INTRODUZIONE

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. 

OBIETTIVI

After completing this course, students will be able to:

• 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

PREREQUISITI

In addition to their professional experience, students who attend this training should already have the following technical knowledge:

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

STRUTTURA DEL CORSO

MODULE 1: Introduction to Data Warehousing

This module describes data warehouse concepts and architecture consideration.

Lessons

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
• Exploring data sources
• Exploring an ETL process
• Exploring a data warehouse

 Lab : Exploring a Data Warehouse Solution

MODULE 2: Planning Data Warehouse Infrastructure

This module describes the main hardware considerations for building a data warehouse.

Lessons

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

 Lab : Planning Data Warehouse Infrastructure

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

After completing this module, you will be able to:

• Implement a logical design for a Data Warehouse
• Designing dimension tables
• Designing fact tables
• Implement a Physical Design for a Data Warehouse

 Lab : Implementing a Data Warehouse Schema

MODULE 4: Columnstore Indexes

This module introduces Columnstore Indexes.

Lessons

After completing this module, you will be able to:

• Create a Columnstore index on the FactProductInventory table
• Create a Columnstore index on the FactInternetSales table
• Create a memory optimized Columnstore table
• Work with Columnstore Indexes

 Lab : Using Columnstore Indexes

MODULE 5: Implementing an Azure SQL Data Warehouse

This module describes Azure SQL Data Warehouses and how to implement them.

Lessons

After completing this module, you will be able to:

• Describe the advantages of Azure SQL Data Warehouse
• Implement an Azure SQL Data Warehouse
• Develop an Azure SQL Data Warehouse
• Migrate to an Azure SQ Data Warehouse
• Copy data with the Azure data factory
• Plan for migrating to Azure SQL Data Warehouse

Lab : Implementing an 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

After completing this module, you will be able to:

• Describe ETL with SSIS
• Explore Source Data
• Transferring data by using a data row task
• Using transformation components in a data row
• Implement a Data Flow

 Lab : Implementing Data Flow in an SSIS Package

MODULE 7: Implementing Control Flow in an SSIS Package

This module describes implementing control flow in an SSIS package.

Lessons

After completing this module, you will be able to:

• Describe Control Flow
• Create Dynamic Packages
• Use Containers
• Managing consistency
• Using tasks and precedence in a control flow
• Using variables and parameters
• Using containers
• Using transactions
• Using checkpoints

Lab : Implementing Control Flow in an SSIS Package
Lab : Using Transactions and Checkpoints

MODULE 8: Debugging and Troubleshooting SSIS Packages

This module describes how to debug and troubleshoot SSIS packages.

Lessons

After completing this module, you will be able to:

• Debug an SSIS package
• Log SSIS package events
• Implementing an event handler
• Handle errors in an SSIS package

 Lab : Debugging and Troubleshooting an SSIS Package

MODULE 9: Implementing a Data Extraction Solution

This module describes how to implement an SSIS solution that supports incremental DW loads and changing data.

Lessons

After completing this module, you will be able to:

• Describe incremental ETL
• Extract modified data
• Load modified data
• Describe temporal tables
• Using a datetime column to incrementally extract data
• Using change data capture
• Using the CDC control task
• Using change tracking
• 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

Lab : Extracting Modified Data
Lab : Loading a data warehouse

MODULE 10: Enforcing Data Quality

This module describes how to implement data cleansing by using Microsoft Data Quality services.

Lessons

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
• Create a DQS knowledge base
• Use a DQS project to cleanse data
• Use DQS in an SSIS package
• Creating a matching policy
• Using a DS project to match data

Lab : Cleansing Data
Lab : De-duplicating Data

MODULE 11: Using Master Data Services

This module describes how to implement master data services to enforce data integrity at source.

Lessons

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
• Use the master data services add-in for Excel
• Enforce business rules
• Load data into a model
• Consume master Data Service data

 Lab : Implementing Master Data Services

MODULE 12: Extending SQL Server Integration Services (SSIS)

This module describes how to extend SSIS with custom scripts and components.

Lessons

After completing this module, you will be able to:

•  Use custom components in SSIS
• Use scripting in SSIS

 Lab : Using scripts

MODULE 13: Deploying and Configuring SSIS Packages

This module describes how to deploy and configure SSIS packages.

Lessons

After completing this module, you will be able to:

• Describe an SSIS deployment
• Deploy an SSIS package
• Create an SSIS catalog
• Deploy an SSIS project
• Create environments for an SSIS solution
• Run an SSIS package in SQL server management studio
• Schedule SSIS packages with SQL server agent
• Plan SSIS package execution

 Lab : Deploying and Configuring SSIS Packages

MODULE 14: Consuming Data in a Data Warehouse

This module describes how to debug and troubleshoot SSIS packages.

Lessons

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

 Lab : Using a data warehouse