Microsoft MCSA: SQL Server 2016 - Business Intelligence Development

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On this accelerated 5 day MCSA: SQL Server 2016 Business Intelligence Development course, you’ll learn to implement a SQL Server 2016 data warehouse solution to support a business intelligence. You’ll go on to learn how to use the capabilities of SQL Server 2016 to create business intelligence solutions.

These skills will allow you to query, process, analyse and report on a vast amount of raw business data. Skills you’ll learn will include creating multidimensional databases and cubes, using MDX, DAX and data mining.

Your accelerated course will be taught by Microsoft Certified Trainers (MCT), using Microsoft Official Curriculum (MOC) and practice tests. You’ll also use Firebrand’s unique Lecture | Lab | Review technique, covering topics like:

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  • Designing and implementing a data warehouse
  • Implementing an Azure SQL data warehouse
  • Working with measures and measure groups
  • Performing predictive analysis with data mining

This course is best suited towards database professionals who work with SQL Server 2016 and conduct analysis using business intelligence solutions.

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Her er 8 grunner til hvorfor du skal gjennomføre ditt MCSA Business Intelligence Dev hos Firebrand Training:

  1. Du blir utdannet og sertifisert på bare 5 dager. Hos oss får du din utdanning og sertifisering på rekordtid, en sertifisering du også gjennomfører der og da som en integrert del av den intensive, akselererte utdanningen.
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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

You'll sit the following exams, covered by your Certification Guarantee:

Exam 70-767: Implementing a SQL Data Warehouse

The skills you'll be tested on include the following:

  • Design and implement a data warehouse (35-40%)
  • Extract, transform and load data (30-45%)
  • Build data quality solutions (15-20%)

Exam 70-768: Developing SQL Data Models

The skills you'll be tested on include the following:

  • Design a multidimensional business intelligence BI semantic model (25-30%)
  • Design a tabular BI semantic model (20-25%)
  • Develop queries using Multidimensional Expressions (MDX) and Data Analysis Expressions (DAX) (15-20%)
  • Configure and maintain SQL Server Analysis Services (SSAS) (30-35%)

Microsoft Official Curriculum

  • MOC 20767C - Implementing a SQL Data Warehouse
  • MOC 20768C - Developing SQL Data Models

It is recommended you have at least 2 years’ experience of working with relational databases, including:

  • Designing a normalised database
  • Creating tables and relationships
  • Some exposure to basic programming constructs (such as looping and branching)

An awareness of key business priorities such as revenue, profitability, and financial accounting is desirable

Microsoft MCSA Business Intelligence Dev Kursdatoer

Microsoft - MCSA: SQL Server 2016 - Business Intelligence Development

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