Microsoft - MCSA: SQL Server 2016 - Business Intelligence Development

Lehrplan

Im MCSA: SQL Server 2016 - Business Intelligence Development Kurs behandeln Sie folgende Inhalte:

Course 20767C: Implementing a SQL Data Warehouse

Module 1: Introduction to Data Warehousing

This module describes data warehouse concepts and architecture consideration.

Lessons

• Overview of Data Warehousing
• Considerations for a Data Warehouse Solution

Lab : Exploring a Data Warehouse Solution

• Exploring data sources
• Exploring an ETL process
• Exploring a data warehouse

After completing this module, you will be able to:

• Describe the key elements of a data warehousing solution
• Describe the key considerations for a data warehousing solution

Module 2: Planning Data Warehouse Infrastructure

This module describes the main hardware considerations for building a data warehouse.

Lessons

• Considerations for data warehouse infrastructure.
• Planning data warehouse hardware.

Lab : Planning Data Warehouse Infrastructure

• Planning data warehouse hardware

After completing this module, you will be able to:

• Describe the main hardware considerations for building a data warehouse
• Explain how to use reference architectures and data warehouse appliances to create a data warehouse

Module 3: Designing and Implementing a Data Warehouse

This module describes how you go about designing and implementing a schema for a data warehouse.

Lessons

• Data warehouse design overview
• Designing dimension tables
• Designing fact tables
• Physical Design for a Data Warehouse

Lab : Implementing a Data Warehouse Schema

• Implementing a star schema
• Implementing a snowflake schema
• Implementing a time dimension table

After completing this module, you will be able to:

• Implement a logical design for a data warehouse
• Implement a physical design for a data warehouse

Module 4: Columnstore Indexes

This module introduces Columnstore Indexes.

Lessons

• Introduction to Columnstore Indexes
• Creating Columnstore Indexes
• Working with Columnstore Indexes

Lab : Using Columnstore Indexes

• Create a Columnstore index on the FactProductInventory table
• Create a Columnstore index on the FactInternetSales table
• Create a memory optimised Columnstore table

After completing this module, you will be able to:

• Create Columnstore indexes
• Work with Columnstore Indexes

Module 5: Implementing an Azure SQL Data Warehouse

This module describes Azure SQL Data Warehouses and how to implement them.

Lessons

• Advantages of Azure SQL Data Warehouse
• Implementing an Azure SQL Data Warehouse
• Developing an Azure SQL Data Warehouse
• Migrating to an Azure SQ Data Warehouse
• Copying data with the Azure data factory

Lab : Implementing an Azure SQL Data Warehouse

• Create an Azure SQL data warehouse database
• Migrate to an Azure SQL Data warehouse database
• Copy data with the Azure data factory

After completing this module, you will be able to:

• Describe the advantages of Azure SQL Data Warehouse
• Implement an Azure SQL Data Warehouse
• Describe the considerations for developing an Azure SQL Data Warehouse
• Plan for migrating to Azure SQL Data Warehouse

Module 6: Creating an ETL Solution

At the end of this module you will be able to implement data flow in a SSIS package.

Lessons

• Introduction to ETL with SSIS
• Exploring Source Data
• Implementing Data Flow

Lab : Implementing Data Flow in an SSIS Package

• Exploring source data
• Transferring data by using a data row task
• Using transformation components in a data row

After completing this module, you will be able to:

• Describe ETL with SSIS
• Explore Source Data
• Implement a Data Flow

Module 7: Implementing Control Flow in an SSIS Package

This module describes implementing control flow in an SSIS package.

Lessons

• Introduction to Control Flow
• Creating Dynamic Packages
• Using Containers
• Managing consistency.

Lab : Implementing Control Flow in an SSIS Package

• Using tasks and precedence in a control flow
• Using variables and parameters
• Using containers

Lab : Using Transactions and Checkpoints

• Using transactions
• Using checkpoints

After completing this module, you will be able to:

• Describe control flow
• Create dynamic packages
• Use containers

Module 8: Debugging and Troubleshooting SSIS Packages

This module describes how to debug and troubleshoot SSIS packages.

