Implementing a SQL Data Warehouse Training (20767)

SQL Data Warehouse Course Outline

• 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, students 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, students 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, students 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, students 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, students 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, students 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

  • 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, students 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, students 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
  • \
  • 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 Incremental Changes

  • 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, students 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

  • 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, students 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
  • Managing Master Data
  • 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, students 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

  After completing this module, students will be able to:

  • Using a script task
  • 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, students 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 Business Intelligence Tools

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

  After completing this module, students 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