Course 20767C: Implementing a SQL Data Warehouse Training

This five-day instructor-led course provides students with the knowledge and skills to provision a Microsoft SQL Server database. The course covers SQL Server provision both on-premise and in Azure, and covers installing from new and migrating from an existing install.The primary audience for this course are database professionals who need to fulfil a Business Intelligence Developer role. They will need to focus on hands-on work creating BI solutions including Data Warehouse implementation, ETL, and data cleansing.

  • ✔ Course Duration : 40 hrs
  • ✔ Training Options : Live Online / Self-Paced / Classroom
  • ✔ Certification Pass : Guaranteed
OVERVIEW LEARNING OBJECTIVES PRE-REQUISITES CURRICULUM FAQ CERTIFICATE

Overview

This five-day instructor-led course provides students with the knowledge and skills to provision a Microsoft SQL Server database. The course covers SQL Server provision both on-premise and in Azure, and covers installing from new and migrating from an existing install.The primary audience for this course are database professionals who need to fulfil a Business Intelligence Developer role. They will need to focus on hands-on work creating BI solutions including Data Warehouse implementation, ETL, and data cleansing.

What you will Learn

  • Describe the key elements of a data warehousing solution
  • Describe the main hardware considerations for building a data warehouse
  • Implement a logical design for a data warehouse
  • Implement a physical design for a data warehouse
  • Create columnstore indexes
  • Implementing an Azure SQL Data Warehouse
  • Describe the key features of SSIS
  • Implement a data flow by using SSIS
  • Implement control flow by using tasks and precedence constraints
  • Create dynamic packages that include variables and parameters
  • Debug SSIS packages
  • Describe the considerations for implement an ETL solution
  • Implement Data Quality Services
  • Implement a Master Data Services model
  • Describe how you can use custom components to extend SSIS
  • Deploy SSIS projects
  • Describe BI and common BI scenarios

PREREQUISITES

  • Basic knowledge of the Microsoft Windows operating system and its core functionality.
  • Working knowledge of relational databases.
  • Some experience with database design.

CURRICULUM

Learning Objectives:

  • 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
  • Learning Objectives:

  • 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
  • Learning Objectives:

  • 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
  • Learning Objectives:

  • 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 optimized Columnstore table
  • Learning Objectives:

  • 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
  • Learning Objectives:

  • 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
  • Learning Objectives:

  • 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
  • Learning Objectives:

  • 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
  • Learning Objectives:

  • 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
  • Learning Objectives:

  • 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
  • Learning Objectives:

  • 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
  • Learning Objectives:

  • 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
  • Learning Objectives:

  • 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 catalog
  • 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
  • Learning Objectives:

  • This module describes how to debug and troubleshoot SSIS packages.
  • Lessons:
  • Introduction to Business Intelligence
  • An Introduction to Data Analysis
  • Introduction to reporting
  • Analyzing 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
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