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NovaNext Training / Microsoft / SQL Server 2016 / Implementing a SQL Data Warehouse

Implementing a SQL Data Warehouse

Codice
MOC20767
Durata
5 Giorni
Prezzo
2.150,00 € (iva escl.)
Lingua
Italiano
Modalità
Virtual Classroom
Corso in aula
       

 

Schedulazione
Luogo Data Iscrizione
Virtual Classroom 30/03/2026
Virtual Classroom 13/07/2026
Virtual Classroom 30/11/2026

 

Prerequisiti

Almeno 2 anni di esperienza di lavoro con i database relazionali, ad esempio:Progettazione di un database normalizzato.Creazione di tabelle e relazioni.Interrogazione con Transact-SQL.

Esposizione ai costrutti di programmazione di base (ad esempio, looping e branching).

E' auspicabile la consapevolezza delle priorità di business chiave come entrate, redditività e contabilità finanziaria. 

 

Obiettivi

Descrivere gli elementi chiave di una soluzione di data warehousing

Descrivere le principali considerazioni di hardware per la costruzione di un data warehouse

Implementare un disegno logico per un data warehouse

Implementare una progettazione fisica di un data warehouse

Creare indici columnstore

Implementare un data warehouse Azure SQL

 

Contenuti

Module 1: Introduction to Data Warehousing

Overview of Data Warehousing

Considerations for a Data Warehouse Solution 

Lab : Exploring a Data Warehouse Solution

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

Considerations for Building a Data Warehouse

Data Warehouse Reference Architectures and Appliances 

Lab : Planning Data Warehouse Infrastructure

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

Logical Design for a Data Warehouse

Physical Design for a Data Warehouse 

Lab : Implementing a Data Warehouse Schema

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

Introduction to Columnstore Indexes

Creating Columnstore Indexes

Working with Columnstore Indexes 

Lab : Using Columnstore Indexes

After completing this module, you will be able to:

Create Columnstore indexes

Work with Columnstore Indexes 

Module 5: Implementing an Azure SQL Data Warehouse

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, 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

Introduction to ETL with SSIS

Exploring Source Data

Implementing Data Flow 

Lab : Implementing Data Flow in an SSIS Package

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

Introduction to Control Flow

Creating Dynamic Packages

Using Containers 

Lab : Implementing Control Flow in an SSIS Package 

Lab : Using Transactions and 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

Debugging an SSIS Package

Logging SSIS Package Events

Handling Errors in an SSIS Package 

Lab : Debugging and Troubleshooting an SSIS Package

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 an Incremental ETL Process

Introduction to Incremental ETL

Extracting Modified Data

Temporal Tables 

Lab : Extracting Modified Data

Lab : Loading Incremental Changes

After completing this module, you will be able to:

Describe incremental ETL

Extract modified data

Describe temporal tables 

Module 10: Enforcing Data Quality

Introduction to Data Quality

Using Data Quality Services to Cleanse Data

Using Data Quality Services to Match Data 

Lab : Cleansing Data 

Lab : De-duplicating 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

Master Data Services Concepts

Implementing a Master Data Services Model

Managing Master Data

Creating a Master Data Hub

Lab : Implementing Master Data Services

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)

Using Custom Components in SSIS

Using Scripting in SSIS

Lab : Using Scripts and Custom Components

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

Overview of SSIS Deployment

Deploying SSIS Projects

Planning SSIS Package Execution

Lab : Deploying and Configuring SSIS Packages

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

Introduction to Business Intelligence

Introduction to Reporting

An Introduction to Data Analysis

Analyzing Data with Azure SQL Data Warehouse

Lab : Using Business Intelligence Tools

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