Lessons

• Debugging an SSIS Package
• Logging SSIS Package Events
• Handling Errors in an SSIS Package

Lab : Debugging and Troubleshooting an SSIS Package

• Debugging an SSIS package
• Logging SSIS package execution
• Implementing an event handler
• Handling errors in data flow

After completing this module, you will be able to:

• Debug an SSIS package
• Log SSIS package events
• Handle errors in an SSIS package

Module 9: Implementing a Data Extraction Solution

This module describes how to implement an SSIS solution that supports incremental DW loads and changing data

Lessons

• Introduction to Incremental ETL
• Extracting Modified Data
• Loading modified data
• Temporal Tables

Lab : Extracting Modified Data

• Using a datetime column to incrementally extract data
• Using change data capture
• Using the CDC control task
• Using change tracking

Lab : Loading a data warehouse

• Loading data from CDC output tables
• Using a lookup transformation to insert or update dimension data
• Implementing a slowly changing dimension
• Using the merge statement

After completing this module, you will be able to:

• Describe incremental ETL
• Extract modified data
• Load modified data.
• Describe temporal tables

Module 10: Enforcing Data Quality

This module describes how to implement data cleansing by using Microsoft Data Quality services.

Lessons

• Introduction to Data Quality
• Using Data Quality Services to Cleanse Data
• Using Data Quality Services to Match Data

Lab : Cleansing Data

• Creating a DQS knowledge base
• Using a DQS project to cleanse data
• Using DQS in an SSIS package

Lab : De-duplicating Data

• Creating a matching policy
• Using a DS project to match data

After completing this module, you will be able to:

• Describe data quality services
• Cleanse data using data quality services
• Match data using data quality services
• De-duplicate data using data quality services

Module 11: Using Master Data Services

This module describes how to implement master data services to enforce data integrity at source.

Lessons

• Introduction to Master Data Services
• Implementing a Master Data Services Model
• Hierarchies and collections
• Creating a Master Data Hub

Lab : Implementing Master Data Services

• Creating a master data services model
• Using the master data services add-in for Excel
• Enforcing business rules
• Loading data into a model
• Consuming master data services data

After completing this module, you will be able to:

• Describe the key concepts of master data services
• Implement a master data service model
• Manage master data
• Create a master data hub

Module 12: Extending SQL Server Integration Services (SSIS)

This module describes how to extend SSIS with custom scripts and components.

Lessons

• Using scripting in SSIS
• Using custom components in SSIS

Lab : Using scripts

• Using a script task

After completing this module, you will be able to:

• Use custom components in SSIS
• Use scripting in SSIS

Module 13: Deploying and Configuring SSIS Packages

This module describes how to deploy and configure SSIS packages

.Lessons

• Overview of SSIS Deployment
• Deploying SSIS Projects
• Planning SSIS Package Execution

Lab : Deploying and Configuring SSIS Packages

• Creating an SSIS catalogue
• Deploying an SSIS project
• Creating environments for an SSIS solution
• Running an SSIS package in SQL server management studio
• Scheduling SSIS packages with SQL server agent

After completing this module, you will be able to:

• Describe an SSIS deployment
• Deploy an SSIS package
• Plan SSIS package execution

Module 14: Consuming Data in a Data Warehouse

This module describes how to debug and troubleshoot SSIS packages.

Lessons

• Introduction to Business Intelligence
• An Introduction to Data Analysis
• Introduction to reporting
• Analysing Data with Azure SQL Data Warehouse

Lab : Using a data warehouse

• Exploring a reporting services report
• Exploring a PowerPivot workbook
• Exploring a power view report

After completing this module, you will be able to:

• Describe at a high level business intelligence
• Show an understanding of reporting
• Show an understanding of data analysis
• Analyse data with Azure SQL data warehouse

Course 20768C: Developing SQL Data Models

Module 1: Introduction to Business Intelligence and Data Modeling

This module introduces key BI concepts and the Microsoft BI product suite.

Lessons

• Introduction to Business Intelligence
• The Microsoft business intelligence platform

Lab :;Exploring a BI Solution

• Exploring a Data Warehouse
• Exploring a data model

After completing this module, you'll be able to:

• Describe BI scenarios, trends, and project roles.
• Describe the products that make up the Microsoft BI platform.

Module 2: Creating Multidimensional Databases

This module describes how to create multidimensional databases using SQL Server Analysis Services.

