Labels to Items
    • The Different Sensitivity Labels
    • How to Use Them
  • Endorsing Items
    • The Different Endorsements
    • How to Use Them
  • Summary
• 8. Maintaining the Analytics Development Lifecycle
  • Configuring Version Control for a Workspace
    • Setting Up GitHub and Azure DevOps Version Control
      • Setting up GitHub version control
      • Setting Up Azure DevOps version control
    • Working with Version Control in Fabric
    • Benefits and Limitations of Version Control
  • Power BI Project Files
    • Saving a Power BI Project File
    • Benefits of Using Power BI Project Files
    • Limitations of Power BI Project Files
  • Reusable Assets in Power BI
    • Power BI Template Files
    • Creating a Power BI Template File
    • Using Report Templates
    • Power BI Data Source (.pbids) Files
    • Creating a Power BI Data Source File
  • Shared Semantic Models
    • Connecting to a Shared Semantic Model
  • Deployment Pipelines
    • Example Scenario
    • Creating a Deployment Pipeline
      • Top menu (area 1)
        • New deployment pipelines
        • Deployment history
        • Manage access
        • Settings
      • Canvas (area 2)
      • Development stage details (area 3)
    • Configuring a Deployment Pipeline
    • Deploying Among Stages
    • Deployment History
    • Performing Impact Analyses of Downstream Dependencies
    • Using the Lineage View
    • Using Impact Analysis
    • Deploying and Managing Semantic Models Using the XMLA Endpoint
    • Setting Up the XMLA Endpoint
    • Managing Semantic Models from the XMLA Endpoint
    • Processing a Semantic Model
  • Summary
• 9. Question Bank
  • Questions
  • Answers
• Index

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