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  • Microsoft/Implementing a Data Warehouse with Microsoft SQL Server Duration5 Days

Implementing A Data Warehouse With Microsoft SQL Server Duration5 Days

Course Overview

This course describes how to implement a data warehouse platform to support a BI solution. Delegates will learn how to create a data warehouse with Microsoft SQL Server 2014, implement ETL with SQL Server Integration Services, and validate and cleanse data with SQL Server Data Quality Services and SQL Server Master Data Services.

 

 

Target Audience:

This course is intended for database professionals who need to create and support a data warehousing solution. Primary responsibilities include:

Implementing a data warehouse.

Developing SSIS packages for data extraction, transformation, and loading.

Enforcing data integrity by using Master Data Services.

Cleansing data by using Data Quality Services.

Prerequisites

  • At least 2 years' experience of working with relational databases, including:
  • Designing a normalized database.
  • Creating tables and relationships.
  • Querying with Transact-SQL.
  • Some exposure to basic programming constructs (such as looping and branching).
  • An awareness of key business priorities such as revenue, profitability, and financial accounting is desirable.
  • At the end of this course you will be able to:
  • Describe data warehouse concepts and architecture considerations.
  • Select an appropriate hardware platform for a data warehouse.
  • Design and implement a data warehouse.
  • Implement Data Flow in an SSIS Package.
  • Implement Control Flow in an SSIS Package.
  • Debug and Troubleshoot SSIS packages.
  • Implement an ETL solution that supports incremental data extraction.
  • Implement an ETL solution that supports incremental data loading.
  • Implement data cleansing by using Microsoft Data Quality Services.
  • Implement Master Data Services to enforce data integrity.
  • Extend SSIS with custom scripts and components.
  • Deploy and Configure SSIS packages.
  • Describe how BI solutions can consume data from the data warehouse.

Outline

Module 1: Introduction to Data Warehousing

This module provides an introduction to the key components of a data warehousing solution and the high-level considerations you must take into account when you embark on a data warehousing project.

Lessons

Overview of Data Warehousing

Considerations for a Data Warehouse Solution

Lab : Exploring a Data Warehousing Solution

Exploring Data Sources

Exploring and ETL Process

Exploring a Data Warehouse

Module 2: Planning Data Warehouse Infrastructure

This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers.

Lessons

Considerations for Data Warehouse Infrastructure

Planning Data Warehouse Hardware

Lab : Planning Data Warehouse Infrastructure

Planning Data Warehouse Hardware

Module 3: Designing and Implementing a Data Warehouse

This module describes the key considerations for the logical design of a data warehouse, and then discusses best practices for its physical implementation.

Lessons

Data Warehouse Design Overview

Designing Dimension Tables

Designing Fact Tables

Physical Design for a Data Warehouse

Lab : Implementing a Data Warehouse

Implement a Star Schema

Implement a Snowflake Schema

Implement a Time Dimension

Module 4: Creating an ETL Solution with SSIS

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

Lessons

Introduction to ETL with SSIS

Exploring Data Sources

Implementing Data Flow

Lab : Implementing Data Flow in an SSIS Package

Exploring Data Sources

Transferring Data by Using a Data Flow Task

Using Transformations in a Data Flow

Module 5: Implementing Control Flow in an SSIS Package

This module describes how to implement ETL solutions that combine multiple tasks and workflow logic.

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

Module 6: Debugging and Troubleshooting SSIS Packages

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

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 a Data Flow

Module 7: Implementing a Data Extraction Solution

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

Lessons

Planning Data Extraction

Extracting Modified Data

Lab : Extracting Modified Data

Using a Datetime Column

Using Change Data Capture

Using the CDC Control Task

Using Change Tracking

Module 8: Loading Data into a Data Warehouse

This module describes the techniques you can use to implement data warehouse load process.

Lessons

Planning Data Loads

Using SSIS for Incremental Loads

Using Transact-SQL Loading Techniques

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

Module 9: Enforcing Data Quality

This module introduces Microsoft SQL Server Data Quality Services (DQS), and describes how you can use it to cleanse and DE duplicate data.

Lessons

Introduction to Data Quality

Using Data Quality Services to Cleanse Data

Using Data Quality Services to Cleanse Data

Lab : Cleansing Data

Creating a DQS Knowledge Base

Using a DQS Project to Cleanse Data

Using DQS in an SSIS Package

Module 10: Master Data Services

Master Data Services provides a way for organizations to standardize data and improve the quality, consistency, and reliability of the data that guides key business decisions. This module introduces Master Data Services and explains the benefits of using it.

Lessons

Introduction to Master Data Services

Implementing a Master Data Services Model

Managing Master Data

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

Module 11: Extending SQL Server Integration Services

This module describes the techniques you can use to extend 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.

Lessons

Using Scripts in SSIS

Using Custom Components in SSIS

Lab : Using Custom Scripts

Using a Script Task

Module 12: Deploying and Configuring SSIS Packages

In this module, delegates will learn how to deploy packages and their dependencies to a server, and how to manage and monitor the execution of deployed 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

Running an SSIS Package in SQL Server Management Studio

Scheduling SSIS Packages with SQL Server Agent

Module 13: Consuming Data in a Data Warehouse

This module introduces business intelligence (BI) solutions and describes how you can use a data warehouse as the basis for enterprise and self-service BI.

Lessons

Introduction to Business Intelligence

Enterprise Business Intelligence

Self-Service BI and Big Data

Lab : Using a Data Warehouse

Exploring an Enterprise BI Solution

Exploring a Self-Service BI Solution