Lessons

• Introduction to Multidimensional Analysis
• Data Sources and Data Source Views
• Cubes
• Overview of Cube Security
• Configure SSAS
• Monitoring SSAS

Lab : Creating a multidimensional database

• Creating a Data Source
• Creating and Configuring a data Source View
• Creating and Configuring a Cube
• Adding a Dimension to a Cube

After completing this module, you will be able to:

• Describe considerations for a multidimensional database.
• Create data sources and data source views.
• Create a cube
• Implement security in a multidimensional database.
• Configure SSAS to meet requirements including memory limits, NUMA and disk layout.
• Monitor SSAS performance.

Module 3: Working with Cubes and Dimensions

This module describes how to implement dimensions in a cube.

Lessons

• Configuring Dimensions
• Defining Attribute Hierarchies
• Implementing Sorting and Grouping Attributes
• Slowly Changing Dimensions

Lab : Working with Cubes and Dimensions

• Configuring Dimensions
• Defining Relationships and Hierarchies
• Sorting and Grouping Dimension Attributes

After completing this module, you will be able to:

• Configure dimensions.
• Define attribute hierarchies.
• Implement sorting and grouping for attributes.
• Implement slowly changing dimensions.

Module 4: Working with Measures and Measure Groups

This module describes how to implement measures and measure groups in a cube.

Lessons

• Working with Measures
• Working with Measure Groups

Lab : Configuring Measures and Measure Groups

• Configuring Measures
• Defining Regular Relationships
• Configuring Measure Group Storage

After completing this module, you will be able to:

• Configure measures.
• Configure measure groups.

Module 5: Introduction to MDX

This module describes the MDX syntax and how to use MDX.

Lessons

• MDX fundamentals
• Adding Calculations to a Cube
• Using MDX to Query a Cube

Lab : Using MDX

• Querying a cube using MDX
• Adding a Calculated Member

After completing this module, you will be able to:

• Use basic MDX functions.
• Use MDX to add calculations to a cube.
• Use MDX to query a cube.

Module 6: Customising Cube Functionality

This module describes how to customise a cube.

Lessons

• Implementing Key Performance Indicators
• Implementing Actions
• Implementing Perspectives
• Implementing Translations

Lab : Customising a Cube

• Implementing an action
• Implementing a perspective
• Implementing a translation

After completing this module, you will be able to:

• Implement KPIs in a Multidimensional database
• Implement Actions in a Multidimensional database
• Implement perspectives in a Multidimensional database
• Implement translations in a Multidimensional database

Module 7: Implementing a Tabular Data Model by Using Analysis Services

This module describes how to implement a tabular data model in Power Pivot.

Lessons

• Introduction to Tabular Data Models
• Creating a Tabular Data Model
• Using an Analysis Services Tabular Data Model in an Enterprise BI Solution

Lab : Working with an Analysis Services Tabular Data Model

• Creating an Analysis Services Tabular Data Model
• Configure Relationships and Attributes
• Configuring Data Model for an Enterprise BI Solution.

After completing this module, you'll be able to:

• Describe tabular data models
• Describe how to create a tabular data model
• Use an Analysis Services Tabular Model in an enterprise BI solution

Module 8: Introduction to Data Analysis Expression (DAX)

This module describes how to use DAX to create measures and calculated columns in a tabular data model.

Lessons

• DAX Fundamentals
• Using DAX to Create Calculated Columns and Measures in a Tabular Data Model

Lab : Creating Calculated Columns and Measures by using DAX

• Creating Calculated Columns
• Creating Measures
• Creating a KPI
• Creating a Parent - Child Hierarchy

After completing this module, you'll be able to:

• Describe the key features of DAX
• Create calculated columns and measures by using DAX

Module 9: Performing Predictive Analysis with Data Mining

This module describes how to use data mining for predictive analysis.

Lessons

• Overview of Data Mining
• Creating a Custom Data Mining Solution
• Validating a Data Mining Model
• Connecting to and Consuming a Data-Mining Model
• Using the Data Mining add-in for Excel

Lab : Using Data Mining

• Creating a Data Mining Structure and Model
• Exploring Data Mining Models
• Validating Data Mining Models
• Consuming a Data Mining Model
• Using the Excel Data Mining add-in

After completing this module, you'll be able to:

• Describe considerations for data mining
• Create a data mining model
• Validate a data mining model
• Connect to a data-mining model
• Use the data mining add-in for Excel