SQL Server 2016/2017: Implémenter un Data Warehouse

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.

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

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.

Module 1: Introduction to Data Warehousing

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

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 data warehouse infrastructure.
• Planning data warehouse hardware.
• Lab: Planning Data Warehouse Infrastructure
• Planning data warehouse hardware

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
• 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

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

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
• 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

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
• Exploring source data
• Transferring data by using a data row task
• Using transformation components in a data row

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
• 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

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
• Debugging an SSIS package
• Logging SSIS package execution
• Implementing an event handler
• Handling errors in data flow

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 a Data Extraction Solution

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

After completing this module, you will be able to:

• Describe incremental ETL
• Extract modified data
• Load 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, 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
• 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

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 scripting in SSIS
• Using custom components in SSIS
• Lab: Using scripts
• Using a script task

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
• 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

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
• 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

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

Attestation de présence remis à l'issu du cours à chaque participant - Préparation de la certification MCSA (Microsoft Certified Solutions Associate)