Implementing a Data Warehouse with Microsoft SQL Server - M20463

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.

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 deduplicate 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, students 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
  • £ 1,935

    Guide Price Per Delegate

  • Dates & Locations Available

  • Birmingham
    27/11/2017
    Glasgow
    27/11/2017
    Glasgow
    27/11/2017
    Glasgow
    27/11/2017
    London
    27/11/2017
    London
    27/11/2017
    London
    27/11/2017
    Birmingham
    04/12/2017
    Leeds
    08/01/2018
    Leeds
    08/01/2018
    Leeds
    08/01/2018
    London
    15/01/2018
    Glasgow
    05/02/2018
    Glasgow
    05/02/2018
    Glasgow
    05/02/2018
    London
    12/02/2018
    London
    12/02/2018
    London
    12/02/2018
    Leeds
    09/04/2018
    Leeds
    09/04/2018
    Leeds
    09/04/2018
    London
    09/04/2018
    London
    09/04/2018
    London
    09/04/2018

Course Information

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.
  • 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.

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