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20463 Implementing a Data Warehouse with Microsoft® SQL Server® 2014

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Date(s) - Enquire Now


All Day

Location
The Quill Consultancy

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Overview


About this Course
This course describes how to implement a data warehouse platform to support a BI solution. Students 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.
Note: This course is designed for customers who are interested in learning SQL Server 2012 or SQL Server 2014. It covers the new features in SQL Server 2014, but also the important capabilities across the SQL Server data platform.

Audience Profile
This course is intended for 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. 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.

At Course Completion
After completing this course, students 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.

Course Details


Course 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
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 project
Module 2: Data Warehouse Hardware Considerations
This module discusses considerations for selecting hardware and distributing SQL Server facilities across servers.
Lessons
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 key considerations for BI infrastructure.
Plan data warehouse infrastructure.
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
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:
Describe a process for designing a dimensional model for a data warehouse
Design dimension tables for a data warehouse
Design fact tables for a data warehouse
Design and implement effective physical data structures for a data warehouse

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
After completing this module, you will be able to:
Describe the key features of SSIS.
Explore source data for an ETL solution.
Implement a data flow by using SSIS
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
Lab : Using Transactions and Checkpoints
After completing this module, you 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
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
After completing this module, you will be able to:
Debug an SSIS package
Implement logging for an SSIS package
Handle errors in an SSIS package
Module 7: Implementing an Incremental ETL Process
This module describes the techniques you can use to implement an incremental data warehouse refresh process.
Lessons
Introduction to Incremental ETL
Extracting Modified Data
Loading Modified data
Lab : Extracting Modified Data
Lab : Loading Incremental Changes
After completing this module, you will be able to:
Plan data extraction
Extract modified data
Module 8: 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 Match data
Lab : Cleansing Data
Lab : De-duplicating data
After completing this module, you 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
Module 9: Using 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
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 key Master Data Services concepts
Implement a Master Data Services model
Use Master Data Services tools to manage master data
Use Master Data Services tools to create a master data hub
Module 10: 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 Components and Scripts
After completing this module, you will be able to:
Include custom scripts in an SSIS package
Describe how custom components can be used to extend SSIS
Module 11: 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
After completing this module, you will be able to:
Describe considerations for SSIS deployment.
Deploy SSIS projects.
Plan SSIS package execution.
Module 12: 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
Introduction to Reporting
An Introduction to Data Analysis
Lab : Using Business Intelligence Tools
After completing this module, you will be able to:
Describe BI and common BI scenarios
Describe how a data warehouse can be used in enterprise BI scenarios
Describe how a data warehouse can be used in self-service BI scenarios

Prerequisites


Student Prerequisites
This course requires that you meet the following 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.