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    12/02/2019 12/07/2019 ICLVLT Register
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    08/03/2020 08/08/2020 ICLVLT Register
    10/26/2020 10/31/2020 ICLVLT Register

    Overview

    Exams Included:

    • 70-767: Implementing a SQL Data Warehouse
    • 70-768: Developing SQL Data Models

    About TechSherpas Boot Camps

    TechSherpas ’ 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
    • 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

    • 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)

    Description

    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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