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Implementing a Data Warehouse with Microsoft SQL Server 2012

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Tipologia Corso
Luogo Milano
Inizio Scegli data
  • Corso
  • Milano
  • Inizio:
    Scegli data
Descrizione

Overview of Data Warehousing Considerations for a Data Warehouse Solution Exploring data sources Exploring an ETL solution Exploring a data warehouse After completing this module, students will be able to:

Strutture (1)
Dove e quando
Inizio Luogo
Scegli data
Milano
Via Valtellina, 63, 20124, Milano, Italia
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Inizio Scegli data
Luogo
Milano
Via Valtellina, 63, 20124, Milano, Italia
Visualizza mappa

Cosa impari in questo corso?

Server
Microsoft SQL Server
Reporting
Windows
SQL
e-Business
Data Warehouse

Programma

Describe the key elements of a data warehousing solution.


Describe the key considerations for a data warehousing project.


This module describes the characteristics of typical data warehouse workloads, and explains how you can use reference architectures and data warehouse appliances to ensure you build the system that is right for your organization.


Considerations for Building a Data Warehouse


Data Warehouse Reference Architectures and Appliances


After completing this module, students 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.


In this module, you will learn how to implement the logical and physical architecture of a data warehouse based on industry-proven design principles.


Logical Design for a Data Warehouse


Physical Design for a Data Warehouse


Implementing a Star Schema


Implementing a Snowflake Schema


Implementing a Time Dimension Table


After completing this module, students will be able to:


Implement a logical design for a data warehouse.


Implement a physical design for a data warehouse.


This module discusses considerations for implementing an ETL process, and then focuses on SQL Server Integration Services (SSIS) as a platform for building ETL solutions.


Introduction to ETL with SSIS


Exploring Source Data


Implementing Data Flow


Exploring Source Data


Transferring Data by Using a Data Flow Task


Using Transformations in a Data Flow


After completing this module, students will be able to:


Describe the key features of SSIS.


Explore source data for an ETL solution.


Implement a data flow using SSIS.


Control flow in SQL Server Integration Services packages enables you to implement complex ETL solutions that combine multiple tasks and workflow logic. This module covers how to implement control flow, and design robust ETL processes for a data warehousing solution that coordinate data flow operations with other automated tasks.


Introduction to Control Flow


Creating Dynamic Packages


Using Containers


Managing Consistency


Using Tasks and Precedence in a Control Flow


Using Variables and Parameters


Using Containers


Using Transactions


Using Checkpoints


After completing this module, students will be able to:


Implement control flow with tasks and precedence constraints.


Create dynamic packages that include variables and parameters.


Use containers in a package control flow.


Enforce consistency with transactions and checkpoints.


This module describes how you can debug SQL Server Integration Services (SSIS) packages to find the cause of errors that occur during execution. Then module then covers the logging functionality built into SSIS you can use to log events for troubleshooting purposes. Finally, the module describes common approaches for handling errors in control flow and data flow.


Debugging an SSIS Package


Logging SSIS Package Events


Handling Errors in an SSIS Package


Debugging an SSIS Package


Logging SSIS Package Execution


Implementing an Event Handler


Handling Errors in a Data Flow


After completing this module, students will be able to:


Debug an SSIS package.


Implement logging for an SSIS package.


Handle errors in an SSIS package.


This module describes the techniques you can use to implement an incremental data warehouse refresh process.


Introduction to Incremental ETL


Extracting Modified Data


Loading Modified Data


Using a DateTime Column to Incrementally Extract Data


Using a Change Data Capture


Using Change Tracking


Using a Lookup Transformation to Insert Dimension Data


Using a Lookup Transformation to Insert or Update Dimension Data


Implementing a Slowly Changing Dimension


Using a MERGE Statement to Load Fact Data


After completing this module, students will be able to:


Describe the considerations for implementing an incremental extract, transform, and load (ETL) solution.


Use multiple techniques to extract new and modified data from source systems.


Use multiple techniques to insert new and modified data into a data warehouse.


In this module, you will learn about how you can use cloud computing in your data warehouse infrastructure and learn about the tools and services available from the Microsoft Azure Marketplace.


Overview of Cloud Data Sources


SQL Server Database


The Windows Azure Marketplace


Creating a SQL Azure Database


Extracting Data from a SQL Azure Database


Obtaining Data from the Windows Azure Marketplace


After completing this module, students will be able to:


Describe cloud data scenarios.


Describe SQL Azure.


Describe the Windows Azure Marketplace.


Ensuring the high quality of data is essential if the results of data analysis are to be trusted. This module explains how to use the SQL Server 2012 Data Quality Services (DQS) to provide a computer assisted process for cleansing data values and identifying and removing duplicate data entities.


Introduction to Data Quality


Using Data Quality Services to Cleanse Data


Using Data Quality Services to Match Data


Creating a DQS Knowledge Base


Using a DQS Project to Cleanse Data


Using DQS in an SSIS Package


Creating a Matching Policy


Using a DQS Project to Match Data


After completing this module, students will be able to:


Describe how Data Quality Services can help you manage data quality.


Use Data Quality Services to cleanse your data.


Use Data Quality Services to match data.


This module introduces Master Data Services and explains the benefits of using it in a data warehousing context. The module also describes the key configuration options for Master Data Services, and explains how to import and export data. Finally, the module explains how to apply rules that help to preserve data integrity, and introduces the new Master Data Services Add-in for Excel.


Introduction to Master Data Services


Implementing a Master Data Services Model


Using the Master Data Services Add-in for Excel


Creating a Basic Model


Editing a Model by Using the Master Data Services Add-in for Excel


Loading Data into a Model


Enforcing Business Rules


Consuming Master Data Services Data


After completing this module, students will be able to:


Describe key Master Data Services concepts.


Implement a Master Data Services model.


Use the Master Data Services Add-in for Excel to view and modify a model.


This module describes the techniques you can use to extend SQL Server Integration Services (SSIS). The module is not designed to be a comprehensive guide to developing custom SSIS solutions, but to provide an awareness of the fundamental steps required to use custom components and scripts in an ETL process that is based on SSIS.


Using Custom Components in SSIS


Using Scripts in SSIS


Using a Custom Component


Using a Script Task


After completing this module, students will be able to:


Describe how custom components can be used to extend SSIS.


Describe how you can include custom scripts in an SSIS package.


SQL Server Integration Services provides tools that make it easy to deploy packages to another computer. The deployment tools also manage any dependencies, such as configurations and files that the package needs. In this module, you will learn how to use these tools to install packages and their dependencies on a target computer.


Overview of SSIS Deployment


Deploying SSIS Projects


Planning SSIS Package Execution


Create a SSIS Catalog


Deploy an SSIS Project


Create 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, students will be able to:


Describe SSIS deployment.


Explain how to deploy SSIS projects using the project deployment model.


Plan SSIS package execution.


This module introduces Business Intelligence (BI), describes the components of SQL Server that you can use to create a BI solution, and the client tools that users can use to create reports and analyze data.


Introduction to Business Intelligence


Introduction to Reporting


Introduction to Data Analysis


Exploring a Reporting Services Report


Exploring a PowerPivot Workbook


Exploring a Power View Report


After completing this module, students will be able to:


Describe BI and common BI scenarios.


Explain the key features of SQL Server Reporting Services.


Explain the key features of SQL Server Analysis Services.



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