MCSA: SQL Server 2016 BI Development Boot Camp

Our boot camps are geared towards providing students with the necessary skills and knowledge to not only pass the Microsoft Certification exams, but to also excel in their IT career paths.

Retail Price: $3,695.00

Next Date: Request Date

Course Days: 6


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About this Course

Our boot camps are geared towards providing students with the necessary skills and knowledge to not only pass the Microsoft Certification exams, but to also excel in their IT career paths.

All of our boot camps are all-inclusive and include benefits such as:

  • 100% Test Pass Guarantee
  • All course materials, practice exams and official certification exams
  • Onsite Prometric Testing Center
  • Hands-on instruction by a certified instructor
  • Breakfast and Lunch provided each day
  • Airfare, lodging and transportation packages available (Option 2)

Audience Profile

This boot camp intended for extract, transform, and load (ETL) and data warehouse developers who create business intelligence (BI) solutions. Their responsibilities include data cleansing, in addition to ETL and data warehouse implementation.

This boot camp is also intended for business intelligence (BI) developers who focus on creating BI solutions that require implementing multidimensional data models, implementing and maintaining OLAP cubes, and implementing tabular data models.

At Course Completion

Upon completing this course, students will be able to:

  • Design and implement a data warehouse
  • Extract, transform, and load data
  • Integrate solutions with cloud data and big data
  • Build data quality solutions
  • Design a multidimensional business intelligence (BI) semantic model (25–30%)
  • Design a tabular BI semantic model
  • Develop queries using Multidimensional Expressions (MDX) and Data Analysis Expressions (DAX)
  • Configure and maintain SQL Server Analysis Services (SSAS)

Course Outline

Exam 1

Module 1: Introduction to Data Warehousing

Describe data warehouse concepts and architecture considerations.

Lessons

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

Lab: Exploring a Data Warehouse Solution

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 Building a Data Warehouse
  • Data Warehouse Reference Architectures and Appliances

Lab: Planning Data Warehouse Infrastructure

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

  • Logical Design for a Data Warehouse
  • Physical Design for a Data Warehouse

Lab: Implementing a Data Warehouse Schema

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

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

Lab: Implementing an Azure SQL Data Warehouse

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

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

Lab: Implementing Control Flow in an SSIS Package

Lab: Using Transactions and 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

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 an Incremental ETL Process

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
  • Temporal Tables

Lab: Extracting Modified Data

Lab: Loading Incremental Changes

After completing this module, you will be able to:

  • Describe incremental ETL
  • Extract 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

Lab: De-duplicating 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

  • Master Data Services Concepts
  • Implementing a Master Data Services Model
  • Managing Master Data
  • Creating a Master Data Hub

Lab: Implementing Master Data Services

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 Custom Components in SSIS
  • Using Scripting in SSIS

Lab: Using Scripts and Custom Components

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

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
  • Introduction to Reporting
  • An Introduction to Data Analysis
  • Analyzing Data with Azure SQL Data Warehouse

Lab: Using Business Intelligence Tools

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
  • Analyze data with Azure SQL data warehouse

 

Exam 2

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 Data Warehouse

After completing this module, you will be able to:

  • Describe the concept of business intelligence
  • Describe the Microsoft business intelligence platform

Module 2: Creating Multidimensional Databases

This module describes the steps required to create a multidimensional database with analysis services.

Lessons

  • Introduction to multidimensional analysis
  • Creating data sources and data source views
  • Creating a cube
  • Overview of cube security

Lab: Creating a multidimensional database

After completing this module, you will be able to:

  • Use multidimensional analysis
  • Create data sources and data source views
  • Create a cube
  • Describe cube security

Module 3: Working with Cubes and Dimensions

This module describes how to implement dimensions in a cube.

Lessons

  • Configuring dimensions
  • Define attribute hierarchies
  • Sorting and grouping attributes

Lab: Working with Cubes and Dimensions

After completing this module, you will be able to:

  • Configure dimensions
  • Define attribute hierarchies.
  • Sort and group attributes

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

After completing this module, you will be able to:

  • Work with measures
  • Work with 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

After completing this module, you will be able to:

  • Describe the fundamentals of MDX
  • Add calculations to a cube
  • Query a cube using MDX

Module 6: Customizing Cube Functionality

This module describes how to customize a cube.

Lessons

  • Implementing key performance indicators
  • Implementing actions
  • Implementing perspectives
  • Implementing translations

Lab: Customizing a Cube

After completing this module, you will be able to:

  • Implement key performance indicators
  • Implement actions
  • Implement perspectives
  • Implement translations

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

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

Lessons

  • Introduction to tabular data models
  • Creating a tabular data model
  • Using an analysis services tabular model in an enterprise BI solution

Lab: Working with an Analysis services tabular data model

After completing this module, you will be able to:

  • Describe tabular data models
  • Create a tabular data model
  • Be able to use an analysis services tabular data 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

After completing this module, you will be able to:

  • Describe the fundamentals of DAX
  • Use DAX to create calculated columns and measures in a tabular data model

Module 9: Performing Predictive Analysis with Data Mining

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

Lessons

  • Overview of data mining
  • Using the data mining add-in for Excel
  • Creating a custom data mining solution
  • Validating a data mining model
  • Connecting to and consuming a data mining model

Lab: Perform Predictive Analysis with Data Mining

After completing this module, you will be able to:

  • Describe data mining
  • Use the data mining add-in for Excel
  • Create a custom data mining solution
  • Validate a data mining solution


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