Viale Premuda 14, 20129 Milano - academy@digiacademy.it - 0250030724

Corsi Microsoft

Implementing a SQL Data Warehouse

Codice corso: 20767C
Durata corso: 5gg

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

P.IVA 06249920965
C.C.I.A.A. REA: MI - 1880014
Cap. Soc. € 12.000,00

Contatti

Viale Premuda n. 14 ,20129 Milano
Questo indirizzo email è protetto dagli spambots. È necessario abilitare JavaScript per vederlo.
Tel.: +39 02 50030 724
Fax.: +39 02 50030 725

© Copyright DI.GI. Academy
Privacy Policy | Cookie Policy