HARTMANN software group

MCSA: SQL Server 2016 BI Development Boot Camp Training in Atlanta

Enroll in or hire us to teach our MCSA: SQL Server 2016 BI Development Boot Camp class in Atlanta, Georgia by calling us @303.377.6176. Like all HSG classes, MCSA: SQL Server 2016 BI Development Boot Camp may be offered either onsite or via instructor led virtual training. Consider looking at our public training schedule to see if it is scheduled: Public Training Classes
Provided there are enough attendees, MCSA: SQL Server 2016 BI Development Boot Camp may be taught at one of our local training facilities.
We offer private customized training for groups of 3 or more attendees.
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Course Description

  
This boot camp is 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 - Lunch provided each day - Airfare, lodging and transportation packages available (Option 2) - Audience Profile At Course Completion - 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 Length: 6 Days
Course Tuition: $3200 (US)

Prerequisites
This boot camp intended for extract, transform, and load (ETL) and data warehouse developers who create business intelligence (BI) solutions. 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.

Course Outline

  

Exams included: 

70-767: Implementing a SQL Data Warehouse
70-768: Developing SQL Data Models
 
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 SSISModule 13: Deploying and Configuring SSIS Packages
 
Module 13: 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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